Open Data Model Context Protocol

Open Data MCP is an open - source project aiming to quickly connect public data sets to LLM applications through the MCP protocol. The project provides a CLI tool to achieve a 2 - minute connection (currently supporting Claude) and establishes a community collaboration framework that allows developers to easily contribute and publish public data sets. The core goal is to build an infrastructure that connects all LLM applications with millions of public data sets.

Research and data Developer tools #Public data #LLM connection #Rapid access #Community collaboration .Python

rating : 3 points

downloads : 26.3K

update time : 2025-04-23

Open Site

What is Open Data MCP?

Open Data MCP is an open - source tool that helps developers and organizations quickly connect public data sets to large language model (LLM) applications. It provides standardized data access interfaces and publication channels.

How to use Open Data MCP?

It can be installed and configured through simple CLI commands. Currently, it supports the Claude desktop application, and more LLM platforms will be supported in the future.

Applicable scenarios

It is suitable for scenarios where public data (such as government open data, corporate public data, etc.) needs to be integrated into LLM applications.

Main features

Open data access

Provides standardized access interfaces for various public data sets

Data publication platform

Provides publication channels and community support for data providers

Convenient CLI tool

The MCP server can be configured with simple commands

Advantages

Rapid integration: Connect open data to LLM applications within minutes

Standardized interface: Unified data access protocol

Community support: Obtain network effects for data publication and use

Limitations

Currently only supports the Claude application

Data must comply with open license agreements

Commercial applications must comply with the project license

How to use

Install dependencies

Ensure that the Claude desktop application and the uv tool are installed

Set up data providers

Use the CLI tool to configure the required data sources

Restart the application

Restart the Claude application to use the new features

Usage examples

Query Swiss railway information

Obtain train schedules and delay information through the SBB data provider

Frequently Asked Questions

Which LLM applications are supported?

How to contribute a new data provider?

Related resources

GitHub repository

Project source code and documentation

Discord community

Developer communication community

MCP protocol specification

Official documentation of the Model Context Protocol

🚀 Open Data MCP Project

This project aims to provide globally publicly available data resources to large language model (LLM) applications through the Model Context Protocol (MCP). Our goal is to build a community-driven infrastructure that enables all LLMs to access millions of open datasets.

🚀 Quick Start

✨ Features

Access:

Open Data Query: Support direct querying of open data sources via the MCP protocol.
Searchability: Provide classification and indexing functions for open data sources.
Remote Access: Support remote data retrieval via standard input/output (stdio) or server-sent events (SSE).

Publication:

Create MCP Server: Easily build custom MCP servers to host open datasets.
Community Contribution: Welcome community members to publish their own open datasets and provide support for existing datasets.
Improved Usability: Strive to simplify the process and documentation for dataset publication.

📦 Installation

No installation steps provided in the original document.

💻 Usage Examples

No code examples provided in the original document.

📚 Documentation

Contribution Guide

Join the Community

We welcome all individuals and organizations passionate about open-source technology to join. You can participate in the following ways:

Submit Code Fixes
Add New Features
Test and Validate

Core Principles

Simplicity: Keep the codebase concise and avoid unnecessary complexity.
Standardization: Follow unified templates and guidelines.
Dependency Management: Minimize external dependencies to ensure the project's light weight.

Project Roadmap

Current Priorities:

Initialize the repository and establish the basic framework.
Deploy a continuous integration/delivery (CI/CD) pipeline for automated PyPI publishing.
Develop provider templates and reference implementations.
Actively Recruit Contributors: Collaborate to drive the integration of more open datasets.

Future Plans:

Extend the functionality of the MCP protocol, such as supporting authentication, rate limiting, etc.
Deploy hosting services to improve access efficiency.
Establish clear guidelines to distinguish the use cases of "tools" and "resources".

Limitations

All data provided through the Open Data MCP server must be open.
The data license agreements of the original data providers must be complied with.
The project license information needs to be referenced in commercial applications.