{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-fhir","slug":"fhir","name":"FHIR","type":"mcp","url":"https://github.com/wso2/fhir-mcp-server/","page_url":"https://unfragile.ai/fhir","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-fhir__cap_0","uri":"capability://tool.use.integration.fhir.resource.search.with.natural.language.translation","name":"fhir resource search with natural language translation","description":"Translates natural language queries into FHIR search operations by mapping user intent to FHIR search parameters, leveraging the fhirpy client library (v2.0.15+) to execute parameterized searches against FHIR R4 servers. The server implements a tool-based interface where queries like 'Get allergy history for patient 53373' are decomposed into structured FHIR search calls with resource type, search parameters, and filters, returning paginated results with full FHIR resource bundles.","intents":["I need to query patient allergies, immunizations, or care plans without writing FHIR search syntax","I want to retrieve filtered FHIR resources based on natural language criteria","I need to integrate FHIR search into an LLM agent without managing FHIR API details"],"best_for":["AI/LLM applications building healthcare data retrieval agents","Healthcare developers integrating FHIR servers with conversational interfaces","Teams building clinical decision support systems with natural language input"],"limitations":["Search parameter mapping is tool-defined; complex FHIR queries with nested parameters may require explicit parameter specification","No built-in query optimization or caching — each search hits the FHIR server directly","Pagination handled by underlying fhirpy client; large result sets require manual iteration","Limited to FHIR R4 resource types supported by the fhirpy library"],"requires":["Python 3.9+","Active FHIR R4 server endpoint with network accessibility","Valid OAuth 2.0 credentials (client_id, client_secret) for FHIR server access","fhirpy>=2.0.15 installed as dependency"],"input_types":["natural language query string","FHIR resource type identifier","search parameters (key-value pairs)"],"output_types":["FHIR Bundle resource (JSON)","FHIR resource collection (Patient, Observation, Medication, etc.)","structured error responses with FHIR OperationOutcome"],"categories":["tool-use-integration","search-retrieval","healthcare-data"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-fhir__cap_1","uri":"capability://tool.use.integration.fhir.resource.crud.operations.with.oauth.secured.access","name":"fhir resource crud operations with oauth-secured access","description":"Exposes create, read, update, and delete operations for FHIR resources through MCP tools (create_fhir, read_fhir, update_fhir, delete_fhir) with automatic OAuth 2.0 client credential flow for FHIR server authentication. Each operation validates input against FHIR R4 schemas using Pydantic models, constructs appropriate HTTP requests via fhirpy, and returns typed FHIR resources or error responses with full audit trail support through OAuth token tracking.","intents":["I need to programmatically create or update patient records in a FHIR server from an AI agent","I want to read specific FHIR resources by ID without managing OAuth authentication manually","I need to delete or archive FHIR resources through a secure, auditable interface"],"best_for":["Healthcare data integration pipelines automating record creation/updates","Clinical AI systems that need to modify FHIR data with audit compliance","Multi-tenant SaaS platforms managing FHIR servers for multiple healthcare organizations"],"limitations":["OAuth token refresh is automatic but adds ~100-200ms latency on first operation per session","No built-in transaction support for multi-resource atomic updates — each operation is independent","Validation errors return FHIR OperationOutcome but don't provide schema-level hints for malformed resources","Delete operations are permanent; no soft-delete or archival patterns built-in"],"requires":["Python 3.9+","FHIR server supporting OAuth 2.0 with PKCE flow","Client credentials (client_id, client_secret) with appropriate FHIR scopes (patient/*.write, patient/*.read, etc.)","fhirpy>=2.0.15 and aiohttp>=3.12.13 for async HTTP operations"],"input_types":["FHIR resource JSON (for create/update operations)","resource ID string (for read/delete operations)","FHIR resource type identifier"],"output_types":["FHIR resource (Patient, Observation, Medication, etc.) as JSON","FHIR OperationOutcome for errors","HTTP status codes (201 Created, 200 OK, 404 Not Found, 400 Bad Request)"],"categories":["tool-use-integration","data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-fhir__cap_2","uri":"capability://tool.use.integration.fhir.capability.statement.retrieval.and.server.metadata.discovery","name":"fhir capability statement retrieval and server metadata discovery","description":"Fetches and parses the FHIR server's CapabilityStatement resource (via GET /metadata endpoint) to expose supported resource types, search parameters, operations, and conformance details. The server caches the capability statement and exposes it through the get_fhir_capabilities MCP tool, enabling AI agents to dynamically discover what FHIR operations are available before attempting queries, with automatic fallback to R4 defaults if the server doesn't expose full metadata.","intents":["I need to discover what FHIR resources and search parameters a server supports