mcp-zenodo — Zenodo MCP Server

mcp-zenodo is a Zenodo MCP server: it connects large language models to Zenodo, the CERN-hosted open-access research repository, through the Model Context Protocol (MCP). It lets an LLM search Zenodo records, read metadata, retrieve citations, and list or download files as callable tools. It is built for developers and researchers who want AI assistants to work with open research datasets, software, and publications. Use it when you want tools like Claude Desktop, Cursor IDE, or a LangChain/LangGraph agent to ground answers in real Zenodo records with DOIs. Do not use it if you need to deposit or publish to Zenodo, since this project is read/retrieval oriented and does not implement upload workflows. Compared with calling the Zenodo REST API directly, mcp-zenodo packages that access as MCP tools so any MCP-compatible client can use Zenodo without writing custom integration code.

One-line value prop: an MCP server for Zenodo that turns open-access research data into LLM-callable tools.

Written in Python. Licensed under Apache-2.0.

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What it does

mcp-zenodo exposes Zenodo's repository of research outputs to LLMs through MCP tools:

• Search and retrieve records — find Zenodo records by query.
• Get citations — retrieve citations in formats such as BibTeX and APA.
• Detect data types — classify a record as dataset, software, or article.
• Access metadata — get detailed metadata for a record.
• List and download files — browse and download files attached to records.

Available tools

Tool	Description
search_records	Search for records in Zenodo
get_metadata	Retrieve detailed metadata of a Zenodo record
get_citation	Return a BibTeX or APA citation of a Zenodo record
detect_data_type	Determine if a record is a dataset, software, or article
compare_records	Compare metadata of multiple Zenodo records
list_files	List available files in a Zenodo record
download_file	Download a specific file from a record
generate_embed_link	Create a direct embeddable link for a record (PDF, dataset)
extract_keywords	Extract top keywords from a record's abstract or text
get_related_records	Find records related to a given Zenodo record
summarize_record	Generate a summary from a record's metadata

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Package structure

Starting from v0.2.0, this repository ships as a single PyPI package (mcp-zenodo) with two sub-packages:

mcp-zenodo/
├── src/
│   ├── mcp_zenodo/          # stdio MCP server (5 tools, core)
│   │   ├── __main__.py       # entry: mcp-zenodo
│   │   ├── server.py         # FastMCP server
│   │   └── ...
│   └── mcp_zenodo_api/       # FastAPI HTTP server (11 tools, optional [api])
│       ├── __main__.py       # entry: mcp-zenodo-api
│       ├── app.py            # FastAPI app
│       └── tools/            # 11 tool modules
├── pyproject.toml
└── README.md

Sub-package	Transport	Tools	Install	Entry point
mcp_zenodo	stdio (MCP SDK)	5 core tools	pip install mcp-zenodo	mcp-zenodo
mcp_zenodo_api	HTTP (FastAPI)	11 tools	pip install mcp-zenodo[api]	mcp-zenodo-api

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30-second quickstart

Option A — Install from PyPI (recommended)

# Core stdio MCP server (Cursor IDE / Claude Desktop)
pip install mcp-zenodo

# Or with uvx (no install needed)
uvx mcp-zenodo

# Full installation with FastAPI HTTP server
pip install "mcp-zenodo[api]"

Then register the server with your MCP client. For Cursor IDE, create an mcp.json (in your project root or ~/.cursor/); for Claude Desktop, add the same block to claude_desktop_config.json:

{
  "mcpServers": {
    "Zenodo": {
      "command": "uvx",
      "args": ["mcp-zenodo"]
    }
  }
}

Restart the client; it will detect and start the Zenodo MCP server. You can then ask the assistant to search Zenodo, fetch metadata, or retrieve a citation.

Option B — FastAPI HTTP server for LangChain / LangGraph / OpenAI-compatible clients

# Install with API extras
pip install "mcp-zenodo[api]"

# Set up environment variables
cp .env.example .env
# Edit .env with your Zenodo API token

# Run the API server
mcp-zenodo-api --host 0.0.0.0 --port 8000

# Or via uvicorn directly
uvicorn mcp_zenodo_api.app:app --host 0.0.0.0 --port 8000

The FastAPI server exposes three endpoints:

• GET /mcp/tools/openai-schema — list all 11 tools in OpenAI function-calling format
• POST /mcp/tools/call — call a tool by name with arguments
• POST /mcp/process — process a full MCP request through the server pipeline

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Example

Ask an MCP-enabled assistant (Claude Desktop, Cursor) a question that triggers the search_records tool:

"Search Zenodo for records about climate model output and show me the top result with its DOI."

