{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-fhir-mcp","slug":"fhir-mcp","name":"FHIR MCP","type":"mcp","url":"https://github.com/the-momentum/fhir-mcp-server","page_url":"https://unfragile.ai/fhir-mcp","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-fhir-mcp__cap_0","uri":"capability://tool.use.integration.fhir.r4.crud.operations.via.mcp.tools","name":"fhir r4 crud operations via mcp tools","description":"Exposes full Create, Read, Update, Delete operations on FHIR R4 resources (Patient, Observation, Condition, Medication, DocumentReference) through dedicated MCP tool routers that abstract OAuth2 authentication and FHIR API communication. Each resource type has a specialized router that handles resource-specific validation, transformation, and server communication via a centralized FHIR Client service that manages token refresh and HTTP protocol compliance.","intents":["Query patient demographics and clinical history from a FHIR server using natural language","Create or update clinical observations and lab results programmatically through an AI agent","Delete or modify condition records without direct API knowledge","Retrieve medication lists and dosing information for a specific patient"],"best_for":["Healthcare AI agents that need to read/write clinical data","Clinical decision support systems integrating with EHR backends","Teams building FHIR-compliant healthcare applications without direct API expertise"],"limitations":["Limited to FHIR R4 standard — non-standard extensions may require custom tool implementation","OAuth2 token refresh adds latency (~500ms per expired token cycle)","No built-in transaction support — multi-resource operations require sequential calls","Resource validation happens at FHIR server level, not client-side, increasing round-trip failures"],"requires":["Python 3.13+","FHIR server endpoint (e.g., Medplum, HAPI FHIR) with OAuth2 support","OAuth2 client credentials (client_id, client_secret)","MCP-compatible client (Claude Desktop, custom MCP consumer)"],"input_types":["natural language queries","FHIR resource JSON objects","patient identifiers (MRN, ID)","search parameters (date ranges, codes)"],"output_types":["FHIR R4 resource JSON","structured patient records","observation/lab result arrays","operation success/failure status"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-fhir-mcp__cap_1","uri":"capability://memory.knowledge.semantic.document.ingestion.and.rag.powered.retrieval","name":"semantic document ingestion and rag-powered retrieval","description":"Ingests clinical documents (PDFs, text) into a vector database (Pinecone) using semantic chunking and embeddings, enabling AI agents to perform semantic search across document collections without full-text indexing. The system chunks documents into semantic units, generates embeddings via an embedding service, stores vectors with metadata in Pinecone, and retrieves relevant chunks based on cosine similarity to natural language queries, with optional re-ranking for relevance.","intents":["Search across patient medical records and clinical notes using natural language queries","Retrieve relevant sections from discharge summaries or clinical documentation","Build context for AI agents by finding semantically similar documents without keyword matching","Ingest new clinical documents and make them immediately searchable"],"best_for":["Healthcare organizations with large unstructured document repositories","AI agents that need context-aware clinical decision support","Teams building document-aware clinical chatbots or copilots"],"limitations":["Requires Pinecone API key and vector database subscription — adds operational cost","Semantic chunking may split important clinical context across chunks, reducing retrieval quality","Embedding generation latency (~100-500ms per document depending on size) impacts ingestion speed","No built-in document versioning — updating documents requires manual deletion and re-ingestion","Vector similarity search cannot guarantee clinical accuracy — requires human validation for critical decisions"],"requires":["Python 3.13+","Pinecone API key and active vector database index","Embedding model access (OpenAI, Hugging Face, or local model)","Document processing library (PyPDF2 or similar for PDF extraction)"],"input_types":["PDF documents","plain text clinical notes","natural language search queries","document metadata (patient ID, date, type)"],"output_types":["vector embeddings","ranked document chunks with similarity scores","metadata-enriched search results","ingestion status reports"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-fhir-mcp__cap_10","uri":"capability://safety.moderation.error.handling.and.graceful.degradation","name":"error handling and graceful degradation","description":"Implements comprehensive