before querying","I want to validate that a FHIR server supports a specific operation before an AI agent attempts it","I need to expose FHIR server capabilities to an LLM so it can generate appropriate queries"],"best_for":["Multi-FHIR-server environments where capabilities vary by implementation","AI agents that need to adapt queries based on server capabilities","Healthcare integration platforms building dynamic FHIR client libraries"],"limitations":["Capability statement is cached at server startup; changes to FHIR server capabilities require MCP server restart","Some FHIR servers return incomplete or non-standard CapabilityStatements; fallback to R4 defaults may mask actual limitations","No real-time capability validation — if a server removes support for a resource type, the cache won't reflect it until refresh","Parsing is FHIR R4-specific; FHIR STU3 or DSTU2 servers may return incompatible metadata"],"requires":["Python 3.9+","FHIR R4 server with accessible /metadata endpoint","Valid OAuth credentials for metadata access (typically read-only scope)","fhirpy>=2.0.15 for CapabilityStatement parsing"],"input_types":["FHIR server base URL (implicit from OAuth configuration)"],"output_types":["FHIR CapabilityStatement resource (JSON)","structured metadata object with supported resources, search parameters, operations"],"categories":["tool-use-integration","memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-fhir__cap_3","uri":"capability://tool.use.integration.dual.role.oauth.2.0.authentication.with.pkce.flow","name":"dual-role oauth 2.0 authentication with pkce flow","description":"Implements a bidirectional OAuth 2.0 system where the MCP server acts as both an OAuth server (authenticating MCP clients via PKCE) and an OAuth client (accessing external FHIR servers). The server manages token lifecycle including acquisition, refresh, and expiration using Pydantic-validated configuration, with support for multiple FHIR server endpoints and per-client credential isolation. PKCE (Proof Key for Code Exchange) is enforced for all flows to prevent authorization code interception attacks.","intents":["I need to securely authenticate MCP clients connecting to the FHIR MCP server","I want to manage OAuth tokens for multiple FHIR servers without exposing credentials to AI agents","I need to enforce per-client access control and audit which client accessed which FHIR resources"],"best_for":["Multi-tenant healthcare platforms where each client needs isolated FHIR server access","Enterprise deployments requiring OAuth audit trails and token revocation","Healthcare organizations integrating multiple FHIR servers with centralized authentication"],"limitations":["Token refresh adds ~100-200ms latency per operation if token is expired","No built-in token revocation endpoint — revoked tokens remain valid until expiration","PKCE implementation assumes clients can generate cryptographically secure code_challenge; weak implementations may be vulnerable","OAuth server role requires HTTPS in production; HTTP-only deployments are insecure"],"requires":["Python 3.9+","OAuth 2.0 provider for FHIR servers (e.g., Keycloak, Azure AD, custom OAuth server)","Client credentials (client_id, client_secret) for each FHIR server","HTTPS endpoint for OAuth server role (required for production security)","aiohttp>=3.12.13 for async OAuth token exchange"],"input_types":["OAuth authorization code (from client redirect)","client_id and client_secret (for token exchange)","code_challenge and code_verifier (PKCE parameters)"],"output_types":["OAuth access token (JWT or opaque)","refresh token (if supported by FHIR server)","token expiration timestamp","OAuth error responses (invalid_grant, invalid_client, etc.)"],"categories":["tool-use-integration","safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-fhir__cap_4","uri":"capability://tool.use.integration.mcp.protocol.bridging.with.flexible.transport.http.and.stdio","name":"mcp protocol bridging with flexible transport (http and stdio)","description":"Exposes FHIR capabilities through the Model Context Protocol (MCP) using both HTTP and STDIO transport mechanisms, enabling integration with diverse AI tool architectures. The server implements the MCP tool interface specification, where each FHIR operation (search, read, create, etc.) is registered as an MCP tool with typed input schemas and output specifications. Transport selection is configurable at startup, allowing deployment as either a long-running HTTP server (for cloud/container environments) or a STDIO process (for local AI tool integration).","intents":["I need to integrate FHIR capabilities into Claude, ChatGPT, or other MCP-compatible AI tools","I want to deploy a FHIR server bridge that works with both cloud-based and local AI agents","I need to expose FHIR operations as callable tools in an LLM agent framework"],"best_for":["AI/LLM platforms adopting MCP as a standard integration protocol","Healthcare teams building AI agents with tool-calling capabilities","Developers integrating FHIR with Claude, ChatGPT, or open-source LLM frameworks"],"limitations":["HTTP transport requires network connectivity and HTTPS in production; adds ~50-100ms latency per request","STDIO transport is single-process; concurrent requests require multiple server instances","MCP tool schemas are static at server startup; dynamic tool registration requires server restart","No built-in rate limiting or request queuing — high-volume AI agent requests may overwhelm