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PubMed Analysis MCP Server

@Darkroaster

リポジトリ

基本情報

カテゴリ

データと分析

ライセンス

MIT

トランスポート

stdio

作者

Darkroaster

PubMed Analysis MCP Server について

A PubMed MCP server.

PubMed Analysis MCP Server の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

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

PubMed Analysis MCP Server のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

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.

よくある質問

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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