From fbac61e4ecc6efb896c0100735b153645c407f22 Mon Sep 17 00:00:00 2001 From: Keyur Shah Date: Tue, 23 Jun 2026 20:01:24 +0530 Subject: [PATCH 1/4] Overhaul mcp tools, add metadata tool and introduce skills --- .../datacommons_mcp/agent_api_service.py | 13 + .../datacommons_mcp/agent_api_tools.py | 98 +- .../datacommons-mcp/datacommons_mcp/app.py | 25 +- .../instructions/agent_api/server.md | 5 + .../skills/data-commons-researcher/SKILL.md | 141 +++ .../agent_api/tools/get_child_observations.md | 1 + .../agent_api/tools/get_observations.md | 1 + .../agent_api/tools/get_variable_metadata.md | 1 + .../tools/search_child_indicators.md | 1 + .../agent_api/tools/search_indicators.md | 1 + .../instructions/{ => local}/server.md | 0 .../{ => local}/tools/get_observations.md | 0 .../{ => local}/tools/search_indicators.md | 0 .../instructions/tools/__init__.py | 0 .../tools/agent_api/get_observations.md | 64 -- .../tools/agent_api/search_indicators.md | 186 ---- .../datacommons-mcp/datacommons_mcp/server.py | 59 +- .../evals/tool_call_evals/agent.py | 2 +- .../get_observations/place_params.test.json | 4 +- .../get_observations/source_params.test.json | 8 +- packages/datacommons-mcp/pyproject.toml | 2 +- .../datacommons-mcp/tests/test_agent_api.py | 50 +- packages/datacommons-mcp/tests/test_app.py | 8 +- packages/datacommons-mcp/tests/test_utils.py | 4 +- pyproject.toml | 1 + uv.lock | 934 ++++++------------ 26 files changed, 679 insertions(+), 930 deletions(-) create mode 100644 packages/datacommons-mcp/datacommons_mcp/instructions/agent_api/server.md create mode 100644 packages/datacommons-mcp/datacommons_mcp/instructions/agent_api/skills/data-commons-researcher/SKILL.md create mode 100644 packages/datacommons-mcp/datacommons_mcp/instructions/agent_api/tools/get_child_observations.md create mode 100644 packages/datacommons-mcp/datacommons_mcp/instructions/agent_api/tools/get_observations.md create mode 100644 packages/datacommons-mcp/datacommons_mcp/instructions/agent_api/tools/get_variable_metadata.md create mode 100644 packages/datacommons-mcp/datacommons_mcp/instructions/agent_api/tools/search_child_indicators.md create mode 100644 packages/datacommons-mcp/datacommons_mcp/instructions/agent_api/tools/search_indicators.md rename packages/datacommons-mcp/datacommons_mcp/instructions/{ => local}/server.md (100%) rename packages/datacommons-mcp/datacommons_mcp/instructions/{ => local}/tools/get_observations.md (100%) rename packages/datacommons-mcp/datacommons_mcp/instructions/{ => local}/tools/search_indicators.md (100%) delete mode 100644 packages/datacommons-mcp/datacommons_mcp/instructions/tools/__init__.py delete mode 100644 packages/datacommons-mcp/datacommons_mcp/instructions/tools/agent_api/get_observations.md delete mode 100644 packages/datacommons-mcp/datacommons_mcp/instructions/tools/agent_api/search_indicators.md diff --git a/packages/datacommons-mcp/datacommons_mcp/agent_api_service.py b/packages/datacommons-mcp/datacommons_mcp/agent_api_service.py index df4f898e..f13c4c24 100644 --- a/packages/datacommons-mcp/datacommons_mcp/agent_api_service.py +++ b/packages/datacommons-mcp/datacommons_mcp/agent_api_service.py @@ -71,3 +71,16 @@ async def search_indicators( "include_topics": include_topics, } return await client.post("agent/search_indicators", payload) + + +async def get_variable_metadata( + variable_dcids: list[str], + entity_dcids: list[str], +) -> dict[str, Any]: + """Retrieves rich structural metadata (definitions, coverage, and provenances) for variables.""" + client = _get_agent_api_client() + payload = { + "variable_dcids": variable_dcids, + "entity_dcids": entity_dcids, + } + return await client.post("agent/get_variable_metadata", payload) diff --git a/packages/datacommons-mcp/datacommons_mcp/agent_api_tools.py b/packages/datacommons-mcp/datacommons_mcp/agent_api_tools.py index feaaf5e9..9ac314cb 100644 --- a/packages/datacommons-mcp/datacommons_mcp/agent_api_tools.py +++ b/packages/datacommons-mcp/datacommons_mcp/agent_api_tools.py @@ -15,29 +15,86 @@ Tool implementations for the Agent API-based Data Commons MCP server. """ +from typing import Any + from datacommons_mcp.agent_api_service import ( get_observations as agent_api_get_observations, ) +from datacommons_mcp.agent_api_service import ( + get_variable_metadata as agent_api_get_variable_metadata, +) from datacommons_mcp.agent_api_service import ( search_indicators as agent_api_search_indicators, ) from datacommons_mcp.data_models.observations import ObservationDateType +# Instruction file constants +SEARCH_INDICATORS_INSTRUCTION_FILE = "tools/search_indicators.md" +SEARCH_CHILD_INDICATORS_INSTRUCTION_FILE = "tools/search_child_indicators.md" +GET_VARIABLE_METADATA_INSTRUCTION_FILE = "tools/get_variable_metadata.md" +GET_OBSERVATIONS_INSTRUCTION_FILE = "tools/get_observations.md" +GET_CHILD_OBSERVATIONS_INSTRUCTION_FILE = "tools/get_child_observations.md" + + +async def search_indicators( + query: str, + places: list[str] | None = None, + per_search_limit: int = 10, + *, + include_topics: bool = True, +) -> dict[str, Any]: + """Searches for statistical indicators matching a natural language query.""" + return await agent_api_search_indicators( + query=query, + places=places, + parent_place=None, + per_search_limit=per_search_limit, + include_topics=include_topics, + ) + + +async def search_child_indicators( + query: str, + parent_place: str, + sample_child_places: list[str], + per_search_limit: int = 10, + *, + include_topics: bool = True, +) -> dict[str, Any]: + """Searches for statistical indicators available at the child-place level.""" + return await agent_api_search_indicators( + query=query, + places=sample_child_places, + parent_place=parent_place, + per_search_limit=per_search_limit, + include_topics=include_topics, + ) + + +async def get_variable_metadata( + variable_dcids: list[str], + entity_dcids: list[str], +) -> dict[str, Any]: + """Retrieves definitions, coverage, and provenances for a list of variables.""" + return await agent_api_get_variable_metadata( + variable_dcids=variable_dcids, + entity_dcids=entity_dcids, + ) + async def get_observations( variable_dcid: str, place_dcid: str, - child_place_type: str | None = None, source_override: str | None = None, date: str = ObservationDateType.LATEST.value, date_range_start: str | None = None, date_range_end: str | None = None, -) -> dict: - """Fetches observations for a statistical variable from Data Commons.""" +) -> dict[str, Any]: + """Fetches time-series observations for a statistical variable at a specific place.""" return await agent_api_get_observations( variable_dcid=variable_dcid, place_dcid=place_dcid, - child_place_type=child_place_type, + child_place_type=None, source_override=source_override, date=date, date_range_start=date_range_start, @@ -45,19 +102,22 @@ async def get_observations( ) -async def search_indicators( - query: str, - places: list[str] | None = None, - parent_place: str | None = None, - per_search_limit: int = 10, - *, - include_topics: bool = True, -) -> dict: - """Searches for indicators (topics and variables) in Data Commons.""" - return await agent_api_search_indicators( - query=query, - places=places, - parent_place=parent_place, - per_search_limit=per_search_limit, - include_topics=include_topics, +async def get_child_observations( + variable_dcid: str, + parent_place_dcid: str, + child_place_type: str, + source_override: str | None = None, + date: str = ObservationDateType.LATEST.value, + date_range_start: str | None = None, + date_range_end: str | None = None, +) -> dict[str, Any]: + """Fetches time-series observations