error handling across MCP tools, service layers, and external API calls with specific error types (authentication failures, FHIR validation errors, vector database timeouts) and graceful degradation strategies. The system returns detailed error messages to MCP clients, logs errors with context for debugging, retries transient failures (network timeouts, rate limits), and falls back to alternative implementations when primary services are unavailable.","intents":["Handle FHIR server authentication failures without crashing the MCP server","Retry failed API calls to Pinecone or LOINC with exponential backoff","Provide meaningful error messages to AI agents for decision-making","Log errors with sufficient context for debugging production issues"],"best_for":["Production healthcare deployments requiring high availability","Systems integrating with unreliable external services","Teams building AI agents that need to handle service failures gracefully"],"limitations":["Retry logic adds latency for failed requests (~1-5 seconds with exponential backoff)","Graceful degradation may return partial results — AI agents must handle incomplete data","Error logging may expose sensitive patient data if not carefully filtered","Fallback implementations may have reduced functionality compared to primary services"],"requires":["Python 3.13+","Structured logging framework (logging module with JSON formatting)","Retry library (tenacity or similar) for exponential backoff"],"input_types":["exception objects from external APIs","HTTP error responses","timeout events"],"output_types":["error messages with error codes","retry status and backoff timing","fallback results or degraded functionality"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-fhir-mcp__cap_2","uri":"capability://data.processing.analysis.healthcare.terminology.lookup.and.medical.code.validation","name":"healthcare terminology lookup and medical code validation","description":"Provides standardized medical code lookup and validation through integration with the LOINC API, enabling AI agents to resolve clinical terminology (lab codes, observation types, medication codes) to standard healthcare vocabularies. The system queries LOINC for code definitions, descriptions, and related codes, with caching to reduce API calls and support for code-to-description and description-to-code lookups.","intents":["Resolve lab test codes (e.g., 'glucose') to LOINC codes for standardized clinical communication","Validate medication codes against standard healthcare vocabularies","Find related clinical codes for a given observation type","Translate between different medical code systems (ICD-10, SNOMED, LOINC)"],"best_for":["Clinical decision support systems that need standardized code resolution","Healthcare AI agents that must communicate with EHR systems using standard vocabularies","Teams building interoperable healthcare applications"],"limitations":["LOINC API rate limiting may throttle high-volume code lookups","Caching strategy is in-memory only — no persistent cache across server restarts","Limited to LOINC vocabulary — ICD-10, SNOMED, RxNorm require separate integrations","Code descriptions may be outdated if LOINC database is not regularly synchronized"],"requires":["Python 3.13+","LOINC API access (free tier available)","Network connectivity to LOINC servers"],"input_types":["LOINC codes (e.g., '2345-7')","clinical term descriptions (e.g., 'glucose')","code system identifiers"],"output_types":["LOINC code definitions","code descriptions and units","related code suggestions","validation status (valid/invalid)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-fhir-mcp__cap_3","uri":"capability://safety.moderation.oauth2.authentication.and.token.lifecycle.management","name":"oauth2 authentication and token lifecycle management","description":"Manages OAuth2 authentication flows and token lifecycle (acquisition, refresh, expiration handling) for FHIR server communication through a centralized FHIR Client service. The system handles client credentials grant flow, automatic token refresh before expiration, and credential rotation, abstracting authentication complexity from individual MCP tools so they can focus on business logic.","intents":["Authenticate with FHIR servers that require OAuth2 without exposing credentials in MCP tools","Automatically refresh expired tokens to maintain uninterrupted FHIR API access","Rotate credentials securely without restarting the MCP server","Support multiple FHIR server endpoints with different OAuth2 configurations"],"best_for":["Production healthcare deployments requiring secure credential management","Multi-tenant systems serving multiple FHIR servers","Teams building long-running AI agents that need persistent FHIR access"],"limitations":["Token refresh adds ~500ms