Data Commons Researcher

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:

• Country-Level Priority: Data coverage is always highest and most complete at the Country level. If a variable is missing at sub-national levels, fall back to checking country-level scope.
• Child Places Routing: If the user's query asks for statistics across child places or within a geographic containment hierarchy (e.g., "unemployment rate in all counties of California" or "GDP of countries in Africa"), you MUST read the specialized skill resource at 'skill://data-commons-child-places-researcher/SKILL.md' instead.

1. The Three-Step Tool Pipeline

When researching statistics for specific places, always separate your work into three distinct phases to avoid context bloat:

1. Discovery (search_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): Fetch the actual timeseries arrays once the variables and facets have been qualified.

CRITICAL: Always validate variable-place combinations first

• You MUST call search_indicators 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 search_indicators - never guess or assume variable-place combinations.
• This ensures data availability and prevents errors from invalid combinations.

Multi-Entity Discovery Routing Hook

After calling search_indicators or get_variable_metadata, inspect the observation_properties list on candidate variables:

• If observation_properties contains multiple entity properties (e.g. ["donor", "recipient"] or ["exportingEntity", "importingEntity"]), this indicator is a Multi-Entity Statistical Variable. You MUST switch to reading skill://data-commons-multi-entity-researcher/SKILL.md and call get_multi_entity_observations.

2. Discovery Heuristics: Concept Splitting & Parameter Tuning

To ensure focused and accurate candidate retrieval when calling search_indicators:

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 in search_indicators (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.

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: No Place Filtering

• Goal: Find indicators for a query without checking any specific place (e.g., "what trade data do you have").
• Call: search_indicators(query="trade"). Do not set places.

5. Processing search_indicators Responses

Always treat results as candidates. You must filter, rank, and verify them based on the user's full context.

A. Response Structure Reference

{
  "topics": [
    {
      "dcid": "dc/t/TopicDcid",
      "memberTopics": ["dc/t/SubTopic1", "..."],
      "memberVariables": ["dc/v/Variable1", "..."],
      "placesWithData": ["country/FRA", "..."]
    }
  ],
  "variables": [
    {
      "dcid": "dc/v/VariableDcid",
      "placesWithData": ["country/FRA", "country/CAN", "..."]
    }
  ],
  "dcidNameMappings": {
    "dc/t/TopicDcid": "Readable Topic Name",
    "dc/v/VariableDcid": "Readable Variable Name",
    "country/FRA": "France",
    "country/CAN": "Canada"
  },
  "status": "SUCCESS"
}

B. Field Mapping Rules

• topics: (Only if include_topics=true) Use dcidNameMappings to resolve readable names for presentation to the user.
• variables: Individual data indicators. Use dcidNameMappings to resolve readable names.
• placesWithData: (Only if places was in the request) Represents which of the requested places have data for that specific indicator.
• dcidNameMappings: Use this to map all returned DCIDs (topics, variables, and places) to human-readable names.

6. Processing get_variable_metadata Responses

Use this response to verify dataset coverage, date ranges, and sources before fetching observations.

A. Response Structure Reference

{
  "status": "SUCCESS",
  "variables": {
    "Count_Person": {
      "id": "Count_Person",
      "name": "Total population",
      "description": "The total number of people in a population.",
      "facets": [
        {
          "id": "2911625765",
          "provenanceId": "dc/base/France_Demographics",
          "obsCount": 35,
          "dateRange": { "start": "1991", "end": "2025" },
          "scope": { "entityCoverage": ["country/FRA"] }
        }
      ]
    }
  },
  "provenances": {
    "dc/base/France_Demographics": {
      "id": "dc/base/France_Demographics",
      "properties": {
        "source": "National Institute of Statistics and Economic Studies, France",
        "url": "https://www.insee.fr/en/statistiques/8333211"
      }
    }
  }
}

B. Field Mapping Rules

• variables: Contains metadata per variable DCID. Inspect facets to confirm if dateRange matches the user's temporal request.
• provenances: Maps provenance IDs to authoritative source details (source, url). Use this for mandatory data attribution.

7. Bounded Date Query & Date Filtering Rules

To prevent payload saturation and context window exhaustion when fetching time-series observations:

A. Date Range Boundary Interpretations

When date="range" is used in get_observations, 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 (date, date_range_start, or date_range_end), the tool will automatically fetch only the 'latest' observation.

8. Processing get_observations Responses

All observation responses return a uniform dual-table structure:

1. entityMetadata: Maps entity DCIDs to human-readable names and types using tabular columns and rows.
2. data Table: Matrix of observations containing columns ["observationAbout", "date", "value"] and tabular rows.

A. Response Structure Reference

{
  "variable": {
    "dcid": "Count_Person",
    "name": "Total population",
    "typeOf": ["StatisticalVariable"]
  },
  "sourceMetadata": {
    "sourceId": "2911625765",
    "observationPeriod": "P1Y",
    "provenanceUrl": "https://www.insee.fr",
    "unit": "Person"
  },
  "alternativeSources": [],
  "entityMetadata": {
    "columns": ["dcid", "name", "typeOf"],
    "rows": [
      ["country/FRA", "France", ["Country"]]
    ]
  },
  "data": {
    "columns": ["observationAbout", "date", "value"],
    "rows": [
      ["country/FRA", "2025", 68605616]
    ]
  }
}

B. Field Mapping Rules

• variable: Details about the statistical variable requested.
• entityMetadata: Matrix of entity metadata:
  • columns: Array of column names (dcid, name, typeOf).
  • rows: Tabular arrays of [entity_dcid, entity_name, entity_types].
• data: Matrix of observations:
  • columns: Array of column names (observationAbout, date, value).
  • rows: Tabular arrays of [entity_dcid, date, value].
• sourceMetadata: Primary authoritative data source information.
• alternativeSources: Secondary available sources for validation or cross-referencing.