the server"],"requires":["Python 3.9+","mcp[cli]>=1.9.1 for MCP framework","aiohttp>=3.12.13 for HTTP transport (if using HTTP mode)","MCP-compatible AI tool or client (Claude, ChatGPT with MCP support, or custom MCP client)"],"input_types":["MCP tool call requests (JSON with tool name and input parameters)","HTTP POST requests (for HTTP transport)","STDIO input stream (for STDIO transport)"],"output_types":["MCP tool result responses (JSON with output data or error)","HTTP responses (JSON with FHIR resources or errors)","STDIO output stream (for STDIO transport)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-fhir__cap_5","uri":"capability://automation.workflow.pydantic.based.configuration.validation.and.environment.management","name":"pydantic-based configuration validation and environment management","description":"Uses Pydantic v2 models to validate and type-check all configuration parameters (OAuth credentials, FHIR server URLs, transport settings) at server startup, with support for environment variable injection and .env file loading. Configuration is immutable after validation, preventing runtime configuration drift. The system enforces required fields, type coercion, and custom validators (e.g., URL format validation, credential format checks) before the server initializes, failing fast with detailed error messages if configuration is invalid.","intents":["I need to validate FHIR server credentials and URLs before the server starts","I want to inject OAuth credentials via environment variables without hardcoding them","I need to ensure configuration consistency across development, staging, and production deployments"],"best_for":["DevOps teams deploying FHIR MCP servers in containerized environments","Healthcare organizations requiring configuration audit trails and validation","Development teams building multi-environment FHIR integrations"],"limitations":["Configuration is immutable after startup; changes require server restart","Pydantic validation adds ~10-50ms overhead at startup; large configuration files may increase startup time","Environment variable injection doesn't support complex nested structures; deeply nested configs require JSON files","No built-in secrets rotation; credentials must be manually updated and server restarted"],"requires":["Python 3.9+","Pydantic>=2.0 (included in dependencies)","Environment variables or .env file with FHIR_SERVER_URL, OAUTH_CLIENT_ID, OAUTH_CLIENT_SECRET, etc."],"input_types":["environment variables (FHIR_SERVER_URL, OAUTH_CLIENT_ID, etc.)",".env file (key=value format)","configuration JSON/YAML (if supported)"],"output_types":["validated configuration object (Pydantic model)","configuration validation errors with field-level details"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-fhir__cap_6","uri":"capability://automation.workflow.asynchronous.fhir.operations.with.aiohttp.based.http.client","name":"asynchronous fhir operations with aiohttp-based http client","description":"Implements all FHIR operations (search, read, create, update, delete) using Python's asyncio framework with aiohttp>=3.12.13 for non-blocking HTTP requests. The server processes multiple concurrent FHIR queries without blocking, enabling high-throughput scenarios where multiple AI agents query the same FHIR server simultaneously. Connection pooling and keep-alive are configured automatically, reducing latency for repeated requests to the same FHIR server.","intents":["I need to handle multiple concurrent FHIR queries from different AI agents without blocking","I want to minimize latency for high-frequency FHIR operations in production deployments","I need to efficiently manage HTTP connections to FHIR servers with connection pooling"],"best_for":["High-throughput healthcare AI systems with multiple concurrent agents","Cloud deployments where resource efficiency is critical","Real-time clinical decision support systems requiring low-latency FHIR access"],"limitations":["Asyncio requires Python 3.9+; older Python versions are not supported","Debugging async code is more complex than synchronous code; stack traces may be harder to interpret","Connection pool size is fixed at server startup; high-concurrency scenarios may require tuning","Timeouts are global; individual FHIR operations cannot have custom timeout values"],"requires":["Python 3.9+","aiohttp>=3.12.13","asyncio event loop (built into Python)"],"input_types":["FHIR operation parameters (search, read, create, etc.)","HTTP headers (Authorization, Content-Type, etc.)"],"output_types":["FHIR resources (async/await compatible)","HTTP status codes and error responses"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-fhir__cap_7","uri":"capability://data.processing.analysis.fhir.r4.resource.type.abstraction.via.fhirpy.client.library","name":"fhir r4 resource type abstraction via fhirpy client library","description":"Abstracts FHIR R4 resource manipulation through the fhirpy>=2.0.15 client library, which provides object-oriented interfaces for all FHIR resource types (Patient, Observation, Medication, etc.). The server leverages fhirpy's built-in FHIR validation, resource serialization, and search parameter mapping, eliminating the need for manual JSON construction or FHIR spec knowledge. Resource operations are type-safe through fhirpy's resource classes, reducing serialization errors and enabling IDE autocomplete.","intents":["I need to work with FHIR resources without manually constructing JSON or understanding FHIR spec