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    Biomcp

    Biomedical Model Context Protocol Python-based implementation.

    327 stars
    Python
    Updated Oct 16, 2025
    ai
    bioinformatics
    clinical-trials
    genomics
    llm
    mcp
    mcp-server
    medical
    model-context-protocol
    pubmed
    pubmed-central

    Table of Contents

    • Description
    • Features
    • Installation
    • PyPI tool install
    • Binary install
    • Claude Desktop extension (.mcpb)
    • Install skills
    • MCP clients
    • Remote HTTP server
    • From source
    • Quick start
    • Command grammar
    • Entities and sources
    • Cross-entity helpers
    • Gene-set enrichment
    • Sections and progressive disclosure
    • API keys
    • Configuration
    • Claude Desktop extension settings
    • Usage Examples
    • Public cross-entity overview
    • Public variant evidence
    • Credentialed OncoKB example
    • Credentialed DisGeNET example
    • Privacy Policy
    • Multi-worker deployment
    • Skills
    • Local study analytics
    • Ops
    • Support
    • Documentation
    • Citation
    • Data Sources and Licensing
    • License

    Table of Contents

    • Description
    • Features
    • Installation
    • PyPI tool install
    • Binary install
    • Claude Desktop extension (.mcpb)
    • Install skills
    • MCP clients
    • Remote HTTP server
    • From source
    • Quick start
    • Command grammar
    • Entities and sources
    • Cross-entity helpers
    • Gene-set enrichment
    • Sections and progressive disclosure
    • API keys
    • Configuration
    • Claude Desktop extension settings
    • Usage Examples
    • Public cross-entity overview
    • Public variant evidence
    • Credentialed OncoKB example
    • Credentialed DisGeNET example
    • Privacy Policy
    • Multi-worker deployment
    • Skills
    • Local study analytics
    • Ops
    • Support
    • Documentation
    • Citation
    • Data Sources and Licensing
    • License

    Documentation

    BioMCP

    One binary. One grammar. Evidence from the biomedical sources you already trust.

    Description

    BioMCP cuts through the usual biomedical data maze: one query reaches the

    sources that normally live behind different APIs, identifiers, and search

    habits. Researchers, clinicians, and agents use the same command grammar to

    search, focus, and pivot without rebuilding the workflow for each source. You

    get compact, evidence-oriented results across live public data plus local study analytics.

    Features

    • Search the literature: search article fans out across PubTator3 and

    Europe PMC, deduplicates PMID/PMCID/DOI identifiers, and can add a Semantic

    Scholar leg when your filters support it.

    • Pivot without rework: move from a gene, variant, drug, disease, pathway,

    protein, or article straight into the next built-in view instead of

    rebuilding filters by hand.

    • Analyze studies locally: study commands cover local query, cohort, survival,

    compare, and co-occurrence workflows with native terminal, SVG, and PNG

    charts for downloaded cBioPortal-style datasets.

    • Follow the paper trail: article citations, article references,

    article recommendations, and article entities turn one known paper into a

    broader evidence map.

    • Enrich and batch: use biomcp enrich for top-level g:Profiler

    enrichment and biomcp batch for up to 10 focused get calls in one

    command.

    Installation

    PyPI tool install

    bash
    uv tool install biomcp-cli
    # or: pip install biomcp-cli

    This installs the biomcp binary on your PATH.

    Binary install

    bash
    curl -fsSL https://biomcp.org/install.sh | bash

    Claude Desktop extension (.mcpb)

    Install BioMCP from the Anthropic Directory in Claude Desktop when that path is

    available for your environment. For local/manual setups, use the JSON MCP

    config below.

    Install skills

    Install guided investigation workflows into your agent directory:

    bash
    biomcp skill install ~/.claude --force

    MCP clients

    json
    {
      "mcpServers": {
        "biomcp": {
          "command": "biomcp",
          "args": ["serve"]
        }
      }
    }

    Remote HTTP server

    For shared or remote deployments:

    bash
    biomcp serve-http --host 127.0.0.1 --port 8080

    Remote clients connect to http://127.0.0.1:8080/mcp. Probe routes are

    GET /health, GET /readyz, and GET /.

