io.github.unibio-intelligence/biomedmcp is an MCP server that integrated public biomedical research with cited, bounded evidence. Its tool list has not been published yet over http and stdio, requires no API key, and scores 59/100 on MCPpedia's security, maintenance and efficiency rubric.
Config is the same across clients - only the file and path differ.
{
"mcpServers": {
"io-github-unibio-intelligence-biomedmcp": {
"args": [
"biomedmcp"
],
"command": "uvx"
}
}
}Are you the author?
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Integrated public biomedical research with cited, bounded evidence.
Run this in your terminal to verify the server starts. Then let us know if it worked - your result helps other developers.
uvx 'biomedmcp' 2>&1 | head -1 && echo "✓ Server started successfully"
After testing, let us know if it worked:
Five weighted categories - click any category to see the underlying evidence.
No known CVEs.
Checked biomedmcp against OSV.dev.
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biomedmcp is a Model Context Protocol (MCP) server that retrieves records from biomedical sources, including PubMed, UniProt, ClinicalTrials.gov, ChEMBL, Open Targets and the Genotype-Tissue Expression (GTEx) project. Each result identifies its source records, so an AI agent's answers can be checked against them.
See the BioMed MCP overview for supported tools and access options.
The following request can be sent from an MCP client:
Build an evidence notebook on LDL-receptor biology and cholesterol lowering. Start with a human LDLR target dossier, inspect tissue expression, then find PCSK9 genetic studies. Separate observations, intervention results and open questions. Cite the records you read and identify any unavailable sources.
Connect to https://biomedmcp.unibiointelligence.com/mcp using Streamable HTTP
and sign in when prompted. For a local server, install the
Python distribution, run the Docker image below, or build from this checkout; see connection options.
Use Python 3.13+ in an activated virtual environment:
python -m pip install biomedmcp==0.2.6
UBI_PROFILE=local biomedmcp build_dossier '{"workflow":"target_dossier","focus":"LDLR","species":9606}'
The single Python distribution includes all source adapters. From a source
checkout, uv sync --frozen installs the locked environment; prefix commands
with uv run --frozen when using it.
The following narrower calls can fill gaps in the notebook:
UBI_PROFILE=local biomedmcp get_record '{"entity":"gene","id":"LDLR","species":9606,"sections":["expression"]}'
UBI_PROFILE=local biomedmcp search_literature '{"query":"PCSK9","filters":{"source":"pubmed","date_from":"2024-01-01"}}'
UBI_PROFILE=local biomedmcp get_variant_evidence '{"id":"PCSK9 R46L"}'
Check each response's source, species and identifiers before combining records; a partial or unavailable section is a gap, not a negative result. Follow the continuation and recovery links in a response to retrieve more than the preview.
| Research task | Where to start |
|---|---|
| Separate target observations from causal claims | Evidence investigations |
| Inspect an allele without conflating transcript or genome build | Typed call examples |
| Read a paper's accessible text and tables | get_record with entity: "article" and sections: ["fulltext"] |
| Examine registered studies | search_trials; inspect each study's submitted record |
| Reproduce a local analysis | Synthetic study walkthrough |
UBI_PROFILE=local biomedmcp-server --transport http --host 127.0.0.1 --port 8000
The versioned image is published to GitHub Container Registry for Linux AMD64. Start a local HTTP server with Docker:
docker run --rm --platform linux/amd64 -p 127.0.0.1:8000:8000 ghcr.io/unibio-intelligence/biomedmcp:0.2.6
For an MCP client that starts its server over stdio:
docker run --rm -i --platform linux/amd64 --no-healthcheck ghcr.io/unibio-intelligence/biomedmcp:0.2.6 biomedmcp-server --transport stdio
Use http://127.0.0.1:8000/mcp, or configure a stdio client using the
connection guide. The call reference covers
exact JSON arguments; [resu