Opendatamcp

🚀 Open Data MCP Protocol Server

This project aims to integrate millions of publicly available datasets into all large language model (LLM) applications, providing open - data access via the MCP protocol.

🚀 Quick Start

The Open Data MCP Protocol Server project is initiated to bring millions of publicly available datasets into all large language model (LLM) applications. The goal is to offer open - data access for LLM applications through the MCP protocol, enabling these data to be used by various LLM tools and platforms.

✨ Features

• Provide access to publicly available datasets for LLM applications.
• Support different types of datasets, including static and dynamic ones.
• Facilitate community contributions in both code and documentation.

📦 Installation

No specific installation steps are provided in the original document, so this section is skipped.

💻 Usage Examples

Basic Usage

Dataset Access

Through our MCP server, you can search and query publicly available datasets. Here are the main types of datasets and their applicable scenarios:

1. Static Datasets

   • If the dataset content is relatively fixed and has a low update frequency, it is recommended to provide it as a resource.
   • Examples: Small tabular data, document libraries, etc.

2. Dynamic Datasets

   • For datasets that require real - time querying or calculation, they should be implemented as tool interfaces.
   • Examples: Database queries, API call results, etc.

Implementation Guide

• Choose between Tool or Resource

  • If your dataset requires parameter - based filtering, complex transformation, or dynamic querying, implement it as a tool interface.
  • If the dataset content is static and suitable for in - memory loading, it is recommended to provide it as a resource.

• Testing Requirements

  • Add comprehensive tests to ensure normal functionality.
  • Verify the error - handling mechanism and performance.

📚 Documentation

Contribution Guide

We welcome community contributions to expand this project. The following are the types of contributions we accept:

Code Contribution

• Submit new MCP server implementations (based on tools or resources).
• Fix issues in the existing code.
• Enhance functional modules.

Documentation Contribution

• Write detailed documentation.
• Organize and supplement the usage guide.
• Optimize example content.

Project Architecture

open-data-mcp
├── src/
│   ├── mcp/
│   │   └── server.py      # MCP protocol server implementation
│   └── providers/        # Directory for implementations of each dataset provider
│       ├── __init__.py
│       ├── tool_provider.py    # Example of a tool interface
│       └── resource_provider.py  # Example of a resource provider
├── tests/
│   ├── test_server.py     # Server functionality test
│   └── test_providers/    # Test cases for each dataset provider
└── README.md               # Project description file

Development Specifications

Code Style

• Follow the PEP8 coding standard.
• Use Black or Ruff for formatting.

Version Control

• Each commit should include detailed commit information.
• Conduct regular code reviews.

Project Status

• Implemented Features

  • Initial server architecture
  • Basic data provider interface
  • Test framework establishment
  • Automated release process

• Features to be Developed

  • Support for more data source types
  • Client tool development
  • Performance optimization

Discussion Community

Welcome to join our Discord community to participate in project discussions and contributions:

Discord Invitation Link

Limitations

• All data provided through the MCP server must be publicly available.
• The license agreements of the original data providers must be complied with.
• Commercial applications must follow the requirements of the project's MIT license.

References

We are grateful for the benefits released by Anthropic's open - source MCP protocol.

📄 License

This project is licensed under the MIT license. Please refer to the LICENSE file for specific terms.