Open Census MCP Server
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- AlicenseNot gradedqualityAmaintenanceEnables AI assistants to query official Census Bureau statistics by listing datasets, retrieving geographies and aggregate data, and resolving geographic names to FIPS codes through natural-language interactions.MIT
- FlicenseNot gradedqualityAmaintenanceEnables AI assistants to access official Census Bureau statistics by listing datasets, fetching aggregate data and geography, resolving FIPS codes, and retrieving population data through natural-language prompts.-
- AlicenseNot gradedqualityBmaintenanceConnects AI assistants with official U.S. Census Bureau statistics, enabling them to discover available datasets, resolve place names to FIPS geography codes, and retrieve aggregate demographic and economic data through natural language. It draws on both the live Census Data API and a locally seeded database for faster search.MIT
- FlicenseNot gradedqualityAmaintenanceEnables AI assistants to query official Census Bureau datasets and statistics, resolve geographies to FIPS codes, and retrieve aggregate population and demographic data.-
- AlicenseNot gradedqualityAmaintenanceConnects AI assistants with official Census Bureau statistics and data from the Census Data API and other official sources, enabling users to list datasets, fetch geography and aggregate data, resolve FIPS codes, and retrieve population statistics through natural language.MIT
- AlicenseNot gradedqualityAmaintenanceEnables AI assistants to query official Census Bureau statistics and metadata, including dataset listings, geography levels, aggregate data, and FIPS code resolution. It also provides prompts for retrieving population data for states, counties, cities, and other geographic areas.MIT
TDQS
Scored across 3 tools
Each tool has a clearly distinct purpose with no overlap: explore_variables for variable discovery, get_methodology_guidance for statistical methodology orientation, and get_census_data for data retrieval. The descriptions explicitly guide when to use each tool, eliminating any ambiguity.
All three tools follow a consistent verb_noun pattern with underscores: explore_variables, get_methodology_guidance, and get_census_data. The naming is uniform and predictable across the set.
With only 3 tools, the set feels thin for a Census data domain that typically involves complex workflows like filtering, aggregation, or visualization. While the tools cover core functions, the low count may limit agent capabilities for comprehensive analysis.
The tools cover key aspects: methodology guidance, variable discovery, and data retrieval, forming a logical workflow. However, there are minor gaps such as no tools for data transformation, geographic mapping, or advanced filtering, which agents might need to work around.