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BioContextAI Knowledgebase MCP server for biomedical agentic AI
Configuration
View docs{
"mcpServers": {
"biocontext-ai-knowledgebase-mcp": {
"url": "https://biocontext-kb.fastmcp.app/mcp",
"headers": {
"UV_PYTHON": "3.12"
}
}
}
}BioContextAI Knowledgebase MCP Server provides a standardized, OpenAPI-compatible interface that lets AI systems query and integrate with biomedical resources. You can run it locally for development or access a remote MCP instance for testing, enabling agentic models to retrieve verified biomedical data and perform domain-specific tasks with consistent behavior.
You connect to the MCP server from your agent or client by pointing it at either the remote MCP endpoint or your local MCP instance. Use the server to query biomedical data sources, retrieve metadata, and perform domain-specific actions through the common Model Context Protocol interface. When you run locally, you avoid remote rate limits and gain consistent availability for development and testing. When you use the remote endpoint, you can prototype quickly without setting up infrastructure.
Prerequisites: you need a supported runtime and a way to start MCP servers locally. The following options are provided for BioContextAI Knowledgebase MCP.
1) Local setup with Claude Desktop. Edit your MCP config to add the server entry for BioContextAI Knowledgebase MCP. The example below shows the configuration you would place in your Claude Desktop local MCP settings.
{
"mcpServers": {
"biocontext_kb": {
"command": "uvx", // On Mac, use "brew install uv"
"args": [
"biocontext_kb@latest"
],
"env": {
"UV_PYTHON": "3.12"
}
}
}
}2) Local setup with uv. Start the server using streamable HTTP and uvicorn.
export MCP_ENVIRONMENT=PRODUCTION && export PORT=8000 && uvx biocontext_kb3) Local setup with IDEs. Configure your coding agent to point at the MCP server using your editor’s MCP settings. For example, in VS Code you would add a server entry under the appropriate MCP settings file with the command and arguments shown in the IDE guidance.
4) Docker. Build and run a container to host the MCP server locally.
# Build the docker container
docker build -t biocontext_kb:latest .
# Run the container, exposing the MCP endpoint
docker run -p 127.0.0.1:8000:8000 biocontext_kb:latestPublic deployments should use a reverse proxy and enforce minimal access. Consider DDOS protection, restricted user permissions, and rootless container execution.
OpenAPI MCP servers can be created from configuration to extend the available tools. You can add new services by editing the configuration file that defines your OpenAPI-based MCP endpoints.
Tools exposed by BioContextAI Knowledgebase MCP include access to antibody registries, literature databases, pathway and protein resources, and more. Each tool represents an external biomedical data source the MCP can query as part of a unified interface.
Security and licensing notes: data sources have their own licenses. Ensure you comply with each source’s terms when using data in research or publication.
Convert gene identifiers using the Antibody Registry data.
Search recent preprints and access metadata.
Convert gene identifiers and query Ensembl data.
Literature search and metadata access from EuropePMC.
Academic publications and author search (local use only).
Protein families, domains, and functional sites classification.
Explore pathways and drug–drug interactions (licensing restrictions may apply).
Target–disease associations across datasets.
Single-cell RNA-seq cell type marker lookup.
Access mass spectrometry proteomics data.
Protein expression data lookup.
Curated biological pathways and annotations.
Protein–protein interaction networks.
Tertiary protein structure predictions.