    Runnable demo:

    bash
    uv run --script examples/streamable-http/streamable_http_client.py

    See Remote HTTP Server for

    the newcomer guide.

    From source

    bash
    make install
    "$HOME/.local/bin/biomcp" --version

    Quick start

    First useful query in under 30 seconds:

    bash
    uv tool install biomcp-cli
    biomcp health --apis-only
    biomcp list gene
    biomcp search all --gene BRAF --disease melanoma  # unified cross-entity discovery
    biomcp get gene BRAF pathways hpa

    Command grammar

    text
    search  [filters]    → discovery
    discover              → concept resolution before entity selection
    get   [sections] → focused detail
             → cross-entity pivots
    enrich      → gene-set enrichment
    batch   → parallel gets
    search all [slot filters]    → counts-first cross-entity orientation

    Entities and sources

    EntityUpstream providers used by BioMCPExample
    geneMyGene.info, UniProt, Reactome, QuickGO, STRING, GTEx, Human Protein Atlas, DGIdb, ClinGenbiomcp get gene BRAF pathways hpa
    variantMyVariant.info, ClinVar, gnomAD fields via MyVariant, CIViC, Cancer Genome Interpreter, OncoKB, cBioPortal, GWAS Catalog, AlphaGenomebiomcp get variant "BRAF V600E" clinvar
    articlePubMed, PubTator3, Europe PMC, PMC OA, NCBI ID Converter, Semantic Scholar (optional auth; S2_API_KEY recommended)biomcp search article -g BRAF --limit 5
    trialClinicalTrials.gov API v2, NCI CTS APIbiomcp search trial -c melanoma -s recruiting
    drugMyChem.info, EMA local batch, ChEMBL, OpenTargets, Drugs@FDA, OpenFDA, CIViCbiomcp get drug Keytruda regulatory --region eu
    diseaseMyDisease.info, Monarch Initiative, MONDO, OpenTargets, Reactome, CIViCbiomcp get disease "Lynch syndrome" genes
    pathwayReactome, KEGG, g:Profiler, Enrichr-backed enrichment sectionsbiomcp get pathway hsa05200 genes
    proteinUniProt, InterPro, STRING, ComplexPortal, PDB, AlphaFoldbiomcp get protein P15056 complexes
    adverse-eventOpenFDA FAERS, MAUDE, Recallsbiomcp search adverse-event --drug pembrolizumab
    pgxCPIC, PharmGKBbiomcp get pgx CYP2D6 recommendations
    gwasGWAS Catalogbiomcp search gwas --trait "type 2 diabetes"
    phenotypeMonarch Initiative (HPO semantic similarity)biomcp search phenotype "HP:0001250"

    Cross-entity helpers

    Pivot between related entities without rebuilding filters.

    See the cross-entity pivot guide for when

    to use a helper versus a fresh search.

    bash
    biomcp variant trials "BRAF V600E" --limit 5
    biomcp variant articles "BRAF V600E"
    biomcp drug adverse-events pembrolizumab
    biomcp drug trials pembrolizumab
    biomcp disease trials melanoma
    biomcp disease drugs melanoma
    biomcp disease articles "Lynch syndrome"
    biomcp gene trials BRAF
    biomcp gene drugs BRAF
    biomcp gene articles BRCA1
    biomcp gene pathways BRAF
    biomcp pathway drugs R-HSA-5673001
    biomcp pathway drugs hsa05200
    biomcp pathway articles R-HSA-5673001
    biomcp pathway trials R-HSA-5673001
    biomcp protein structures P15056
    biomcp article entities 22663011
    biomcp article citations 22663011 --limit 3
    biomcp article references 22663011 --limit 3
    biomcp article recommendations 22663011 --limit 3

    Gene-set enrichment

    bash
    biomcp enrich BRAF,KRAS,NRAS --limit 10

    Top-level biomcp enrich uses g:Profiler. Gene enrichment sections inside

    other entity views still reference Enrichr where that is the backing

    source.

