PubMed MCP Server

@chrismannina

代码仓库

基本信息

分类

数据与分析

许可证

MIT

传输方式

stdio

作者

chrismannina

关于 PubMed MCP Server

A Model Context Protocol (MCP) server that enables AI assistants to search and analyze PubMed medical literature with advanced filtering, citations, and research tools.

PubMed MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "pubmed-mcp": {
      "command": "python",
      "args": [
        "-m",
        "src.main"
      ]
    }
  }
}

PubMed MCP Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

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。

概览

What is PubMed MCP Server?

A comprehensive Model Context Protocol (MCP) server for PubMed literature search and management. It provides advanced search capabilities, citation formatting, and research analysis tools, integrating with the PubMed API through NCBI E‑utilities. Designed for researchers, developers, and anyone needing programmatic access to PubMed.

How to use PubMed MCP Server?

Install Python 3.8+, obtain a free NCBI API key and a valid email, clone the repository, install dependencies (pip install -r requirements.txt), configure environment variables in a .env file, then run python -m src.main. Use an MCP client to call tools such as search_pubmed, get_article_details, and export_citations.

Key features of PubMed MCP Server

Advanced PubMed search with date ranges, article types, authors, journals, and MeSH terms
Retrieve detailed article information including abstracts, authors, and metadata
Export citations in BibTeX, APA, MLA, Chicago, Vancouver, EndNote, and RIS formats
Search by author with co‑author information
Discover related articles and explore MeSH terms
Analyze publication trends over time and compare multiple articles
Built‑in caching and configurable rate limiting for respectful API usage

Use cases of PubMed MCP Server

Automate literature searches with complex filters for systematic reviews
Fetch and format citations for reference management tools
Analyze research trends by topic over a specified number of years
Compare multiple articles side‑by‑side for literature analysis
Integrate PubMed search capabilities into AI assistants and research workflows

常见问题

What API key is required?

A free NCBI API key is required. Obtain it from NCBI Account Settings under “API Key Management”.

What are the runtime dependencies?

Python 3.8 or higher, an NCBI API key, and a valid email for NCBI API identification.

Does the server store any data locally?

It uses built‑in caching (configurable TTL and max size) for improved performance, but no persistent storage of user data is described.

Are there any rate limits?

The server includes configurable rate limiting to ensure respectful API usage. The default rate limit parameter is `RATE_LIMIT=3.0`.

Can I run the server in a container?

Yes, a Dockerfile and Docker Compose configuration are provided. Build with `make docker-build` and run with `make docker-run` or via `docker-compose`.

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