for a statistical variable across child places.""" + return await agent_api_get_observations( + variable_dcid=variable_dcid, + place_dcid=parent_place_dcid, + child_place_type=child_place_type, + source_override=source_override, + date=date, + date_range_start=date_range_start, + date_range_end=date_range_end, ) diff --git a/packages/datacommons-mcp/datacommons_mcp/app.py b/packages/datacommons-mcp/datacommons_mcp/app.py index 0b607b76..fdea9a79 100644 --- a/packages/datacommons-mcp/datacommons_mcp/app.py +++ b/packages/datacommons-mcp/datacommons_mcp/app.py @@ -36,6 +36,8 @@ MCP_SERVER_NAME = "DC MCP Server" DEFAULT_INSTRUCTIONS_PACKAGE = "datacommons_mcp.instructions" SERVER_INSTRUCTIONS_FILE = "server.md" +MODE_AGENT_API = "agent_api" +MODE_LOCAL = "local" class DCApp: @@ -55,6 +57,9 @@ def __init__(self) -> None: logger.error("Settings error: %s", e) raise + # Establish active mode directory + self.mode_dir = MODE_AGENT_API if self.settings.use_agent_api else MODE_LOCAL + # Create client only if agent APIs are NOT enabled (as fallback is not needed) self.client = None if not self.settings.use_agent_api: @@ -91,30 +96,34 @@ async def lifespan(_server: FastMCP) -> AsyncIterator[dict[str, Any]]: ) def _load_instructions(self, filename: str) -> str: - """ - Loads markdown content. + """Loads markdown content relative to the active mode subfolder. + Priority: - 1. DC_INSTRUCTIONS_DIR/{filename} (if set and exists) - 2. Package default: datacommons_mcp/instructions/{filename} + 1. DC_INSTRUCTIONS_DIR/{self.mode_dir}/{filename} (if set and exists) + 2. Package default: datacommons_mcp/instructions/{self.mode_dir}/{filename} """ + path_in_mode = f"{self.mode_dir}/{filename}" + # Check specific override if self.settings.instructions_dir: - content = read_external_content(self.settings.instructions_dir, filename) + content = read_external_content( + self.settings.instructions_dir, path_in_mode + ) if content is not None: logger.info( "Loaded custom instructions for %s from %s", - filename, + path_in_mode, self.settings.instructions_dir, ) return content logger.debug( "Custom instructions file %s not found in %s, falling back to default.", - filename, + path_in_mode, self.settings.instructions_dir, ) # Fallback to package resources - return read_package_content(DEFAULT_INSTRUCTIONS_PACKAGE, filename) + return read_package_content(DEFAULT_INSTRUCTIONS_PACKAGE, path_in_mode) def register_tool(self, func: Callable[..., Any], instruction_file: str) -> None: """Register a tool with instructions loaded from a file. diff --git a/packages/datacommons-mcp/datacommons_mcp/instructions/agent_api/server.md b/packages/datacommons-mcp/datacommons_mcp/instructions/agent_api/server.md new file mode 100644 index 00000000..cb374f47 --- /dev/null +++ b/packages/datacommons-mcp/datacommons_mcp/instructions/agent_api/server.md @@ -0,0 +1,5 @@ +Act as a Data Commons Research Assistant. This server provides direct access to a massive, unified knowledge graph of aggregated statistical data from authoritative regional and global sources like the UN, World Bank, and Census Bureau. Use it to transform natural language queries into precise statistical insights by identifying specific indicators and retrieving their observations. It contains historical and recent data points on topics like demographics, economics, health, and environment across various geographic levels. It does not contain information on topics like real-time news, subjective viewpoints, or private corporate data. + +CRITICAL INSTRUCTION: When the user asks you to perform statistical research, search for indicators, or fetch observations, you MUST first read the skill resource at 'skill://data-commons-researcher/SKILL.md' to load the official research guidelines, place resolution heuristics, and child-sampling playbooks before calling any tools. This playbook contains the required rules and steps to successfully fulfill the query. + +Crucially, every data point retrieved must be attributed to its original source provided in the tool output; never present statistics as "known facts" without citing the specific organization or dataset they originated from. Prioritize data integrity and transparency, ensuring that users understand both the metric and the provenance of the information provided. diff --git a/packages/datacommons-mcp/datacommons_mcp/instructions/agent_api/skills/data-commons-researcher/SKILL.md b/packages/datacommons-mcp/datacommons_mcp/instructions/agent_api/skills/data-commons-researcher/SKILL.md new file mode 100644 index 00000000..6f3d405c --- /dev/null +++ b/packages/datacommons-mcp/datacommons_mcp/instructions/agent_api/skills/data-commons-researcher/SKILL.md @@ -0,0 +1,141 @@ +--- +name: data-commons-researcher +description: Guidelines, heuristics, and workflows for concept splitting, place resolution, variable metadata assessment, child-level sampling, and retrieving statistical observations from Data Commons. +--- + +## Foundational Knowledge: Data Commons Graph Structure + +Data Commons organizes data into two main structural hierarchies. Understanding these is key to choosing your place names and variables: + +1. **Topics (Variable Hierarchy)**: A taxonomy of categories (e.g., `Health` -> `Clinical Data` -> `Medical Conditions`). Topics contain sub-topics and individual variables. +2. **Places (Geographic Hierarchy)**: A taxonomy of spatial containment (e.g., `World` -> `Continent` -> `Country` -> `State` -> `County`). + +### Data Availability & Efficiency Tips: + +* **Direct Containment Efficiency**: Querying the direct child places of a parent (e.g., all counties inside California) is highly optimized and returns faster than querying arbitrary cross-border place sets. + +--- + +## 1. The Three-Step Tool Pipeline + +When researching statistics, always separate your work into three distinct phases to avoid context bloat: + +1. **Discovery (`search_indicators` or `search_child_indicators`)**: Use this to find candidate variables matching the user's concept. +2. **Assessment (`get_variable_metadata`)**: Pass candidate variables and target locations to retrieve structural metadata, ensuring the dataset matches the required temporal range, granularity, and source trust. +3. **Retrieval (`get_observations` or `get_child_observations`)**: Fetch the actual timeseries arrays once the variables and facets have been qualified. + +### CRITICAL: Always validate variable-place combinations first +* You **MUST** call discovery tools first to verify that the variable exists for the specified place. +* You **MUST** call `get_variable_metadata` to verify dataset facets (source, dates, coverage) before retrieving heavy observation arrays. +* Only use DCIDs returned by the discovery tools - never guess or assume variable-place combinations. + +--- + +## 2. Discovery Heuristics: Concept Splitting & Parameter Tuning + +To ensure focused and accurate candidate retrieval when calling discovery tools: + +### A. Concept Extraction & Multi-Query Splitting + +* **Search Single Concepts**: Always search for one semantic concept at a time. +* **Split Compound Queries**: + * *Incorrect*: `query="health and unemployment rate"` (Causes search index confusion). + * *Correct*: Split into two separate, sequential tool calls: + 1. `search_indicators(query="health", ...)` + 2. `search_indicators(query="unemployment rate", ...)` + +### B. Parameter Configuration Guidelines + +* **Toggling Topics (`include_topics`)**: + * Set `include_topics=true` (Default) when the user's request is exploratory (e.g., *"What health data do you have?"