MCP Simple PubMed
@andybrandt
About MCP Simple PubMed
MCP server for searching and querying PubMed medical papers/research database
Config
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"mcp-simple-pubmed": {
"command": "npx",
"args": [
"-y",
"@smithery/cli",
"install",
"mcp-simple-pubmed",
"--client",
"claude"
]
}
}
}Tools
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Fetch the live tool list by running this server in a temporary sandbox using the button above.
Overview
What is MCP Simple PubMed?
MCP Simple PubMed is an MCP server that provides access to PubMed articles and abstracts through the Entrez API. It is intended for researchers, clinicians, and AI assistants needing to search biomedical literature.
How to use MCP Simple PubMed?
Install via pip install mcp-simple-pubmed or automatically via Smithery with npx -y @smithery/cli install mcp-simple-pubmed --client claude. Set environment variables PUBMED_EMAIL (required) and optionally PUBMED_API_KEY. Then add the server configuration to your Claude Desktop claude_desktop_config.json specifying the Python module mcp_simple_pubmed.
Key features of MCP Simple PubMed
- Search PubMed using keywords, MeSH terms, author names, and Boolean operators.
- Retrieve full text for open-access articles in PubMed Central.
- Access article abstracts and metadata via resource URIs.
- Pre-built MCP prompts for systematic reviews, PICO searches, and author searches.
- Returns an XML-ized version of full text for structured readability by AI.
Use cases of MCP Simple PubMed
- Conduct systematic literature reviews with comprehensive search strategies.
- Build clinical PICO (Population, Intervention, Comparison, Outcome) queries.
- Find all publications by a specific author with correct name formatting.
- Retrieve abstracts and metadata for research or evidence synthesis.
FAQ from MCP Simple PubMed
Does MCP Simple PubMed always return full text for every article?
No. Full text is only available for open-access articles in PubMed Central. For other articles, the tool may return an abstract or indicate unavailability. If you cannot access a paper through the tool, try manually with a regular web browser or through your library.
Can MCP Simple PubMed access paywalled papers?
No. The tool cannot access paywalled or paid papers. You may be able to read them through your library access or other means.
What are the rate limits for MCP Simple PubMed?
The default NCBI rate limit is 3 requests per second. You can optionally register for an API key to increase this to 10 requests per second.
What should I do if I encounter an SSL certificate error on macOS?
Run the Install Certificates.command script that comes with Python from python.org (e.g., /Applications/Python 3.13/Install Certificates.command), then restart Claude Desktop for the change to take effect.
What format does the full text come in?
Full text is returned as an XML-ized version, which is designed to be more useful for AI models by preserving document structure.
Frequently asked questions
Does MCP Simple PubMed always return full text for every article?
No. Full text is only available for open-access articles in PubMed Central. For other articles, the tool may return an abstract or indicate unavailability. If you cannot access a paper through the tool, try manually with a regular web browser or through your library.
Can MCP Simple PubMed access paywalled papers?
No. The tool cannot access paywalled or paid papers. You may be able to read them through your library access or other means.
What are the rate limits for MCP Simple PubMed?
The default NCBI rate limit is 3 requests per second. You can optionally register for an API key to increase this to 10 requests per second.
What should I do if I encounter an SSL certificate error on macOS?
Run the `Install Certificates.command` script that comes with Python from python.org (e.g., `/Applications/Python 3.13/Install Certificates.command`), then restart Claude Desktop for the change to take effect.
What format does the full text come in?
Full text is returned as an XML-ized version, which is designed to be more useful for AI models by preserving document structure.
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