PubMed-MCP
A Model Context Protocol (MCP) server that provides tools for searching PubMed articles using the NCBI Entrez API.
Author: Emilio Delgado Muñoz
Features
- Search PubMed for articles based on queries
- Retrieve detailed information including title, authors, abstract, journal, and publication date
- Returns results in JSON format
- Configurable maximum number of results
Architecture
graph TB
A[User] --> B[MCP Server<br/>pubmed_server.py]
B --> C[search_pubmed function]
C --> D[Entrez.esearch<br/>Search in PubMed]
D --> E[PubMed database<br/>NCBI]
E --> F[List of PMIDs]
F --> G[Entrez.efetch<br/>Fetch details]
G --> E
G --> H[Article XML records]
H --> I[Data processing]
I --> J[Extraction of:<br/>- Title<br/>- Authors<br/>- Abstract<br/>- Journal<br/>- Date]
J --> K[List of articles<br/>in JSON format]
K --> L[Response to user]
subgraph "Dependencies"
M[BioPython<br/>requirements.txt]
N[FastMCP<br/>requirements.txt]
end
B -.-> M
B -.-> N
subgraph "Configuration"
O[Entrez.email<br/>Configured in code]
end
C -.-> O
style A fill:#e1f5fe
style L fill:#c8e6c9
style E fill:#fff3e0
Installation
-
Clone this repository:
git clone <repository-url> cd PubMed-MCP -
Install dependencies:
uv sync -
Configure your email in
pubmed_server.py:Entrez.email = 'your-email@example.com' # Replace with your actual email
VS Code Configuration
To use this MCP server locally in VS Code, the project includes a pre-configured .vscode/mcp.json file. This file tells VS Code how to run the MCP server.
The configuration is already set up to use uv for running the server:
{
"servers": {
"pubmed-mcp": {
"command": "uv",
"args": ["run", "${workspaceFolder}/pubmed_server.py"]
}
}
}
Requirements for VS Code Integration
- VS Code with MCP extension support
uvpackage manager installed- Python virtual environment set up
Alternative Configuration
If you prefer to use pip instead of uv, you can modify the .vscode/mcp.json file:
{
"servers": {
"pubmed-mcp": {
"command": "python",
"args": ["${workspaceFolder}/pubmed_server.py"]
}
}
}
Make sure your virtual environment is activated when using this configuration.
Requirements
- Python 3.11+
- BioPython
- FastMCP
Usage
Run the MCP server:
python pubmed_server.py
The server will start and listen for MCP protocol messages on stdin/stdout.
Available Tools
search_pubmed
Searches PubMed for articles matching the given query.
Parameters:
query(string): The search querymax_results(integer, optional): Maximum number of results to return (default: 10)title(bool, optional): If true (default) search in Title fieldabstract(bool, optional): If true (default) search in Abstract fieldkeywords(bool, optional): If true (default) expand search with Author Keywords ([ot]) and MeSH Headings ([mh])
Field logic:
title=Trueandabstract=True-> query applied as(your terms)[tiab]- Only
title=True->(your terms)[ti] - Only
abstract=True->(your terms)[ab] - Both false -> no field tag (all fields)
keywords=True-> OR-expanded with(your terms)[ot] OR (your terms)[mh]
Example refined queries:
query = "breast cancer metastasis"
title=True, abstract=True, keywords=True -> (breast cancer metastasis)[tiab] OR ((breast cancer metastasis)[ot] OR (breast cancer metastasis)[mh])
title=True, abstract=False, keywords=False -> (breast cancer metastasis)[ti]
title=False, abstract=False, keywords=True -> (breast cancer metastasis) OR ((breast cancer metastasis)[ot] OR (breast cancer metastasis)[mh])
Returns: A list of article objects containing:
pmid: PubMed IDtitle: Article titleauthors: List of author namesabstract: Article abstractjournal: Journal namepublication_year: Year of publicationpublication_month: Month of publicationurl: PubMed URL
Configuration
Before using the tool, you must set your email address in the Entrez.email variable. This is required by NCBI's Entrez API.
License
This project is open source. Please check the license file for details.
Contributing
Contributions are welcome! Please feel free to submit a Pull Request.