*). Use the returned topics to map the category hierarchy. + * Set `include_topics=false` when targeting a specific dataset or observation (e.g., *"Find the diabetes rate for California"*). This reduces the return payload size. + * **Primary Rule**: If a user explicitly states what they want, follow their request. Otherwise, default to the guidelines above. + +* **Setting Result Limits (`per_search_limit`)**: + * Always stick to the default value of `10` to keep payloads small. + * Do **not** increase the limit unless the user explicitly requests more candidate indicators. + +--- + +## 3. Geographic Place Qualification & Fallback Recovery + +Data Commons requires qualified geographic names to avoid database name conflicts. + +### A. Core Qualification Rules + +* **Never use DCIDs in Search Parameters**: Only pass qualified, human-readable English place names to `places` or `parent_place` in discovery tools (e.g., use `"California"`, not `"geoId/06"`). +* **Always Qualify Naming Ambiguities**: Add parent geographic or administrative context: + * *New York*: Differentiate between `"New York City, USA"` and `"New York State, USA"`. + * *Washington*: Differentiate between `"Washington, DC, USA"` and `"Washington State, USA"`. + * *Madrid*: Differentiate between `"Madrid, Spain"` (city) and `"Community of Madrid, Spain"` (autonomous community). + * *London*: Differentiate between `"London, UK"` and `"London, Ontario, Canada"`. + * *Scotland*: Differentiate between `"Scotland, UK"` and `"Scotland County, USA"`. +* **Extracting names from other tools**: If you get place info from another tool, extract and use *only* the readable name, but always qualify it with geographic context. +* **Child Place Indicator Discovery Rule**: When searching for indicators related to child places within a parent (e.g., states within a country), you MUST use `search_child_indicators`, passing the parent place name in `parent_place` and a diverse sample of 5-6 of its child places in the `sample_child_places` list. + +### B. Vague & Unqualified Query Fallbacks + +* If a user asks a general question about available data without specifying a place (e.g., *"What data do you have?"*), proactively run a global topic lookup: + * Call: `search_indicators(query="", places=["World"], include_topics=true)`. + * Present the high-level World topics, then ask the user which specific place or territory they are interested in. + * *Example response pattern*: "Here is a general overview of the data topics available for the World. You can also ask for this information for a specific place, like 'Africa', 'India', 'California, USA', or 'Paris, France'." + +### C. Geographic Resolution Recovery (Troubleshooting) + +* If the search tool resolves the wrong place (e.g., the user asked about Scotland but the results attach to "Scotland County, NC"): + * Re-run `search_indicators` with explicit parent parameters (e.g., set `places=["Scotland, UK"]`). + +--- + +## 4. Playbook Recipes & Call Examples + +### Recipe 1: Data for a Specific Place +* **Goal**: Find and retrieve an indicator *about* a single place (e.g., "population of France"). +* **Step 1 (Discovery)**: `search_indicators(query="population", places=["France"])` +* **Step 2 (Assessment)**: `get_variable_metadata(variable_dcids=["Count_Person"], entity_dcids=["country/FRA"])` +* **Step 3 (Retrieval)**: `get_observations(variable_dcid="Count_Person", place_dcid="country/FRA")` + +### Recipe 2: Sampling Child Places & Containment Data +* **Goal**: Check and retrieve data across child places of a parent (e.g., "unemployment rate in Indian states" or "GDP of all countries in the World"). +* **Step 1 (Discovery & Sampling)**: Call `search_child_indicators` using a diverse sample of child places to verify variable availability: + * *Example (States in India)*: `search_child_indicators(query="unemployment", parent_place="India", sample_child_places=["Uttar Pradesh, India", "Maharashtra, India", "Tripura, India", "Bihar, India", "Kerala, India"])` + * *Example (Countries in the World)*: `search_child_indicators(query="GDP", parent_place="World", sample_child_places=["USA", "China", "Germany", "Nigeria", "Brazil"])` +* **Proxy Logic rules**: + 1. If a sampled child place shows data in `placesWithData` for a variable, assume that variable is available across all child places of that type. + 2. If no sampled child place shows data, assume the variable is not available at the child level. + 3. **Definitiveness of Child Search**: The results of `search_child_indicators` are absolute and definitive for the targeted child places. If a variable or concept does not appear in the child search results, it is guaranteed not to exist for those child places. Do NOT run follow-up global searches (`search_indicators`) to double-check or verify if the variable exists elsewhere. + 4. **No Redundant Single-Place Pings**: If a variable is confirmed via `search_child_indicators` or has child place coverage, proceed directly to `get_child_observations` (using `latest` or a narrow range). Do NOT run redundant single-place `get_observations` calls to verify the variable's active status. + 5. Determine the common child place type (e.g. `"State"` or `"Country"`) from the returned `dcidPlaceTypeMappings`. +* **Step 2 (Assessment)**: Verify facets and date ranges for the variable across the child level by passing the resolved DCIDs of the sampled child places: + * `get_variable_metadata(variable_dcids=["unemployment_rate_dcid"], entity_dcids=["resolved_child_dcid_1", "resolved_child_dcid_2", "..."])` +* **Step 3 (Retrieval)**: Query observations for **ALL** child places of the determined type: + * `get_child_observations(variable_dcid="unemployment_rate_dcid", parent_place_dcid="country/IND", child_place_type="State")` + +--- + +## 5. Child Place Type Determination Heuristics + +Before calling `get_child_observations`, inspect the `dcidPlaceTypeMappings` returned by `search_child_indicators` to determine the value for the `child_place_type` parameter: +1. **Common Type**: Find the place type common to ALL sampled child places. +2. **Specific Type Priority**: If multiple types are common to all child places, choose the most specific type (e.g., prefer `"County"` over `"AdministrativeArea2"`). +3. **Majority Fallback**: If no single type is common to all, use the type that maps to a clear majority (50%+ threshold) of the sample. +4. **Resolution Failure**: If there is no common type and no majority type, child-place mode is not supported. Fall back to making individual `get_observations` calls for each child place. + +--- + +## 6. Bounded Date Query & Date Filtering Rules + +To prevent payload saturation and context window exhaustion when fetching time-series observations: + +### A. Child Places Mode Constraint +* When calling `get_child_observations`, **never** set `date="all"`. +* **Safe Date Strategies**: + * Set `date="latest"` to retrieve only the most recent data point for each child place. + * Explicitly define a narrow window using `date_range_start` and `date_range_end` (e.g., `2020` to `2023`). + +### B. Date Range Boundary Interpretations +When `date="range"` is used, the date ranges are evaluated as follows: +* **Start Date Only**: If only `date_range_start` is specified, the response will contain all observations starting at and after that date (inclusive). +* **End Date Only**: If only `date_range_end` is specified, the response will contain all observations before and up to that date (inclusive). +* **Both Boundaries**: If both are specified, the response contains observations within the provided range (inclusive). +* **Default Fallback**: If you do not provide any date parameters, the tool will automatically fetch only the `'latest'` observation. diff --git a/packages/datacommons-mcp/datacommons_mcp/instructions/agent_api/tools/get_child_observations.md b/packages/datacommons-mcp/datacommons_mcp/instructions/agent_api/tools/get_child_observations.md new file mode 100644 index 00000000..e63243a6 --- /dev/null +++ b/packages/datacommons-mcp/datacommons_mcp/instructions/agent_api/tools/get_child_observations.md @@ -0,0 +1 @@ +Retrieve time-series numerical observations for a statistical variable across all child places of a specific type within a parent geographic entity. Returns an array of dated observation values for each child place and their source metadata. Requires specifying the child place type (e.g., 'County' or 'State') and a bounded date range or 'latest' filter to prevent payload saturation. diff --git a/packages/datacommons-mcp/datacommons_mcp/instructions/agent_api/tools/get_observations.md b/packages/datacommons-mcp/datacommons_mcp/instructions/agent_api/tools/get_observations.md new file mode 100644 index 00000000..6ca2a803 --- /dev/null +++ b/packages/datacommons-mcp/datacommons_mcp/instructions/agent_api/tools/get_observations.md @@ -0,0 +1 @@ +Retrieve time-series numerical observations for a specific statistical variable at a target place. Returns an array of dated observation values and their source metadata. Operates in single-place mode; for child-level containment data, use get_child_observations. diff --git a/packages/datacommons-mcp/datacommons_mcp/instructions/agent_api/tools/get_variable_metadata.md b/packages/datacommons-mcp/datacommons_mcp/instructions/agent_api/tools/get_variable_metadata.md new file mode 100644 index 00000000..8303d882 --- /dev/null +++ b/packages/datacommons-mcp/datacommons_mcp/instructions/agent_api/tools/get_variable_metadata.md @@ -0,0 +1 @@ +Retrieve detailed structural metadata, including definitions, temporal coverage, and provenances, for a list of statistical variables. Returns a dictionary mapping variables to their source facets and entity coverages. Use this to assess and qualify variables before fetching heavy observation data. diff --git a/packages/datacommons-mcp/datacommons_mcp/instructions/agent_api/tools/search_child_indicators.md b/packages/datacommons-mcp/datacommons_mcp/instructions/agent_api/tools/search_child_indicators.md new file mode 100644 index 00000000..d179c5eb --- /dev/null +++ b/packages/datacommons-mcp/datacommons_mcp/instructions/agent_api/tools/search_child_indicators.md @@ -0,0 +1 @@ +Search the Data Commons Knowledge Graph for statistical indicators available at the child-place level within a parent geographic entity. Returns candidate indicator DCIDs and child place data availability mappings. Requires providing a parent place and a diverse sample list of its child places to verify sub-national coverage. diff --git a/packages/datacommons-mcp/datacommons_mcp/instructions/agent_api/tools/search_indicators.md b/packages/datacommons-mcp/datacommons_mcp/instructions/agent_api/tools/search_indicators.md new file mode 100644 index 00000000..f6d720e5 --- /dev/null +++ b/packages/datacommons-mcp/datacommons_mcp/instructions/agent_api/tools/search_indicators.md @@ -0,0 +1 @@ +Search the Data Commons Knowledge Graph for topics and statistical variables (indicators) matching a natural language query. Returns candidate indicator DCIDs, names, and data availability mappings. Can be optionally scoped to a list of target places to verify data presence. diff --git a/packages/datacommons-mcp/datacommons_mcp/instructions/server.md b/packages/datacommons-mcp/datacommons_mcp/instructions/local/server.md similarity index 100% rename from packages/datacommons-mcp/datacommons_mcp/instructions/server.md rename to packages/datacommons-mcp/datacommons_mcp/instructions/local/server.md diff --git a/packages/datacommons-mcp/datacommons_mcp/instructions/tools/get_observations.md b/packages/datacommons-mcp/datacommons_mcp/instructions/local/tools/get_observations.md similarity index 100% rename from packages/datacommons-mcp/datacommons_mcp/instructions/tools/get_observations.md rename to packages/datacommons-mcp/datacommons_mcp/instructions/local/tools/get_observations.md diff --git a/packages/datacommons-mcp/datacommons_mcp/instructions/tools/search_indicators.md b/packages/datacommons-mcp/datacommons_mcp/instructions/local/tools/search_indicators.md similarity index 100% rename from packages/datacommons-mcp/datacommons_mcp/instructions/tools/search_indicators.md rename to packages/datacommons-mcp/datacommons_mcp/instructions/local/tools/search_indicators.md diff --git a/packages/datacommons-mcp/datacommons_mcp/instructions/tools/__init__.py b/packages/datacommons-mcp/datacommons_mcp/instructions/tools/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/packages/datacommons-mcp/datacommons_mcp/instructions/tools/agent_api/get_observations.md b/packages/datacommons-mcp/datacommons_mcp/instructions/tools/agent_api/get_observations.md deleted file mode 100644 index 8e18c389..00000000 --- a/packages/datacommons-mcp/datacommons_mcp/instructions/tools/agent_api/get_observations.md +++ /dev/null @@ -1,64 +0,0 @@ -Fetches observations for a statistical variable from Data Commons. - -**CRITICAL: Always validate variable-place combinations first** -- You **MUST** call `search_indicators` first to verify that the variable exists for the specified place -- Only use DCIDs returned by `search_indicators` - never guess or assume variable-place combinations -- This ensures data availability and prevents errors from invalid combinations - -This tool can operate in two primary modes: -1. **Single Place Mode**: Get data for one specific place (e.g., "Population of California"). -2. **Child Places Mode**: Get data for all child places of a certain type within a parent place (e.g., "Population of all counties in California"). - -### Core Logic & Rules - -* **Variable Selection**: You **must** provide the `variable_dcid`. - * Variable DCIDs are unique identifiers for statistical variables in Data Commons and are returned by prior calls to the - `search_indicators` tool. - -* **Place Selection**: You **must** provide the `place_dcid`. - -* **Mode Selection**: - * To get data for the specified place (e.g., California), **do not** provide `child_place_type`. - * To get data for all its children (e.g., all counties in California), you **must also** provide the `child_place_type` (e.g., "County"). - **CRITICAL:** Before calling `get_observations` with `child_place_type`, you **MUST** first call `search_indicators` with child sampling to determine the correct child place type. - **Child Type Determination Logic:** - 1. Use the `dcid_place_type_mappings` or inline `typeOf` fields from the `search_indicators` response to examine the types of sampled child places - 2. Use the type that is common to ALL sampled child places - 3. If more than one type is common to all child places, use the most specific type - 4. If there is no common type across all sampled child places, use the majority type (50%+ threshold) if there's a clear majority - 5. If there is no common type and no clear majority, this tool cannot be called with child-place mode - fall back to single-place mode `get_observations` calls for each place - **Note:** If you used child sampling in `search_indicators` to validate variable existence, you should still get data for ALL children of that type, not just the sampled subset. - -* **Data Volume Constraint**: When using **Child Places Mode** (when `child_place_type` is set), you **must** be conservative with your date range to avoid requesting too much data. - * Avoid requesting `'all'` data via the `date` parameter. - * **Instead, you must either request the `'latest'` data or provide a specific, bounded date range.