BioContextAI Knowledgebase MCP

BioContextAI - Knowledgebase MCP

A Model Context Protocol (MCP) server for biomedical research that provides a standardized connection layer between artificial intelligence systems and biomedical resources. Documentation and usage guides are available at: https://biocontext.ai

Overview

BioContextAI Knowledgebase MCP is an MCP server implementation for common biomedical resources, enabling agentic large language models (LLMs) to retrieve verified information and perform domain-specific tasks. Unlike previous approaches that required custom integration for each resource, BioContextAI KB MCP provides a unified access layer through the Model Context Protocol that enables interoperability between AI systems and domain-specific data sources.

The Knowledgebase MCP is available both as:

An open-source software package for local hosting (see Installation) - Choose this option for Claude Desktop, IDEs or your own agentic systems
A remote server for setup-free integration at https://mcp.biocontext.ai/mcp/ (for testing purposes only, subject to fair use)

Warning

If possible, we encourage you to run BioContextAI Knowledgebase MCP locally to avoid rate limits and ensure the service's availability for applications that rely on remote hosting.

The Knowledgebase MCP is part of the wider BioContextAI project. The BioContextAI Registry catalogues community servers that expose biomedical databases and analysis tools, providing the community with a resource for tool discovery and distribution. The registry index can be found at: https://biocontext.ai/registry.

Implemented Tools

BioContextAI Knowledgebase MCP exposes a number of external biomedical APIs. You can think of BioContextAI as a browser for your LLM that allows it to find relevant information across these knowledge bases. Please make sure to adhere to the usage limits (e.g., rate limits) of the respective services when using BioContextAI Knowledgebase MCP. If you use data from these services in your research, please make sure to cite both BioContextAI as well as the respective data source/tool.

Warning

The data accessed through these APIs is not covered by the BioContextAI Knowledgebase MCP license. You are responsible for ensuring that your use of the data aligns with permitted practices.

Tools

Antibody Registry - Gene id conversion
bioRxiv/medRxiv - Recent preprint search and metadata access
Ensembl - Gene id conversion
EuropePMC - Literature search and full-text access
Google Scholar - Academic publication and author search (only available for local use due to rate limiting)
InterPro - Protein families, domains, and functional sites classification
KEGG - Pathways, gene and drug-drug interaction database (only available for local use due to licensing restrictions)
OpenTargets - Target-disease associations
PanglaoDB - Single-cell RNA-sequencing cell type markers
PRIDE - Proteomics data repository for mass spectrometry data
Protein Atlas - Protein expression data
Reactome - Pathways database
STRING - Protein-protein interaction networks
AlphaFold DB - Tertiary protein structure predictions

OpenAPI MCP Servers

FastMCP allows for easy conversion of REST endpoints following the OpenAPI specification into MCP servers. We have added code to automatically create such servers based on schemas provided through a configuration file, so that users deploying their own version of BioContextAI can easily extend the list of available tools. The configuration file is located at src/biocontext_kb/openapi/config.yaml. By default, no OpenAPI servers are included, but you can edit the configuration file to add services.

Installation

Local setup with Claude Desktop:

Edit your claude_desktop_config.json file. To find it, click on your name and then “Settings”. Next, click on “Developer” to see “Local MCP servers” and then click on “Edit Config”.

{
  "mcpServers": {
    "biocontext_kb": {
      "command": "uvx", // On Mac, use "brew install uv"
      "args": [
        "biocontext_kb@latest"
      ],
      "env": {
        "UV_PYTHON": "3.12"
      }
    }
  }
}

Warning

Don't forget to restart Claude to apply the changes.

Local setup with uv

Run the server with streamable HTTP and uvicorn:

export MCP_ENVIRONMENT=PRODUCTION && export PORT=8000 && uvx biocontext_kb

Run the server with stdio transport:

export MCP_ENVIRONMENT=DEVELOPMENT && uvx biocontext_kb

Local setup with IDEs

Change the configuration file of your coding agents, e.g., VS Code (.vscode/mcp.json), Cursor (.cursor/mcp.json), or WindSurf (.codeium/windsurf/mcp_config.json):

{
  // VS Code: Use "servers" instead of "mcpServers"
  "mcpServers": {
    "biocontext_kb": {
      // if it doesn't work, replace with the `which uvx` path (installation via Homebrew recommended on macOS)
      "command": "uvx",
      "args": [
        "biocontext_kb@latest"
      ],
    }
  }
}

When using Windows and WSL2 the above config needs to be adapted as follows:

{
  // VS Code: "servers"
  "mcpServers": {
    "biocontext_kb": {
      "command": "wsl",
      "args": [
        "~/.local/bin/uvx", // set to the path to your `uvx`
        "biocontext_kb"
      ]
    }
  }
}

Docker

Clone the latest version of this repository:

git clone https://github.com/biocontext-ai/knowlegebase-mcp.git
cd knowlegebase-mcp

Then build the container, running gunicorn with multiple uvicorn workers:

# Build the docker container
docker build -t biocontext_kb:latest .
docker run -p 127.0.0.1:8000:8000 biocontext_kb:latest

This exposes your MCP server at: http://127.0.0.1:8000/mcp/

Warning

For public deployments, you should disable unnecessary ports and access your MCP server through a reverse proxy, e.g., Nginx or Caddy. You may also want to configure the running user and the directory to have limited rights, use Docker or podman in a rootless setup and take additional security measures like DDOS protection with Cloudflare or fail2ban.