latency per expired token cycle","In-memory token storage is lost on server restart — requires re-authentication","No support for OAuth2 authorization code flow (user-delegated access) — only client credentials","Credential rotation requires configuration update and potential downtime"],"requires":["Python 3.13+","OAuth2 client credentials (client_id, client_secret) from FHIR server","FHIR server with OAuth2 token endpoint","Secure credential storage (environment variables or encrypted config)"],"input_types":["OAuth2 client credentials","FHIR server token endpoint URL","token scope requirements"],"output_types":["OAuth2 access tokens","token expiration timestamps","authentication status"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-fhir-mcp__cap_4","uri":"capability://tool.use.integration.mcp.tool.orchestration.and.fastmcp.framework.integration","name":"mcp tool orchestration and fastmcp framework integration","description":"Implements the Model Context Protocol (MCP) server using the FastMCP framework, which handles MCP protocol compliance, tool registration, and request routing. The system mounts specialized routers (patient_router, observation_router, condition_router, document_reference_router, generic_router) onto a FastMCP instance, enabling MCP-compatible clients (Claude Desktop, custom consumers) to discover and invoke tools through a standardized protocol with automatic schema validation and error handling.","intents":["Expose FHIR operations as discoverable MCP tools to Claude Desktop and other MCP clients","Route natural language requests from AI agents to appropriate FHIR resource handlers","Validate tool inputs against MCP schemas before execution","Handle MCP protocol negotiation and tool discovery automatically"],"best_for":["Teams building AI agents that need standardized tool interfaces","Healthcare organizations integrating with Claude or other MCP-compatible LLMs","Developers who want to expose FHIR operations without building custom REST APIs"],"limitations":["FastMCP framework abstractions add ~100-200ms latency per tool invocation","Tool schema validation happens at MCP protocol level, not business logic level","No built-in rate limiting or quota management — requires external middleware","MCP protocol is still evolving — breaking changes may require updates"],"requires":["Python 3.13+","FastMCP framework (installed via uv)","MCP-compatible client (Claude Desktop 0.1.0+, or custom MCP consumer)","Network connectivity between client and MCP server"],"input_types":["MCP tool invocation requests","tool parameters as JSON","MCP protocol messages"],"output_types":["MCP tool results","error messages with MCP error codes","tool discovery metadata"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-fhir-mcp__cap_5","uri":"capability://search.retrieval.multi.resource.fhir.search.and.filtering","name":"multi-resource fhir search and filtering","description":"Provides search and filtering capabilities across FHIR resources using FHIR search parameters (date ranges, codes, patient identifiers, status filters) through a generic_router fallback that handles any FHIR resource type. The system translates natural language search intents into FHIR search parameter queries, executes searches against the FHIR server, and returns paginated results with metadata, supporting complex filters without requiring users to know FHIR query syntax.","intents":["Find all observations for a patient within a date range using natural language","Search for conditions with specific status (active, resolved) across patient populations","Retrieve medications by code or description without FHIR syntax knowledge","Filter results by multiple criteria (patient ID, date, code) in a single query"],"best_for":["Clinical researchers querying patient cohorts","Healthcare AI agents that need flexible search without FHIR expertise","Teams building clinical dashboards or reporting systems"],"limitations":["Search performance depends on FHIR server indexing — complex queries may timeout","FHIR search parameter support varies by server implementation","No full-text search across unstructured fields — requires RAG for document search","Pagination requires manual offset/limit management for large result sets"],"requires":["Python 3.13+","FHIR server with search parameter support","OAuth2 credentials for FHIR server access"],"input_types":["natural language search queries","FHIR search parameters","patient identifiers","date ranges","clinical codes"],"output_types":["FHIR resource arrays","search result metadata (total count, page info)","filtered resource lists"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-fhir-mcp__cap_6","uri":"capability://safety.moderation.configuration.management.with.encrypted.credential.storage","name":"configuration management with encrypted