    Sections and progressive disclosure

    Every get command supports selectable sections for focused output:

    bash
    biomcp get gene BRAF                    # summary card
    biomcp get gene BRAF pathways           # add pathway section
    biomcp get gene BRAF hpa                # protein tissue expression + localization
    biomcp get gene BRAF civic interactions # multiple sections
    biomcp get gene BRAF all                # everything
    
    biomcp get variant "BRAF V600E" clinvar population conservation
    biomcp get article 22663011 tldr
    biomcp get drug pembrolizumab label targets civic approvals
    biomcp get drug Keytruda regulatory --region eu
    biomcp get disease "Lynch syndrome" genes phenotypes variants
    biomcp get trial NCT02576665 eligibility locations outcomes

    In JSON mode, get responses expose _meta.next_commands for the next likely

    follow-ups and _meta.section_sources for section-level provenance. `batch ...

    --json` returns per-entity objects with the same metadata shape.

    API keys

    Most commands work without credentials. Optional keys improve rate limits or

    unlock optional enrichments:

    bash
    export NCBI_API_KEY="..."        # PubTator, PMC OA, NCBI ID converter
    export S2_API_KEY="..."          # Optional Semantic Scholar auth; dedicated quota at 1 req/sec
    export OPENFDA_API_KEY="..."     # OpenFDA rate limits
    export NCI_API_KEY="..."         # NCI CTS trial search (--source nci)
    export ONCOKB_TOKEN="..."        # OncoKB variant helper
    export ALPHAGENOME_API_KEY="..." # AlphaGenome variant effect prediction

    search article, get article, article batch, get article ... tldr, and

    the explicit Semantic Scholar helpers all work without S2_API_KEY. With the

    key, BioMCP sends authenticated requests and uses a dedicated rate limit at

    1 req/sec. Without it, BioMCP uses the shared unauthenticated pool at 1 req/2sec.

    search article --source supports all, pubtator, europepmc, and

    pubmed. The

    default compatible article federation uses PubTator3, Europe PMC, and PubMed,

    while the S2 leg remains automatic rather than directly selectable. References

    and recommendations can be empty for paywalled papers because of publisher

    elision in Semantic Scholar upstream coverage.

    Configuration

    Claude Desktop extension settings

    The directory bundle exposes only the optional settings needed for the first

    reviewer-facing build:

    Claude Desktop fieldRuntime env varPurpose
    OncoKB TokenONCOKB_TOKENEnables biomcp variant oncokb " " therapy and level evidence
    DisGeNET API KeyDISGENET_API_KEYEnables scored DisGeNET sections on gene and disease lookups
    Semantic Scholar API KeyS2_API_KEYImproves reliability for article TLDR, citation, reference, and recommendation helpers

    The first directory build exposes only those three optional settings. Advanced

    CLI-only env vars remain documented in

    API Keys for the general BioMCP CLI path.

    Usage Examples

    Public cross-entity overview

    User prompt: Give me a low-noise overview of BRAF in melanoma.

    Expected tool call: biomcp search all --gene BRAF --disease melanoma --counts-only

    Expected behavior: Returns a cross-entity counts summary that orients the

    next command instead of dumping long detail tables.

    Expected output: Counts-first summary with suggested next commands for the

    highest-yield entity follow-ups.

    Public variant evidence

    User prompt: Summarize ClinVar significance and population frequency for BRAF V600E.

    Expected tool call: biomcp get variant "BRAF V600E" clinvar population

    Expected behavior: Retrieves the focused variant card, ClinVar section, and

    population-frequency data in one read-only call.

    Expected output: Variant summary, ClinVar significance details, and gnomAD

    population frequencies.