MCP Clients

To develop your own agentic systems that make use of the MCP server, you may want to consider some of the following options:

Preprint & Documentation

You can find our preprint here: https://www.biorxiv.org/content/10.1101/2025.07.21.665729v1.full.pdf.

If our work is useful to your research, please cite it as below.

@misc{kuehlCommunitybasedBiomedicalContext2025,
  title = {Community-Based Biomedical Context to Unlock Agentic Systems},
  author = {Kuehl, Malte and Schaub, Darius P. and Carli, Francesco and Heumos, Lukas and {Fern{\'a}ndez-Zapata}, Camila and Kaiser, Nico and Schaul, Jonathan and Panzer, Ulf and Bonn, Stefan and Lobentanzer, Sebastian and {Saez-Rodriguez}, Julio and Puelles, Victor G.},
  year = {2025},
  month = jul,
  pages = {2025.07.21.665729},
  publisher = {bioRxiv},
  issn = {2692-8205},
  doi = {10.1101/2025.07.21.665729},
  urldate = {2025-07-28},
  abstract = {Large language models (LLMs) face reliability challenges stemming from hallucinations and insufficient access to validated scientific resources. Existing solutions are often fragmented and limited to specific applications, hindering broader adoption and interoperability. Here, we present Biomedical Context for Artificial Intelligence (BioContextAI), an open-source initiative centered on Model Context Protocol (MCP) servers to address these limitations. BioContextAI provides a community-oriented registry for discovering domain-specific MCP servers and a proof-of-concept server implementation that integrates widely-used biomedical knowledgebases. By enabling standardized access to validated scientific knowledge, BioContextAI aims to facilitate the development of composable agentic systems for biomedical research. Together, this work contributes to an emerging ecosystem of community-driven approaches for expanding the capabilities and reliability of biomedical AI systems.},
  archiveprefix = {bioRxiv},
  copyright = {{\copyright} 2025, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution 4.0 International), CC BY 4.0, as described at http://creativecommons.org/licenses/by/4.0/},
}

Further Resources

Project documentation: https://biocontext.ai
API documentationt: https://docs.kb.biocontext.ai/
BioContextAI Registry: https://github.com/biocontext-ai/registry
Chat Interface: https://biocontext.ai/chat

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

Warning

The Apache 2.0 License only applies to the code provided in this repository. For usage limitations and licenses of the individually integrated APIs, users should directly refer to the respective API providers. We provide an overview below.

Data Sources and Licensing

Data Source	License	URL	Notes
AlphaFold (EMBL-EBI)	CC BY 4.0	https://alphafold.ebi.ac.uk/
Antibody Registry (RRIDs)	CC0 (metadata: CC-NC)	https://www.antibodyregistry.org/faq	Commercial reuse restrictions on some metadata
bioRxiv/medRxiv	CC BY 4.0	https://www.biorxiv.org/about/FAQ	Preprint content licenses vary
ClinicalTrials.gov API	Terms of Service	https://clinicaltrials.gov/about-site/terms-conditions	Attribution required
Ensembl	No restrictions*	https://www.ensembl.org/info/about/legal/disclaimer.html	*Some third-party data may have restrictions
EuropePMC	Various/Copyright protected	https://europepmc.org/Copyright	Individual article licenses vary
Google Scholar	Terms of Service	https://scholar.google.com/intl/en/scholar/terms.html	Rate limiting; use responsibly
Grants.gov API	Terms of Service	https://www.grants.gov/api/terms-conditions	Attribution required
Human Protein Atlas	CC BY-SA 4.0	https://www.proteinatlas.org/about/licence
InterPro	CC0 1.0 Universal	https://www.ebi.ac.uk/interpro/	Includes InterPro, Pfam, PRINTS, and SFLD data
KEGG	Proprietary (Free academic use)	https://www.kegg.jp/kegg/legal.html	Commercial services/remote hosting not permitted
Ontology Lookup Service (EMBL-EBI)	Generally CC0/CC BY	https://www.ebi.ac.uk/licencing/	Refer to EMBL-EBI general licensing
Open Targets	CC0 1.0	https://platform-docs.opentargets.org/licence
OpenFDA	CC0 1.0 Universal*	https://open.fda.gov/license/	*Some data may have restrictions
PanglaoDB	Public data	https://panglaodb.se/about.html	All data are public
PRIDE	CC0/CC BY 4.0*	https://www.ebi.ac.uk/pride/markdownpage/license	*CC0 for datasets from June 2018+, CC BY 4.0 for derived resources
Reactome	CC0	https://reactome.org/license
STRING	CC BY 4.0	https://string-db.org/cgi/access?footer_active_subpage=licensing
UniProt	CC BY 4.0	https://www.uniprot.org/help/license

Disclaimer

Users are solely responsible for ensuring compliance with all applicable license terms and conditions when accessing data through this MCP server. The licenses and terms listed above are subject to change, and additional citation requirements may apply for specific datasets or publications. Before using any data for commercial purposes, redistribution, or publication, please review the current license terms directly from each data source. Some data sources may have additional restrictions not fully captured in this summary.

For KEGG data specifically, please note that while academic use is permitted, providing commercial services or remote hosting using KEGG data is not allowed under their proprietary license terms.

Install Server

HTTP connection URL

security – no known vulnerabilities

license - permissive license

quality - confirmed to work

How are these scores calculated?

hybrid server

The server is able to function both locally and remotely, depending on the configuration or use case.

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An MCP server that provides standardized access to biomedical knowledge bases and resources, enabling AI systems to retrieve verified information from sources like bioRxiv, EuropePMC, and various protein/gene databases.

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