credential storage","description":"Manages application configuration (FHIR server URLs, API keys, Pinecone credentials) through environment-based configuration with optional encryption for sensitive values. The system loads configuration from environment variables or encrypted config files, validates required settings at startup, and provides utilities for encrypting/decrypting credentials without exposing them in logs or version control.","intents":["Store FHIR server credentials securely without hardcoding in source code","Manage different configurations for development, staging, and production environments","Rotate API keys and credentials without code changes","Encrypt sensitive configuration values at rest"],"best_for":["Production healthcare deployments with security compliance requirements","Multi-environment deployments (dev/staging/prod)","Teams using CI/CD pipelines that need secure credential injection"],"limitations":["Encryption key management requires separate secure storage (e.g., AWS Secrets Manager)","Environment variable length limits may constrain large configuration files","Configuration validation happens at startup only — runtime changes require restart","No built-in audit logging for configuration access"],"requires":["Python 3.13+","Environment variable support (Docker, Kubernetes, or shell)","Encryption key for sensitive values (optional but recommended)"],"input_types":["environment variables","encrypted configuration files","plaintext credentials for encryption"],"output_types":["validated configuration objects","encrypted credential strings","configuration validation status"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-fhir-mcp__cap_7","uri":"capability://automation.workflow.docker.containerization.and.orchestration","name":"docker containerization and orchestration","description":"Provides Docker and Docker Compose configurations for containerized deployment of the FHIR MCP server, enabling reproducible environments across development, testing, and production. The system includes a Dockerfile with Python 3.13+ runtime, dependency installation via uv, and Docker Compose configuration for multi-container orchestration with FHIR server, vector database, and MCP server services.","intents":["Deploy FHIR MCP server in containerized environments (Kubernetes, Docker Swarm)","Ensure consistent runtime environment across development and production","Orchestrate multiple services (FHIR server, Pinecone, MCP server) with Docker Compose","Scale MCP server instances horizontally in container orchestration platforms"],"best_for":["Teams deploying to Kubernetes or container orchestration platforms","Healthcare organizations with containerization requirements","CI/CD pipelines that need reproducible build artifacts"],"limitations":["Docker image size may be large due to Python dependencies — requires optimization for edge deployments","In-memory token storage is lost on container restart — requires external token cache","Docker Compose is suitable for development only — production requires Kubernetes or similar","Container networking requires careful configuration for FHIR server and Pinecone connectivity"],"requires":["Docker 20.10+","Docker Compose 2.0+ (for multi-container orchestration)","Container registry access (Docker Hub, ECR, etc.) for image storage"],"input_types":["Dockerfile","docker-compose.yml","environment variables for container configuration"],"output_types":["Docker images","running containers","container logs and metrics"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-fhir-mcp__cap_8","uri":"capability://automation.workflow.layered.service.architecture.with.separation.of.concerns","name":"layered service architecture with separation of concerns","description":"Implements a three-layer architecture separating MCP protocol handling (FastMCP routers), business logic (FHIR/RAG services), and external service integration (FHIR Client, Pinecone, LOINC API). Each layer has distinct responsibilities: routers handle tool invocation and schema validation, services implement domain logic and error handling, and integration clients manage external API communication with retry logic and connection pooling.","intents":["Maintain clean separation between MCP protocol and healthcare business logic","Enable independent testing of FHIR operations without MCP framework","Swap external service implementations (e.g., different vector databases) without changing MCP tools","Reuse service layer logic across multiple client types (MCP, REST, CLI)"],"best_for":["Teams building maintainable healthcare systems with multiple client interfaces","Organizations that need to test business logic independently from protocol handling","Projects planning to support multiple FHIR servers or vector databases"],"limitations":["Layered