    Credentialed OncoKB example

    User prompt: Show OncoKB therapy evidence for BRAF V600E.

    Expected tool call: biomcp variant oncokb "BRAF V600E"

    Expected behavior: Uses ONCOKB_TOKEN when configured and otherwise

    returns helpful guidance about the missing credential.

    Expected output: Therapy and level evidence when ONCOKB_TOKEN is set, or

    a clear setup hint when it is not.

    Credentialed DisGeNET example

    User prompt: Show scored DisGeNET associations for TP53.

    Expected tool call: biomcp get gene TP53 disgenet

    Expected behavior: Uses DISGENET_API_KEY to retrieve the scored

    gene-disease association section.

    Expected output: Ranked disease-association table with evidence counts and

    scores when DISGENET_API_KEY is configured.

    Privacy Policy

    BioMCP does not add telemetry, analytics, or remote log upload. Review the

    full privacy statement at .

    Multi-worker deployment

    BioMCP rate limiting is process-local. For many concurrent workers, run one shared

    Streamable HTTP biomcp serve-http endpoint so all workers share a single

    limiter budget:

    bash
    biomcp serve-http --host 0.0.0.0 --port 8080

    Remote clients should connect to http://:8080/mcp. Lightweight process

    probes are available at GET /health, GET /readyz, and GET /.

    Skills

    BioMCP ships an embedded agent guide instead of a browsable in-binary catalog.

    Use biomcp skill to read the embedded BioMCP guide, then install it into

    your agent directory when you want local copies of the workflow references:

    bash
    biomcp skill
    biomcp skill install ~/.claude --force

    See Skills for supported install targets,

    installed files, and legacy compatibility notes.

    Local study analytics

    study is BioMCP's local analysis family for downloaded cBioPortal-style datasets.

    The 12 remote entity commands query upstream APIs for discovery and detail; study

    commands work on local datasets when you need per-study query, cohort, survival,

    comparison, or co-occurrence workflows.

    Use study download to fetch a dataset into your local study root. Set

    BIOMCP_STUDY_DIR when you want an explicit dataset location for reproducible

    scripts and demos; if it is unset, BioMCP falls back to its default study root.

    bash
    export BIOMCP_STUDY_DIR="$HOME/.local/share/biomcp/studies"
    biomcp study download msk_impact_2017
    biomcp study query --study msk_impact_2017 --gene TP53 --type mutations --chart bar --theme dark --palette wong -o docs/blog/images/tp53-mutation-bar.svg

    See the CLI reference

    for the full study command family and dataset prerequisites.

    Ops

    bash
    biomcp version          # show version and build info
    biomcp health           # inspect API connectivity plus local EMA/cache readiness
    biomcp update           # self-update to latest release
    biomcp update --check   # check for updates without installing
    biomcp uninstall        # remove biomcp from ~/.local/bin

    Support

    • GitHub issues:
    • Troubleshooting: docs/troubleshooting.md
    • Full documentation:

    Documentation

    • Getting Started
    • Search All Workflow
    • BioASQ Benchmark
    • Cross-Entity Pivot Guide
    • Privacy Policy
    • Source Licensing and Terms
    • Data Sources
    • Quick Reference
    • Troubleshooting

    Citation

    If you use BioMCP in research, cite it via [CITATION.cff](CITATION.cff).

    GitHub also exposes Cite this repository in the repository sidebar when that file is present.

    Data Sources and Licensing

    BioMCP is MIT-licensed. It performs on-demand queries against upstream providers instead of vendoring or mirroring their datasets, but upstream terms govern reuse of retrieved results.

    Some providers are fully open, some BioMCP features require registration or API keys, and some queryable sources still impose notable reuse limits. The two biggest cautions are KEGG, which distinguishes academic and non-academic use, and COSMIC, which BioMCP keeps indirect-only because its licensing model is incompatible with a direct open integration.

    Use Source Licensing and Terms for the per-source breakdown and API Keys for setup steps and registration links.

    License

    MIT

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