** - -* **Date Filtering**: The tool filters observations by date using the following priority: - 1. **`date`**: The `date` parameter is optional and can be one of the values 'all', 'latest', 'range', or a date string in the format 'YYYY', 'YYYY-MM', or 'YYYY-MM-DD'. - 2. **Date Range**: If `date` is set to 'range', you must specify a date range using `date_range_start` and/or `date_range_end`. - * If only `date_range_start` is specified, then the response will contain all observations starting at and after that date (inclusive). - * If only `date_range_end` is specified, then the response will contain all observations before and up to that date (inclusive). - * If both are specified, the response contains observations within the provided range (inclusive). - * Dates must be in `YYYY`, `YYYY-MM`, or `YYYY-MM-DD` format. - 3. **Default Behavior**: If you do not provide **any** date parameters (`date`, `date_range_start`, or `date_range_end`), the tool will automatically fetch only the `'latest'` observation. - -Args: - variable_dcid (str, required): The unique identifier (DCID) of the statistical variable. - place_dcid (str, required): The DCID of the place. - child_place_type (str, optional): The type of child places to get data for. **Use this to switch to Child Places Mode.** - source_override (str, optional): An optional source ID to force the use of a specific data source. - date (str, optional): An optional date filter. Accepts 'all', 'latest', 'range', or single date values of the format 'YYYY', 'YYYY-MM', or 'YYYY-MM-DD'. Defaults to 'latest' if no date parameters are provided. - date_range_start (str, optional): The start date for a range (inclusive). **Used only if `date` is set to'range'.** - date_range_end (str, optional): The end date for a range (inclusive). **Used only if `date` is set to'range'.** - -Returns: - The fetched observation data directly from Data Commons. - The response format contains: - - `variable`: Details about the statistical variable requested (dcid, name, typeOf). - - `resolvedParentPlace`: The resolved node information for the parent place, if one was provided. - - `childPlaceType`: The place type of the children observations if ContainedIn mode was used. - - `placeObservations`: A list of observations, one entry per place. Each entry contains: - - `place`: Details about the observed place (dcid, name, typeOf). - - `timeSeries`: A list of point objects containing `{"date": "...", "value": ...}` where value is a number. - - `sourceMetadata`: Information about the primary data source used (sourceId, importName, measurementMethod, unit, etc.). - - `alternativeSources`: Details about other available data sources. diff --git a/packages/datacommons-mcp/datacommons_mcp/instructions/tools/agent_api/search_indicators.md b/packages/datacommons-mcp/datacommons_mcp/instructions/tools/agent_api/search_indicators.md deleted file mode 100644 index 9d6530d0..00000000 --- a/packages/datacommons-mcp/datacommons_mcp/instructions/tools/agent_api/search_indicators.md +++ /dev/null @@ -1,186 +0,0 @@ -**Purpose:** -Search for topics and variables (collectively called "indicators") available in the Data Commons Knowledge Graph. - -**Core Concept: Results are Candidates** -This tool returns *candidate* indicators that match your query. You must always filter and rank these results based on the user's context to find the most relevant one. - -**Background: Data Commons Structure** -Data Commons organizes data in two main hierarchies: - -1. **Topics:** A hierarchy of categories (e.g., `Health` -> `Clinical Data` -> `Medical Conditions`). Topics contain sub-topics and member variables. - -2. **Places:** A hierarchy of geographic containment (e.g., `World` -> `Continent` -> `Country` -> `State`). - -**CRITICAL DATA PRINCIPLE:** -The *same* statistical concept (e.g., "Population") might use *different* indicator DCIDs for different place types (e.g., one DCID for `Country` and another for `State`). This tool is essential for discovering *which* specific indicators are available for the `places` you are querying. - -**Efficiency Tips:** - -* Data coverage is generally high at the `Country` level. - -* Fetching direct children of a place (e.g., states in a country) is efficient. - -### Parameters - -**1. `query` (str, required)** - - - The search query for indicators (topics or variables). - - **Examples:** `"health grants"`, `"carbon emissions"`, `"unemployment rate"` - - **CRITICAL RULES:** - * Search for one concept at a time to get focused results. - - Instead of: "health and unemployment rate" (single search) - - Use: "health" and "unemployment rate" as separate searches - -**2. `places` (list[str], optional)** - - - A list of English, human-readable place names to filter indicators by. - - If provided, the tool will only return indicators that have data for at least one of the specified places. - - When `parent_place` is used, this parameter should **only** contain a sample of child places. - - When `parent_place` is **not** used, this can contain any place. - -**3. `parent_place` (str, optional)** - - - An English, human-readable name for a parent place. - - Use this **only** when searching for indicators about a *type* of child place (e.g., "states in India"). - - When using this parameter, you **must** also provide a sample of child places in the `places` parameter. - -**Place Name Qualification (CRITICAL):** -The following rules apply to **both** the `places` and `parent_place` parameters. - - - **ALWAYS qualify place names** with geographic context to avoid ambiguity (e.g., `"California, USA"`, `"Paris, France"`, `"Springfield, IL"`). - - - **ALWAYS specify administrative level** when ambiguous: - - For the city: `"Madrid, Spain"` - - For the autonomous community: `"Community of Madrid, Spain"` - - Similarly, differentiate between `"New York City, USA"` and `"New York State, USA"`. - - - **Common Ambiguous Cases:** - - **New York:** `"New York City, USA"` vs `"New York State, USA"` - - **Madrid:** `"Madrid, Spain"` (city) vs `"Community of Madrid, Spain"` - - **London:** `"London, UK"` (city) vs `"London, Ontario, Canada"` - - **Washington:** `"Washington, DC, USA"` vs `"Washington State, USA"` - - **Springfield:** `"Springfield, IL, USA"` vs `"Springfield, MO, USA"` (add state) - - - **NEVER** use DCIDs (e.g., `"geoId/06"`, `"country/CAN"`). - - If you get place info from another tool, extract and use *only* the readable name, but always qualify it with geographic context. - - When searching for indicators related to child places within a larger geographic entity (e.g., states within a country, or countries within a continent/the world), you MUST include the parent entity in the `parent_place` parameter and a diverse sample of 5-6 of its child places in the `places` list. - - This ensures the discovery of indicators that have data at the child place level. Refer to 'Recipe 2: Sampling Child Places' for detailed examples. - -**How to Use Place Parameters (Recipes):** - - - **Recipe 1: Data for a Specific Place** - - **Goal:** Find an indicator *about* a single place (e.g., "population of France"). - - **Call:** `query="population"`, `places=["France"]` - - - **Recipe 2: Sampling Child Places** - - **Goal:** Check data availability for a *type* of child place (e.g., "population of Indian states" or "highest GDP countries" or "top 5 US states