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PubMed Analysis MCP Server

@Darkroaster

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

Darkroaster

关于 PubMed Analysis MCP Server

A PubMed MCP server.

PubMed Analysis 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": {
    "pubmearch": {
      "command": "uv",
      "args": [
        "pip",
        "install",
        "-e",
        "."
      ]
    }
  }
}

PubMed Analysis 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 Analysis MCP Server?

A professional MCP server for analyzing PubMed medical literature. It helps researchers quickly gain insights into medical research dynamics by supporting advanced search syntax, keyword frequency analysis, trend tracking, publication counts, and comprehensive report generation.

How to use PubMed Analysis MCP Server?

Install dependencies using uv pip install -e . in the pyproject.toml directory. Configure the MCP server in mcp.json with the command uv run --directory path/to/project -m pubmearch.server, and set environment variables NCBI_USER_EMAIL and NCBI_USER_API_KEY. Use the provided tools (e.g., search_pubmed, analyze_research_keywords) via LLM prompts.

Key features of PubMed Analysis MCP Server

  • Literature retrieval with PubMed advanced search syntax and date filtering.
  • Hotspot analysis: keyword frequency and popular research area identification.
  • Trend tracking: keyword frequency changes over time.
  • Publication count analysis with customizable time periods.
  • Comprehensive reports: one-click generation of hotspot, trend, and statistics.

Use cases of PubMed Analysis MCP Server

  • Identify research hotspots on a specific medical topic (e.g., prostate cancer immunotherapy).
  • Track how research trends evolve over custom date ranges.
  • Generate a full analytical report of publication volume and keyword dynamics.
  • Retrieve and save PubMed search results for offline review.

常见问题

1

What dependencies are required?

Python environment with `uv` is recommended. The server requires NCBI API credentials (`NCBI_USER_EMAIL` and `NCBI_USER_API_KEY`).

2

How are search results stored?

Results are saved in the `pubmearch/results` directory; logs are in `pubmed_server.log`.

3

What is the default maximum number of search results?

The default `max_results` for `search_pubmed` is 1000.

4

Is there any usage policy to follow?

Yes, you must follow NCBI's API usage policies.

5

What license does the project use?

MIT license.

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