The assistant calls the search_records tool, then get_metadata, and returns a record's title, authors, DOI, and description. A representative (abbreviated) tool result looks like:

{
  "hits": [
    {
      "title": "Example Climate Model Output Dataset",
      "doi": "10.5281/zenodo.1234567",
      "type": "dataset",
      "url": "https://zenodo.org/record/1234567"
    }
  ]
}

You can then follow up with get_citation to obtain a BibTeX or APA citation, or list_files / download_file to access the record's files. (Exact fields and values depend on the live Zenodo record returned.)

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Use cases

• Literature and dataset discovery — let an AI assistant search Zenodo and surface relevant datasets, software, and papers with DOIs.
• Citation retrieval — pull BibTeX or APA citations for records directly into a manuscript or notebook.
• Research agents — give LangChain/LangGraph agents a Zenodo tool so they can ground answers in open research data.
• In-editor research — query Zenodo from Cursor IDE or Claude Desktop while writing code or documentation.
• Reproducibility workflows — locate, inspect metadata for, and download research artifacts programmatically.

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Comparison and alternatives

• vs. calling the Zenodo REST API directly — the Zenodo REST API gives full control (including deposition/upload), but you must write and maintain the integration yourself. mcp-zenodo wraps common read/retrieval operations as MCP tools so any MCP client can use them with no custom code.
• vs. general-purpose HTTP/fetch MCP servers — a generic fetch tool can hit Zenodo URLs but has no knowledge of records, metadata, citations, or file listings. mcp-zenodo provides Zenodo-specific, typed tools.
• vs. other research-repository MCP servers — comparable MCP servers exist for sources such as arXiv and other data repositories; mcp-zenodo is focused specifically on Zenodo. It ships two integration paths (a stdio MCP server and a FastAPI service), which lets you choose between direct MCP clients and LLM-framework/OpenAI-compatible usage.

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Limitations / when NOT to use

• Not for depositing or publishing — this project focuses on searching and retrieving from Zenodo; it does not implement Zenodo deposition/upload workflows.
• Depends on the Zenodo API — availability, rate limits, and returned fields are governed by Zenodo; some operations may require a Zenodo API token.
• Retrieval quality depends on Zenodo metadata — data-type detection, keyword extraction, and summaries are derived from record metadata and are best-effort.
• Two transport modes — the stdio MCP server (mcp-zenodo) and the FastAPI HTTP server (mcp-zenodo-api) serve different use cases; pick the one matching your client.

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FAQ

What is mcp-zenodo? mcp-zenodo is a Zenodo MCP server — an implementation of the Model Context Protocol that exposes Zenodo (the CERN open-access research repository) to large language models as callable tools for searching records, reading metadata, retrieving citations, and listing or downloading files.

How do I use Zenodo with Claude or ChatGPT via MCP? For Claude Desktop or Cursor IDE, install the mcp_sdk_core server and add it to your MCP client config (claude_desktop_config.json or mcp.json) as shown in the quickstart. For OpenAI-compatible / ChatGPT-style workflows, run the mcp_api FastAPI service and call its MCP-compatible tools from your framework (e.g., LangChain, LangGraph) or OpenAI-compatible client.

Which MCP clients are supported? Any MCP-compatible client can use the mcp_sdk_core server over the standard MCP transport; it is tested with Cursor IDE and works with Claude Desktop. The mcp_api service targets LLM frameworks such as LangChain and LangGraph, OpenAI-compatible clients, and platforms such as LibreChat.

Do I need a Zenodo API token? The FastAPI service reads a Zenodo API token from .env (see .env.example). Some Zenodo operations work without a token, but a token is recommended for reliable access.

Is mcp-zenodo on PyPI? Yes. Starting from v0.2.0, install with pip install mcp-zenodo or uvx mcp-zenodo. The FastAPI HTTP server is available as pip install mcp-zenodo[api].

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Contributing

Contributions to both implementations are welcome. Please see the implementation READMEs (mcp_sdk_core, mcp_api) for details.

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License

This project is licensed under the Apache License 2.0 — see the LICENSE file for details.

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Citation

If you use mcp-zenodo in your work, please cite it. Citation metadata is provided in CITATION.cff. Example:

Seyedkazemi Ardebili, M. mcp-zenodo: A Zenodo MCP server. https://github.com/MSKazemi/mcp-zenodo