with lowest unemployment rate"). - - **Action:** You must *proxy* this request by sampling a few children. - - **Example 1: Child places of a country** - - **Call:** - * `query="population"` - * `parent_place="India"` - * `places=["Uttar Pradesh, India", "Maharashtra, India", "Tripura, India", "Bihar, India", "Kerala, India"]` - - **Logic:** - 1. Include the parent place ("India"). The tool uses this for context and to return its DCID. - 2. Include 5-6 *diverse* child places (e.g., try to pick large/small, north/south/east/west, if known). - 3. The results for these 5-6 places are a *proxy* for all children. - 4. If a sampled child place shows data for an indicator, assume that data is available for all child places of that type for that indicator. - Conversely, if, after sampling, no child place shows data for a specific indicator, assume that data is not available for any of the child places for that indicator. - - - **Example 2: Child places of the World (Countries)** - - **Call:** - * `query="GDP"` - * `parent_place="World"` - * `places=["USA", "China", "Germany", "Nigeria", "Brazil"]` - - **Logic:** - 1. Include the parent place ("World"). - 2. Include 5-6 *diverse* child countries (e.g., from different continents, different economies). - 3. This sampling helps discover the correct indicator DCID used for the `Country` place type, which you can then use in other tools (like `get_observations` with the parent's DCID in the `place_dcid` parameter and `child_place_type='Country'`). - - - **Example 3: Administrative Level Sampling** - - **Goal:** Check data availability for different administrative levels (e.g., "population of US cities" vs "population of US states"). - - **Call:** - * **For Cities:** `query="population"`, `parent_place="USA"`, `places=["New York City, USA", "Los Angeles, USA", "Chicago, USA", "Houston, USA", "Phoenix, USA"]` - * **For States:** `query="population"`, `parent_place="USA"`, `places=["California, USA", "Texas, USA", "Florida, USA", "New York State, USA", "Pennsylvania, USA"]` - - **Logic:** Specify the exact administrative level you want to sample to avoid confusion between city and state data. - - - **Recipe 3: No Place Filtering** - - **Goal:** Find indicators for a query without checking any specific place (e.g., "what trade data do you have"). - - **Call:** `query="trade"`. Do not set `places` or `parent_place`. - - **Result:** The tool returns matching indicators, but `places_with_data` will be empty. - -**4. `per_search_limit` (int, optional, default=10, max=100)** - - Maximum results per search. - - **CRITICAL RULE:** Only set per_search_limit when explicitly requested by the user. - - Use the default value (10) unless the user specifies a different limit - - Don't assume the user wants more or fewer results - -**5. `include_topics` (bool, optional, default=True)** - - **Primary Rule:** If a user explicitly states what they want, follow their request. Otherwise, use these guidelines: - - **`include_topics = True` (Default): For Exploration & Discovery** - - **Purpose:** To explore the data hierarchy and find related variables. - - **Use when:** - - The user is exploring (e.g., "what basic health data do you have?"). - - You need to understand how data is organized to ask a better follow-up. - - **Returns:** Both topics (categories) and variables. - - **`include_topics = False`: For Specific Data** - - **Purpose:** To find a specific variable for fetching data. - - **Use when:** - - The user's goal is to get a specific number or dataset (e.g., "find unemployment rate for United States"). - - **Returns:** Variables only. - -### Special Query Scenarios - -**Scenario 1: Vague, Unqualified Queries ("what data do you have?")** - - **Action:** If a user asks a general question about available data, proactively call the tool for "World" to provide an initial overview. - - **Call:** `query=""`, `places=["World"]`, `include_topics=True` - - **Result:** This returns the top-level topics for the World. - - **Agent Follow-up:** After showing the World data, consider asking if the user would like to see data for a different, more specific place if it seems helpful for the conversation. - - **Example agent response:** "Here is a general overview of the data topics available for the World. You can also ask for this information for a specific place, like 'Africa', 'India', 'California, USA', or 'Paris, France'." - -**Scenario 2: Ambiguous Place Names** - - **Problem 1:** Geographic ambiguity - User asks for "Scotland", tool returns "Scotland County, USA". - - **Solution:** Re-run with qualified name: `places=["Scotland, UK"]` - - **Problem 2:** Administrative level ambiguity - User asks for "New York", tool returns state-level data when city-level was intended. - - **Solution:** Specify administrative level: `places=["New York City, USA"]` vs `places=["New York State, USA"]` - -### Response Structure - -Returns a dictionary containing candidate indicators directly from Data Commons. -```json -{ - "status": "SUCCESS", - "resolvedParentPlace": { - "dcid": "geoId/06", - "name": "California", - "typeOf": ["State"] - }, - "topics": [ - { - "dcid": "dc/topic/Health", - "name": "Health", - "typeOf": ["Topic"], - "memberTopics": ["dc/topic/ClinicalData"], - "memberVariables": ["Count_Person_Obese"], - "placesWithData": ["geoId/06"] - } - ], - "variables": [ - { - "dcid": "Count_Person_Obese", - "name": "Obese population", - "typeOf": ["StatisticalVariable"], - "placesWithData": ["geoId/06"] - } - ] -} -``` - -### How to Process the Response - - - `topics`: (Only if `include_topics=True`) Collections of variables and sub-topics. Contains dcid, name, and typeOf inline. - - `variables`: Individual data indicators. Contains dcid, name, and typeOf inline. - - `placesWithData`: A list of place DCIDs for which the indicator has data. - - `resolvedParentPlace`: (Only if `parent_place` was in the request) The resolved node information for the parent place. - -**Final Reminder:** Always treat results as *candidates*. You must filter and rank them based on the user's full context. diff --git a/packages/datacommons-mcp/datacommons_mcp/server.py b/packages/datacommons-mcp/datacommons_mcp/server.py index b4cb5735..a000cdf8 100644 --- a/packages/datacommons-mcp/datacommons_mcp/server.py +++ b/packages/datacommons-mcp/datacommons_mcp/server.py @@ -16,13 +16,16 @@ """ import logging +from pathlib import Path +from fastmcp import FastMCP +from fastmcp.server.providers.skills import SkillsDirectoryProvider from starlette.requests import Request from starlette.responses import JSONResponse import datacommons_mcp.agent_api_tools as agent_api_tools import datacommons_mcp.tools as tools -from datacommons_mcp.app import app +from datacommons_mcp.app import DCApp, app from datacommons_mcp.version import __version__ # Configure logging @@ -42,11 +45,57 @@ async def health_check(request: Request) -> JSONResponse: # noqa: ARG001 reques # Register tools if app.settings.use_agent_api: app.register_tool( - agent_api_tools.get_observations, "tools/agent_api/get_observations.md" + agent_api_tools.search_indicators, + agent_api_tools.SEARCH_INDICATORS_INSTRUCTION_FILE, ) app.register_tool( - agent_api_tools.search_indicators, "tools/agent_api/search_indicators.md" + agent_api_tools.search_child_indicators, + agent_api_tools.SEARCH_CHILD_INDICATORS_INSTRUCTION_FILE, + ) + app.register_tool( + agent_api_tools.get_variable_metadata, + agent_api_tools.GET_VARIABLE_METADATA_INSTRUCTION_FILE, + ) + app.register_tool( + agent_api_tools.get_observations, + agent_api_tools.GET_OBSERVATIONS_INSTRUCTION_FILE, + ) + app.register_tool( + agent_api_tools.get_child_observations, + agent_api_tools.GET_CHILD_OBSERVATIONS_INSTRUCTION_FILE, ) else: - app.register_tool(tools.get_observations, tools.GET_OBSERVATIONS_INSTRUCTION_FILE) - app.register_tool(tools.search_indicators, tools.SEARCH_INDICATORS_INSTRUCTION_FILE) + app.register_tool( + tools.get_observations, + tools.GET_OBSERVATIONS_INSTRUCTION_FILE, + ) + app.register_tool( + tools.search_indicators, + tools.SEARCH_INDICATORS_INSTRUCTION_FILE, + ) + + +def _register_skills(mcp_server: FastMCP, app_instance: DCApp) -> None: + """Configures and registers the native FastMCP SkillsDirectoryProvider.""" + skills_roots = [] + if app_instance.settings.instructions_dir: + custom_skills = ( + Path(app_instance.settings.instructions_dir) + / app_instance.mode_dir + / "skills" + ) + if custom_skills.exists(): + skills_roots.append(custom_skills) + + default_skills = ( + Path(__file__).parent / "instructions" / app_instance.mode_dir / "skills" + ) + if default_skills.exists(): + skills_roots.append(default_skills) + + if skills_roots: + mcp_server.add_provider(SkillsDirectoryProvider(roots=skills_roots)) + + +# Call provider registration on startup +_register_skills(mcp, app) diff --git a/packages/datacommons-mcp/evals/tool_call_evals/agent.py b/packages/datacommons-mcp/evals/tool_call_evals/agent.py index 894a563f..979fda0c 100644 --- a/packages/datacommons-mcp/evals/tool_call_evals/agent.py +++ b/packages/datacommons-mcp/evals/tool_call_evals/agent.py @@ -15,7 +15,7 @@ ) # Model for the agent -AGENT_MODEL = "gemini-2.5-flash" +AGENT_MODEL = os.environ.get("EVAL_AGENT_MODEL", "gemini-flash-latest") def create_agent(instruction: str, name: str = "tool_usage_agent") -> LlmAgent: diff --git a/packages/datacommons-mcp/evals/tool_call_evals/data/get_observations/place_params.test.json b/packages/datacommons-mcp/evals/tool_call_evals/data/get_observations/place_params.test.json index 791731da..e5605588 100644 --- a/packages/datacommons-mcp/evals/tool_call_evals/data/get_observations/place_params.test.json +++ b/packages/datacommons-mcp/evals/tool_call_evals/data/get_observations/place_params.test.json @@ -17,10 +17,10 @@ "query": "What about for states in India?", "expected_tool_use": [ { - "tool_name": "get_observations", + "tool_name": "get_child_observations", "tool_input": { "date": "latest", - "place_dcid": "country/IND", + "parent_place_dcid": "country/IND", "variable_dcid": "Count_Person", "child_place_type": "AdministrativeArea1" } diff --git a/packages/datacommons-mcp/evals/tool_call_evals/data/get_observations/source_params.test.json b/packages/datacommons-mcp/evals/tool_call_evals/data/get_observations/source_params.test.json index 7e0f8e4c..cfa96086 100644 --- a/packages/datacommons-mcp/evals/tool_call_evals/data/get_observations/source_params.test.json +++ b/packages/datacommons-mcp/evals/tool_call_evals/data/get_observations/source_params.test.json @@ -3,10 +3,10 @@ "query": "What is the population (dcid=Count_Person) of Indian states (child_place_type=AdministrativeArea1)?", "expected_tool_use": [ { - "tool_name": "get_observations", + "tool_name": "get_child_observations", "tool_input": { "date": "latest", - "place_dcid": "country/IND", + "parent_place_dcid": "country/IND", "variable_dcid": "Count_Person", "child_place_type": "AdministrativeArea1" } @@ -18,10 +18,10 @@ "query": "How do those numbers compare to the Indian Census", "expected_tool_use": [ { - "tool_name": "get_observations", + "tool_name": "get_child_observations", "tool_input": { "date": "latest", - "place_dcid": "country/IND", + "parent_place_dcid": "country/IND", "variable_dcid": "Count_Person", "child_place_type": "AdministrativeArea1", "source_override": "2123271870" diff --git a/packages/datacommons-mcp/pyproject.toml b/packages/datacommons-mcp/pyproject.toml index 046fdb5f..4a893a4d 100644 --- a/packages/datacommons-mcp/pyproject.toml +++ b/packages/datacommons-mcp/pyproject.toml @@ -7,7 +7,7 @@ requires-python = ">=3.11, <3.14" dependencies = [ "fastapi>=0.115.0", "uvicorn", - "fastmcp", + "fastmcp>=3.0.0", "requests", "datacommons-client>=2.1.6", "pydantic>=2.11.7", diff --git a/packages/datacommons-mcp/tests/test_agent_api.py b/packages/datacommons-mcp/tests/test_agent_api.py index 1850f88b..0b7c7338 100644 --- a/packages/datacommons-mcp/tests/test_agent_api.py +++ b/packages/datacommons-mcp/tests/test_agent_api.py @@ -20,7 +20,11 @@ import httpx import pytest from datacommons_mcp.agent_api_client import AgentAPIClient -from datacommons_mcp.agent_api_service import get_observations, search_indicators +from datacommons_mcp.agent_api_service import ( + get_observations, + get_variable_metadata, + search_indicators, +) from datacommons_mcp.agent_api_tools import ( get_observations as agent_api_tools_get_obs, ) @@ -201,3 +205,47 @@ def raise_status_error(): assert exc_info.value.body == '{"message": "Internal error"}' await client.close() + + +@pytest.mark.asyncio +async def test_agent_api_service_get_variable_metadata(): + """Verify get_variable_metadata builds correct payload and invokes client.""" + mock_client = AsyncMock() + mock_client.post.return_value = {"metadata": {}, "provenance": {}} + + with patch.object(app, "agent_api_client", mock_client): + result = await get_variable_metadata( + variable_dcids=["Count_Person"], + entity_dcids=["geoId/06"], + ) + assert result == {"metadata": {}, "provenance": {}} + mock_client.post.assert_called_once_with( + "agent/get_variable_metadata", + { + "variable_dcids": ["Count_Person"], + "entity_dcids": ["geoId/06"], + }, + ) + + +def test_skills_provider_registration(): + """Verify that SkillsDirectoryProvider is correctly registered when skills exist.""" + from datacommons_mcp.server import _register_skills + from fastmcp.server.providers.skills import SkillsDirectoryProvider + + mock_mcp = MagicMock() + mock_app = MagicMock() + mock_app.mode_dir = "agent_api" + mock_app.settings.instructions_dir = None # Force package default fallback + + _register_skills(mock_mcp, mock_app) + + # Verify that add_provider was called + mock_mcp.add_provider.assert_called_once() + provider = mock_mcp.add_provider.call_args[0][0] + assert isinstance(provider, SkillsDirectoryProvider) + + # Verify that the provider root points to agent_api/skills + assert len(provider._roots) == 1 + assert "agent_api" in str(provider._roots[0]) + assert "skills" in str(provider._roots[0]) diff --git a/packages/datacommons-mcp/tests/test_app.py b/packages/datacommons-mcp/tests/test_app.py index e38f1652..71827bfd 100644 --- a/packages/datacommons-mcp/tests/test_app.py +++ b/packages/datacommons-mcp/tests/test_app.py @@ -69,7 +69,7 @@ def test_app_initialization_override( """Test that DCApp loads instructions from DC_INSTRUCTIONS_DIR.""" # Create custom instructions custom_dir = tmp_path / "instructions" - create_test_file("instructions/server.md", "Custom Server Instructions") + create_test_file("instructions/local/server.md", "Custom Server Instructions") # Configure settings to use custom dir mock_settings.return_value.instructions_dir = str(custom_dir) @@ -88,7 +88,9 @@ def test_load_instruction_tool_override(mock_settings, tmp_path, create_test_fil """Test loading tool instructions with override.""" # Create custom instructions custom_dir = tmp_path / "instructions" - create_test_file("instructions/tools/test_tool.md", "Custom Tool Instructions") + create_test_file( + "instructions/local/tools/test_tool.md", "Custom Tool Instructions" + ) # Configure settings to use custom dir mock_settings.return_value.instructions_dir = str(custom_dir) @@ -121,7 +123,7 @@ def