PubMed Analysis MCP Server

@Darkroaster

Basic information

About PubMed Analysis MCP Server

A PubMed MCP server.

PubMed Analysis MCP Server config

Add this server to your MCP-compatible client using the configuration below.

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

PubMed Analysis MCP Server tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

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.

Frequently asked questions

What dependencies are required?

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

How are search results stored?

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

What is the default maximum number of search results?

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

Is there any usage policy to follow?

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

What license does the project use?

MIT license.

Comments

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