details","I want type-safe FHIR resource manipulation with validation built-in","I need to support multiple FHIR resource types (Patient, Observation, Medication, etc.) with a unified interface"],"best_for":["Healthcare developers unfamiliar with FHIR specification details","Teams building FHIR integrations that need rapid development","AI systems that need to work with diverse FHIR resource types"],"limitations":["fhirpy is FHIR R4-specific; FHIR STU3 or DSTU2 servers require a different client library","Custom FHIR extensions are supported but require manual JSON manipulation","fhirpy's search parameter mapping may not cover all server-specific search parameters","Performance overhead of object-oriented abstraction (~5-10% slower than raw HTTP calls)"],"requires":["Python 3.9+","fhirpy>=2.0.15","FHIR R4 server"],"input_types":["FHIR resource objects (Patient, Observation, etc.)","resource IDs (strings)","search parameters (key-value pairs)"],"output_types":["FHIR resource objects (typed)","FHIR Bundle objects (for search results)","validation errors with field-level details"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-fhir__cap_8","uri":"capability://safety.moderation.error.handling.and.fhir.operationoutcome.responses","name":"error handling and fhir operationoutcome responses","description":"Implements comprehensive error handling that catches FHIR server errors, OAuth failures, and validation errors, translating them into standardized FHIR OperationOutcome resources. The server distinguishes between client errors (400 Bad Request), authentication errors (401 Unauthorized), authorization errors (403 Forbidden), and server errors (500 Internal Server Error), providing detailed error messages and issue codes that conform to FHIR specification. Errors are logged with full context (request parameters, OAuth token info, FHIR server response) for debugging and audit trails.","intents":["I need to understand why a FHIR operation failed with a standardized error format","I want to distinguish between client errors, authentication failures, and server errors","I need to log and audit all FHIR operation failures for compliance and debugging"],"best_for":["Healthcare systems requiring detailed error logging and audit trails","AI agents that need to handle FHIR errors gracefully and retry appropriately","Development teams debugging FHIR integration issues"],"limitations":["FHIR OperationOutcome format is verbose; error messages may be difficult to parse for non-FHIR developers","Some FHIR servers return non-standard error responses; error parsing may fail for non-compliant servers","Error logging includes sensitive information (OAuth tokens, patient IDs); logs must be secured","No built-in error recovery or automatic retry logic; clients must implement retry strategies"],"requires":["Python 3.9+","fhirpy>=2.0.15 for OperationOutcome parsing"],"input_types":["FHIR server error responses (HTTP status codes + response body)","OAuth error responses","validation errors from Pydantic"],"output_types":["FHIR OperationOutcome resource (JSON)","structured error object with issue code, severity, and diagnostic message","HTTP status codes (400, 401, 403, 500, etc.)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":30,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","Active FHIR R4 server endpoint with network accessibility","Valid OAuth 2.0 credentials (client_id, client_secret) for FHIR server access","fhirpy>=2.0.15 installed as dependency","FHIR server supporting OAuth 2.0 with PKCE flow","Client credentials (client_id, client_secret) with appropriate FHIR scopes (patient/*.write, patient/*.read, etc.)","fhirpy>=2.0.15 and aiohttp>=3.12.13 for async HTTP operations","FHIR R4 server with accessible /metadata endpoint","Valid OAuth credentials for metadata access (typically read-only scope)","fhirpy>=2.0.15 for CapabilityStatement parsing"],"failure_modes":["Search parameter mapping is tool-defined; complex FHIR queries with nested parameters may require explicit parameter specification","No built-in query optimization or caching — each search hits the FHIR server directly","Pagination handled by underlying fhirpy client; large result sets require manual iteration","Limited to FHIR R4 resource types supported by the fhirpy library","OAuth token refresh is automatic but adds ~100-200ms latency on first operation per session","No built-in transaction support for multi-resource atomic updates — each operation is independent","Validation errors return FHIR OperationOutcome but don't provide schema-level hints for malformed resources","Delete operations are permanent; no soft-delete or archival patterns built-in","Capability statement is cached at server startup; changes to FHIR server capabilities require MCP server restart","Some FHIR servers return incomplete or non-standard CapabilityStatements; fallback to R4 defaults may mask actual limitations","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.43,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"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","compare_url":"https://unfragile.ai/compare?artifact=fhir"}},"signature":"FHNTorAlrBcDHV8ACS389PJCDDAZdt3hHS1BtRePi2MU7Nskj1uFvnLlg+pV9ZxOROLss1XpsvoZCEK/6uVQBA==","signedAt":"2026-06-23T05:52:00.223Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/fhir","artifact":"https://unfragile.ai/fhir","verify":"https://unfragile.ai/api/v1/verify?slug=fhir","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"}}