biomcp

# BioMCP: Biomedical Model Context Protocol BioMCP is an open source (MIT License) toolkit that empowers AI assistants and agents with specialized biomedical knowledge. Built following the Model Context Protocol (MCP), it connects AI systems to authoritative biomedical data sources, enabling them to answer questions about clinical trials, scientific literature, and genomic variants with precision and depth. [](https://www.youtube.com/watch?v=bKxOWrWUUhM) ## MCPHub Certification BioMCP is certified by [MCPHub](https://mcphub.com/mcp-servers/genomoncology/biomcp). This certification ensures that BioMCP follows best practices for Model Context Protocol implementation and provides reliable biomedical data access. ## Why BioMCP? While Large Language Models have broad general knowledge, they often lack specialized domain-specific information or access to up-to-date resources. BioMCP bridges this gap for biomedicine by: - Providing **structured access** to clinical trials, biomedical literature, and genomic variants - Enabling **natural language queries** to specialized databases without requiring knowledge of their specific syntax - Supporting **biomedical research** workflows through a consistent interface - Functioning as an **MCP server** for AI assistants and agents ## Biomedical Data Sources BioMCP integrates with multiple biomedical data sources: ### Literature Sources - **PubTator3/PubMed** - Peer-reviewed biomedical literature with entity annotations - **bioRxiv/medRxiv** - Preprint servers for biology and health sciences - **Europe PMC** - Open science platform including preprints ### Clinical & Genomic Sources - **ClinicalTrials.gov** - Clinical trial registry and results database - **NCI Clinical Trials Search API** - National Cancer Institute's curated cancer trials database - Advanced search filters (biomarkers, prior therapies, brain metastases) - Organization and intervention databases - Disease vocabulary with synonyms - **BioThings Suite** - Comprehensive biomedical data APIs: - **MyVariant.info** - Consolidated genetic variant annotation - **MyGene.info** - Real-time gene annotations and information - **MyDisease.info** - Disease ontology and synonym information - **MyChem.info** - Drug/chemical annotations and properties - **TCGA/GDC** - The Cancer Genome Atlas for cancer variant data - **1000 Genomes** - Population frequency data via Ensembl - **cBioPortal** - Cancer genomics portal with mutation occurrence data - **OncoKB** - Precision oncology knowledge base for clinical variant interpretation (demo server with BRAF, ROS1, TP53) - Therapeutic implications and FDA-approved treatments - Oncogenicity and mutation effect annotations - Works immediately without authentication ### Regulatory & Safety Sources - **OpenFDA** - FDA regulatory and safety data: - **Drug Adverse Events (FAERS)** - Post-market drug safety reports - **Drug Labels (SPL)** - Official prescribing information - **Device Events (MAUDE)** - Medical device adverse events, with genomic device filtering ## Available MCP Tools BioMCP provides 24 specialized tools for biomedical research: ### Core Tools (3) #### 1. Think Tool (ALWAYS USE FIRST!) **CRITICAL**: The `think` tool MUST be your first step for ANY biomedical research task. ```python # Start analysis with sequential thinking think( thought="Breaking down the query about BRAF mutations in melanoma...", thoughtNumber=1, totalThoughts=3, nextThoughtNeeded=True ) ``` The sequential thinking tool helps: - Break down complex biomedical problems systematically - Plan multi-step research approaches - Track reasoning progress - Ensure comprehensive analysis #### 2. Search Tool The search tool supports two modes: ##### Unified Query Language (Recommended) Use the `query` parameter with structured field syntax for powerful cross-domain searches: ```python # Simple natural language search(query="BRAF melanoma") # Field-specific search search(query="gene:BRAF AND trials.condition:melanoma") # Complex queries search(query="gene:BRAF AND variants.significance:pathogenic AND articles.date:>2023") # Get searchable fields schema search(get_schema=True) # Explain how a query is parsed search(query="gene:BRAF", explain_query=True) ``` **Supported Fields:** - **Cross-domain**: `gene:`, `variant:`, `disease:` - **Trials**: `trials.condition:`, `trials.phase:`, `trials.status:`, `trials.intervention:` - **Articles**: `articles.author:`, `articles.journal:`, `articles.date:` - **Variants**: `variants.significance:`, `variants.rsid:`, `variants.frequency:` ##### Domain-Based Search Use the `domain` parameter with specific filters: ```python # Search articles (includes automatic cBioPortal integration) search(domain="article", genes=["BRAF"], diseases=["melanoma"]) # Search with mutation-specific cBioPortal data search(domain="article", genes=["BRAF