architecture adds abstraction overhead (~50-100ms per request)","Service layer must handle all error cases — incomplete error handling propagates to MCP layer","Testing requires mocking multiple service dependencies","Documentation must clearly define service boundaries to prevent tight coupling"],"requires":["Python 3.13+","Understanding of service-oriented architecture patterns","Dependency injection framework or manual service instantiation"],"input_types":["MCP tool requests","service method calls","external API responses"],"output_types":["MCP tool results","service responses","error objects with context"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-fhir-mcp__cap_9","uri":"capability://data.processing.analysis.batch.document.processing.and.semantic.chunking","name":"batch document processing and semantic chunking","description":"Processes multiple clinical documents in batch mode, extracting text from PDFs, splitting into semantic chunks based on clinical structure (sections, paragraphs), generating embeddings for each chunk, and storing in Pinecone with metadata. The system handles document parsing errors gracefully, tracks ingestion status per document, and supports resumable batch processing for large document collections.","intents":["Ingest a patient's entire medical record (multiple documents) into the RAG system","Process bulk document uploads from EHR exports without manual intervention","Track ingestion progress and handle failures for large document batches","Update document collections incrementally without re-processing unchanged documents"],"best_for":["Healthcare organizations migrating legacy documents to AI-searchable systems","Bulk data import operations from EHR systems","Teams building document-aware clinical AI systems"],"limitations":["Semantic chunking may split important clinical context — requires validation","Batch processing is sequential — no parallel document processing for speed","PDF extraction quality varies by document format — scanned PDFs require OCR","No deduplication — duplicate documents are processed multiple times"],"requires":["Python 3.13+","PDF processing library (PyPDF2 or similar)","Embedding model access","Pinecone API key and vector database"],"input_types":["PDF files","text files","document metadata (patient ID, date, type)","batch configuration (chunk size, overlap)"],"output_types":["ingestion status per document","vector IDs in Pinecone","error logs with document references","batch completion report"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":28,"verified":false,"data_access_risk":"high","permissions":["Python 3.13+","FHIR server endpoint (e.g., Medplum, HAPI FHIR) with OAuth2 support","OAuth2 client credentials (client_id, client_secret)","MCP-compatible client (Claude Desktop, custom MCP consumer)","Pinecone API key and active vector database index","Embedding model access (OpenAI, Hugging Face, or local model)","Document processing library (PyPDF2 or similar for PDF extraction)","Structured logging framework (logging module with JSON formatting)","Retry library (tenacity or similar) for exponential backoff","LOINC API access (free tier available)"],"failure_modes":["Limited to FHIR R4 standard — non-standard extensions may require custom tool implementation","OAuth2 token refresh adds latency (~500ms per expired token cycle)","No built-in transaction support — multi-resource operations require sequential calls","Resource validation happens at FHIR server level, not client-side, increasing round-trip failures","Requires Pinecone API key and vector database subscription — adds operational cost","Semantic chunking may split important clinical context across chunks, reducing retrieval quality","Embedding generation latency (~100-500ms per document depending on size) impacts ingestion speed","No built-in document versioning — updating documents requires manual deletion and re-ingestion","Vector similarity search cannot guarantee clinical accuracy — requires human validation for critical decisions","Retry logic adds latency for failed requests (~1-5 seconds with exponential backoff)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.32,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.6,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:03.039Z","last_scraped_at":"2026-05-03T14:00:15.503Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=fhir-mcp","compare_url":"https://unfragile.ai/compare?artifact=fhir-mcp"}},"signature":"nRQ7weXQa0rSWU+UY1VNZOyCfSIBB0hXSYgKhDCjfJ/5McfNiFAaqr5abwYynS1EvvizQ8i9hdSa6/H0ytz6AQ==","signedAt":"2026-06-21T13:44:49.889Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/fhir-mcp","artifact":"https://unfragile.ai/fhir-mcp","verify":"https://unfragile.ai/api/v1/verify?slug=fhir-mcp","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}