Which implementation should I use?
Use the MCP SDK Core if you want to access Zenodo directly from MCP‑enabled environments like Cursor IDE. Use the MCP API if you are building LLM applications with LangChain, LangGraph, or similar frameworks.
@MSKazemi
基本信息
Tool-based LLM integration with Zenodo via the Model Context Protocol (MCP)
工具列表暂未提供
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工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。
A comprehensive toolkit for interacting with Zenodo records through the Model Context Protocol (MCP). It provides two implementations: a Python‑based MCP SDK for direct integration with MCP‑enabled environments like Cursor IDE, and a FastAPI‑based MCP API service for use with LLM frameworks such as LangChain and LangGraph.
Choose the implementation that fits your workflow. For Cursor IDE integration, clone the repository, install dependencies (pip install -r requirements.txt), and configure via an mcp.json file. For LLM framework integration, clone the repository, copy .env.example to .env and add your Zenodo API token, then run the API server with uvicorn server.main:app --host 0.0.0.0 --port 8000.
Use the MCP SDK Core if you want to access Zenodo directly from MCP‑enabled environments like Cursor IDE. Use the MCP API if you are building LLM applications with LangChain, LangGraph, or similar frameworks.
Both implementations are Python‑based. The MCP SDK Core requires Python and the packages listed in `requirements.txt`. The MCP API requires Python, FastAPI, and an environment variable for the Zenodo API token.
All data and records reside on Zenodo’s servers. The MCP server acts as a client to search, retrieve, and download Zenodo records.
For the MCP API, you must set a Zenodo API token in a `.env` file. The MCP SDK Core’s authentication details are managed via its configuration file.
The README does not explicitly list limitations. Both implementations rely on the Zenodo API and may be subject to its rate limits and access restrictions.

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