MCP 服务器

Data Commons MCP Server

Enables AI agents to query and retrieve public statistical data from Data Commons through search and observation tools. Provides access to demographic, economic, and other statistical indicators for analysis and research.

README

Data Commons MCP Server

A fully functional Model Context Protocol (MCP) server for accessing public statistical data from Data Commons. This server is optimized for deployment on Railway.app and can be accessed remotely by MCP clients like Manus, Claude Desktop, and other MCP-enabled applications.

Overview

Data Commons is an open knowledge repository providing a unified view across multiple public datasets and statistics. This MCP server enables AI agents and applications to query the Data Commons knowledge graph through a standardized protocol.

Key Features

MCP-Compliant: Implements the Model Context Protocol for seamless agent integration
Data Commons Access: Fetches public statistics from the datacommons.org knowledge graph
Custom Instance Support: Can be configured to work with custom Data Commons instances
Railway-Ready: Pre-configured for one-click deployment on Railway.app
Remote Access: Accessible via HTTP for remote MCP clients
Comprehensive Tools: Includes tools for searching indicators and fetching observations

Architecture

The server provides two main MCP tools:

search_indicators: Search and discover statistical variables (indicators) available in Data Commons
get_observations: Fetch actual statistical data for specific variables and places

Quick Start

Prerequisites

Data Commons API Key: Create one at apikeys.datacommons.org
Python 3.11+: Required for local development
Railway Account: For deployment (optional)

Local Development

Clone the repository:

git clone https://github.com/ARJ999/Data-Commons-mcp-server.git
cd Data-Commons-mcp-server

Set up environment:

cp .env.example .env
# Edit .env and add your DC_API_KEY

Install dependencies:
```
pip install -r requirements.txt
```

Run the server:

python -m datacommons_mcp.cli serve http --host 0.0.0.0 --port 8080

Access the MCP endpoint:
```
http://localhost:8080/mcp
```

Railway Deployment

One-Click Deploy

Manual Deployment

Create a new Railway project:
- Go to railway.app
- Click "New Project" → "Deploy from GitHub repo"
- Select this repository
Configure environment variables:
- Add DC_API_KEY with your Data Commons API key
- Railway automatically sets PORT
Deploy:
- Railway will automatically detect the configuration and deploy
- Your MCP server will be available at: https://your-app.railway.app/mcp

Environment Variables

Variable	Required	Description
`DC_API_KEY`	Yes	Your Data Commons API key from apikeys.datacommons.org
`DC_API_ROOT`	No	Custom Data Commons instance URL (defaults to datacommons.org)
`PORT`	No	Server port (Railway sets this automatically)

Using with MCP Clients

Manus

Configure Manus to connect to your deployed MCP server:

{
  "mcpServers": {
    "datacommons": {
      "url": "https://your-app.railway.app/mcp",
      "transport": "http"
    }
  }
}

Claude Desktop

Add to your Claude Desktop MCP settings:

{
  "mcpServers": {
    "datacommons": {
      "command": "curl",
      "args": ["-X", "POST", "https://your-app.railway.app/mcp"]
    }
  }
}

Other MCP Clients

Any MCP-enabled client can connect using the HTTP endpoint:

Endpoint: https://your-app.railway.app/mcp
Transport: Streamable HTTP
Protocol: MCP (Model Context Protocol)

Available Tools

1. search_indicators

Search for statistical variables (indicators) in Data Commons.

Parameters:

query (string): Natural language search query
place_dcids (list, optional): Filter by specific place DCIDs
topic_dcids (list, optional): Filter by topic DCIDs

Example:

search_indicators(
    query="population growth rate",
    place_dcids=["country/USA"]
)

2. get_observations

Fetch statistical observations for a variable and place.

Parameters:

variable_dcid (string): Variable identifier from search_indicators
place_dcid (string): Place identifier
child_place_type (string, optional): Get data for child places
date (string, optional): Date filter ('latest', 'all', or specific date)
date_range_start (string, optional): Start of date range
date_range_end (string, optional): End of date range

Example:

get_observations(
    variable_dcid="Count_Person",
    place_dcid="country/USA",
    date="latest"
)

Project Structure

Data-Commons-mcp-server/
├── datacommons_mcp/          # Main package
│   ├── __init__.py
│   ├── server.py             # MCP server implementation
│   ├── cli.py                # Command-line interface
│   ├── clients.py            # Data Commons API client
│   ├── services.py           # Business logic
│   ├── settings.py           # Configuration
│   ├── data_models/          # Pydantic models
│   └── ...
├── requirements.txt          # Python dependencies
├── pyproject.toml           # Project metadata
├── Procfile                 # Railway start command
├── railway.json             # Railway configuration
├── runtime.txt              # Python version
├── .env.example             # Environment template
├── .gitignore              # Git ignore rules
└── README.md               # This file

Technical Details

Dependencies

FastAPI: Web framework for HTTP server
FastMCP: MCP protocol implementation
Uvicorn: ASGI server
datacommons-client: Official Data Commons Python client
Pydantic: Data validation and settings management

Transport Modes

The server supports two transport modes:

Streamable HTTP (default for Railway):
- Accessible via HTTP/HTTPS
- Suitable for remote clients
- Endpoint: /mcp
stdio (for local integrations):
- Communicates via standard input/output
- Used by local MCP clients like Gemini CLI