test_load_instruction_fallback(mock_settings, tmp_path): def test_register_tool(mock_settings, mock_fastmcp, tmp_path, create_test_file): """Test tool registration with instruction loading.""" # Create custom instructions - create_test_file("instructions/tools/sample.md", "Sample Tool Description") + create_test_file("instructions/local/tools/sample.md", "Sample Tool Description") mock_settings.return_value.instructions_dir = str(tmp_path / "instructions") from datacommons_mcp.app import DCApp diff --git a/packages/datacommons-mcp/tests/test_utils.py b/packages/datacommons-mcp/tests/test_utils.py index 7ad32785..716a9708 100644 --- a/packages/datacommons-mcp/tests/test_utils.py +++ 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+++++++++++++------ .../datacommons-mcp/tests/test_agent_api.py | 7 +++++- 2 files changed, 21 insertions(+), 8 deletions(-) diff --git a/packages/datacommons-mcp/datacommons_mcp/server.py b/packages/datacommons-mcp/datacommons_mcp/server.py index a000cdf8..280719be 100644 --- a/packages/datacommons-mcp/datacommons_mcp/server.py +++ b/packages/datacommons-mcp/datacommons_mcp/server.py @@ -79,13 +79,21 @@ def _register_skills(mcp_server: FastMCP, app_instance: DCApp) -> None: """Configures and registers the native FastMCP SkillsDirectoryProvider.""" skills_roots = [] if app_instance.settings.instructions_dir: - custom_skills = ( - Path(app_instance.settings.instructions_dir) - / app_instance.mode_dir - / "skills" - ) - if custom_skills.exists(): - skills_roots.append(custom_skills) + if app_instance.settings.instructions_dir.startswith("gs://"): + # TODO(keyurs): Support GCS-hosted custom skills by syncing the GCS + # instructions directory to a local temporary directory at startup (bootstrap cache). + logger.warning( + "GCS paths are not supported for loading custom skills: %s. Skipping.", + app_instance.settings.instructions_dir, + ) + else: + custom_skills = ( + Path(app_instance.settings.instructions_dir) + / app_instance.mode_dir + / "skills" + ) + if custom_skills.exists(): + skills_roots.append(custom_skills) default_skills = ( Path(__file__).parent / "instructions" / app_instance.mode_dir / "skills" diff --git a/packages/datacommons-mcp/tests/test_agent_api.py b/packages/datacommons-mcp/tests/test_agent_api.py index 0b7c7338..b6b3e6c3 100644 --- a/packages/datacommons-mcp/tests/test_agent_api.py +++ b/packages/datacommons-mcp/tests/test_agent_api.py @@ -31,7 +31,6 @@ from datacommons_mcp.agent_api_tools import ( search_indicators as agent_api_tools_search_ind, ) -from datacommons_mcp.app import app from datacommons_mcp.tools import ( get_observations as tools_get_obs, ) @@ -67,6 +66,8 @@ async def test_agent_api_client_post(): @pytest.mark.asyncio async def test_agent_api_service_get_observations(): """Verify get_observations builds correct payload and invokes agent_api_client.""" + from datacommons_mcp.app import app + mock_client = AsyncMock() mock_client.post.return_value = {"placeObservations": []} @@ -98,6 +99,8 @@ async def test_agent_api_service_get_observations(): @pytest.mark.asyncio async def test_agent_api_service_search_indicators(): """Verify search_indicators builds correct payload and invokes agent_api_client.""" + from datacommons_mcp.app import app + mock_client = AsyncMock() mock_client.post.return_value = {"variables": []} @@ -210,6 +213,8 @@ def raise_status_error(): @pytest.mark.asyncio async def test_agent_api_service_get_variable_metadata(): """Verify get_variable_metadata builds correct payload and invokes client.""" + from datacommons_mcp.app import app + mock_client = AsyncMock() mock_client.post.return_value = {"metadata": {}, "provenance": {}} From 6a195f60c9dddaf644c1c6a5e66ecf1224718e90 Mon Sep 17 00:00:00 2001 From: Keyur Shah Date: Wed, 24 Jun 2026 10:06:48 +0530 Subject: [PATCH 3/4] Pin FastMCP dependency to exact version 3.4.2 --- packages/datacommons-mcp/pyproject.toml | 2 +- pyproject.toml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/packages/datacommons-mcp/pyproject.toml b/packages/datacommons-mcp/pyproject.toml index 4a893a4d..444fcd23 100644 --- a/packages/datacommons-mcp/pyproject.toml +++ b/packages/datacommons-mcp/pyproject.toml @@ -7,7 +7,7 @@ requires-python = ">=3.11, <3.14" dependencies = [ "fastapi>=0.115.0", "uvicorn", - "fastmcp>=3.0.0", + "fastmcp==3.4.2", "requests", "datacommons-client>=2.1.6", "pydantic>=2.11.7", diff --git a/pyproject.toml b/pyproject.toml index 15265d8c..3aee7af8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -20,7 +20,7 @@ dependencies = [ "pytest>=8.4.1", "pytest-asyncio>=1.1.0", "pre-commit>=4.3.0", - "fastmcp>=2.13.1", + "fastmcp==3.4.2", ] [project.optional-dependencies] From 06aeb22093abbb3d116a998d3de92ee679f8be44 Mon Sep 17 00:00:00 2001 From: Keyur Shah Date: Wed, 24 Jun 2026 23:05:20 +0530 Subject: [PATCH 4/4] Revert changes to evaluation toolset and fixtures --- packages/datacommons-mcp/evals/tool_call_evals/agent.py | 2 +- .../data/get_observations/place_params.test.json | 4 ++-- .../data/get_observations/source_params.test.json | 8 ++++---- 3 files changed, 7 insertions(+), 7 deletions(-) diff --git a/packages/datacommons-mcp/evals/tool_call_evals/agent.py b/packages/datacommons-mcp/evals/tool_call_evals/agent.py index 979fda0c..894a563f 100644 --- a/packages/datacommons-mcp/evals/tool_call_evals/agent.py +++ b/packages/datacommons-mcp/evals/tool_call_evals/agent.py @@ -15,7 +15,7 @@ ) # Model for the agent -AGENT_MODEL = os.environ.get("EVAL_AGENT_MODEL", "gemini-flash-latest") +AGENT_MODEL = "gemini-2.5-flash" def create_agent(instruction: str, name: str = "tool_usage_agent") -> LlmAgent: diff --git a/packages/datacommons-mcp/evals/tool_call_evals/data/get_observations/place_params.test.json b/packages/datacommons-mcp/evals/tool_call_evals/data/get_observations/place_params.test.json index e5605588..791731da 100644 --- a/packages/datacommons-mcp/evals/tool_call_evals/data/get_observations/place_params.test.json +++ b/packages/datacommons-mcp/evals/tool_call_evals/data/get_observations/place_params.test.json @@ -17,10 +17,10 @@ "query": "What about for states in India?", "expected_tool_use": [ { - "tool_name": "get_child_observations", + "tool_name": "get_observations", "tool_input": { "date": "latest", - "parent_place_dcid": "country/IND", + "place_dcid": "country/IND", "variable_dcid": "Count_Person", "child_place_type": "AdministrativeArea1" } diff --git a/packages/datacommons-mcp/evals/tool_call_evals/data/get_observations/source_params.test.json b/packages/datacommons-mcp/evals/tool_call_evals/data/get_observations/source_params.test.json index cfa96086..7e0f8e4c 100644 --- a/packages/datacommons-mcp/evals/tool_call_evals/data/get_observations/source_params.test.json +++ b/packages/datacommons-mcp/evals/tool_call_evals/data/get_observations/source_params.test.json @@ -3,10 +3,10 @@ "query": "What is the population (dcid=Count_Person) of Indian states (child_place_type=AdministrativeArea1)?", "expected_tool_use": [ { - "tool_name": "get_child_observations", + "tool_name": "get_observations", "tool_input": { "date": "latest", - "parent_place_dcid": "country/IND", + "place_dcid": "country/IND", "variable_dcid": "Count_Person", "child_place_type": "AdministrativeArea1" } @@ -18,10 +18,10 @@ "query": "How do those numbers compare to the Indian Census", "expected_tool_use": [ { - "tool_name": "get_child_observations", + "tool_name": "get_observations", "tool_input": { "date": "latest", - "parent_place_dcid": "country/IND", + "place_dcid": "country/IND", "variable_dcid": "Count_Person", "child_place_type": "AdministrativeArea1", "source_override": "2123271870"