MCP Toolkit for Public Info from Data Commons: Server Integration Solution

Un Datacommons MCP

This project provides an MCP toolkit and sample agents for obtaining public information from Data Commons, including the implementation of the MCP server and a quick - start guide.

Research and data Education and learning tools #Data query #Public information #MCP tools #Sample agents .Python

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update time : 2025-12-29

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What is the Data Commons MCP Server?

The Data Commons MCP Server is a Model Context Protocol server that allows AI assistants and applications to access the vast public datasets on the Data Commons platform. Data Commons is an open - source knowledge graph supported by Google, integrating statistical data from multiple authoritative institutions such as the US Census Bureau, the World Bank, and the Environmental Protection Agency. Through this MCP server, you can query data in various fields such as population, economy, health, education, and environment without writing complex data processing code.

How to use the Data Commons MCP Server?

You can use this server through AI platforms that support the MCP protocol (such as Claude Desktop, Gemini CLI, etc.). After installation and configuration, the AI assistant can directly call the data query function of Data Commons. You can ask questions in natural language, such as "What is the population of California?" or "Compare the unemployment rates of New York and Los Angeles." The AI will obtain accurate data through the MCP server and analyze it for you.

Use cases

This tool is particularly suitable for researchers, students, journalists, data analysts, and anyone who needs to quickly access reliable public data. You can use it for academic research, market analysis, news reporting, policy - making support, or just to satisfy personal curiosity. For example, you can query the cost of living in a certain city, compare the carbon emissions of different countries, or analyze the development trend of a certain industry.

Main features

Multi - domain data query

Supports data queries in multiple fields such as demographics, economic indicators, education data, health statistics, environmental monitoring, and energy use, covering the global scope.

Structured data return

The returned data is in a structured JSON format, containing clear metadata, which is convenient for AI assistants to understand and further process.

Geospatial query

Supports data queries by different geographical levels such as countries, states, counties, and cities, and can perform comparative analysis between regions.

Time - series data

Provides the function of querying historical data, can obtain data trends over multiple years, and supports time - series analysis.

Transparent data source

All data is marked with a clear source institution to ensure the authority and traceability of the data.

Advantages

Authoritative and reliable data: All data comes from government agencies and authoritative organizations, with guaranteed quality.

Completely free: Data Commons is an open - source project, and all data is provided for free.

Easy to use: You can query through natural language without learning complex data query languages.

Data integration: Unifies the format of data from multiple sources, facilitating comparison and analysis.

Real - time update: Data is updated regularly to keep the information up - to - date.

Limitations

Limited data scope: Mainly covers public datasets and does not include commercial or private data.

Update frequency: Some data may not be updated in real - time and there is a certain delay.

Geographical coverage: The data in some regions may not be detailed or complete enough.

Query complexity: Complex data association queries may require professional knowledge.

Network dependency: A stable network connection is required to access the data.

How to use

Install the MCP server

Install the Data Commons MCP server package through pip

Configure the AI platform

Configure the MCP server connection in the AI platform you use (such as Claude Desktop)

Start querying

Ask questions to the AI assistant in natural language, and the AI will automatically call the Data Commons tool to obtain the data

Analyze the results

The AI will return the query results and explain them, and you can continue to ask in - depth questions

Usage examples

Academic research support

A graduate student is writing a paper on educational inequality in the United States and needs data on education funding and student performance in each state.

Business market analysis

An entrepreneur wants to understand the potential market for opening coffee shops in different cities and needs data on population density, income levels, and consumption habits.

Environmental report writing

An environmental protection organization needs to produce a report on the carbon emissions of US states.