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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 9 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
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.
Unique: Implements dual-layer abstraction: MCP tool interface wraps fhirpy client library, enabling LLM agents to invoke FHIR searches without direct API knowledge while maintaining full FHIR R4 compliance through standardized parameter mapping
vs alternatives: Provides natural language FHIR search through MCP protocol (enabling any MCP-compatible AI tool integration) rather than requiring direct REST API calls or custom healthcare data adapters
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.
Unique: Implements dual-role OAuth architecture where the MCP server acts as OAuth client to FHIR servers, handling token lifecycle (acquisition, refresh, expiration) transparently while exposing simple MCP tool interfaces — developers never touch OAuth directly
vs alternatives: Abstracts OAuth 2.0 PKCE complexity away from AI agents compared to direct REST API integration, reducing security misconfiguration risk while maintaining full FHIR R4 compliance
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.
Unique: Caches FHIR CapabilityStatement at server initialization and exposes it as an MCP tool, allowing AI agents to introspect server capabilities without direct HTTP calls — bridges the gap between dynamic FHIR servers and static LLM knowledge
vs alternatives: Provides server-agnostic capability discovery through MCP protocol rather than requiring agents to make raw HTTP calls to /metadata, enabling safer and more informed query generation
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.
Unique: Implements dual OAuth roles (server + client) within a single MCP server, enabling transparent credential management for AI agents while maintaining OAuth security standards — agents never see FHIR server credentials, only MCP-level tokens
vs alternatives: Provides centralized OAuth token management for multiple FHIR servers compared to distributing credentials to each AI agent, reducing credential exposure surface and enabling audit trails
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).
Unique: Implements MCP protocol as a first-class abstraction layer, supporting both HTTP and STDIO transports from a single codebase — enables seamless integration with any MCP-compatible AI tool without custom adapters
vs alternatives: Provides standardized MCP protocol integration compared to custom REST API wrappers, enabling AI tools to discover and call FHIR operations dynamically with type-safe schemas
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.
Unique: Enforces configuration validation at server startup using Pydantic models, preventing invalid credentials or URLs from causing runtime failures — configuration errors are caught immediately rather than during first FHIR operation
vs alternatives: Provides type-safe configuration validation compared to manual string parsing, reducing configuration errors and enabling IDE autocomplete for configuration objects
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.
Unique: Leverages Python asyncio for non-blocking FHIR operations, enabling the server to handle multiple concurrent AI agent requests without thread overhead — single-threaded async model scales better than thread pools for I/O-bound healthcare data access
vs alternatives: Provides non-blocking FHIR access compared to synchronous REST clients, enabling higher throughput and lower latency for multi-agent healthcare AI systems
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.
Unique: Delegates FHIR resource handling to fhirpy library, providing object-oriented abstractions for all FHIR R4 resource types — developers work with Python objects instead of raw JSON, reducing FHIR spec knowledge requirements
vs alternatives: Provides type-safe FHIR resource manipulation compared to raw REST API calls, reducing serialization errors and enabling IDE autocomplete for FHIR resources
+1 more capabilities
Executes SQL queries against Supabase PostgreSQL instances through the Model Context Protocol, translating natural language or structured query requests into parameterized SQL statements. Uses MCP's tool-calling interface to expose database operations as callable functions with schema validation, enabling LLM agents to perform CRUD operations, joins, and aggregations with automatic connection pooling and credential management through Supabase client SDK.
Unique: Exposes Supabase PostgreSQL as MCP tools with automatic credential injection from Supabase client SDK, eliminating manual connection string management and enabling seamless LLM-to-database queries within Claude or compatible agents
vs alternatives: Tighter integration than generic SQL MCP servers because it leverages Supabase's built-in authentication and connection pooling rather than requiring separate database credential configuration
Exposes Supabase Auth session state and user metadata through MCP tools, allowing agents to inspect current authentication context, retrieve user profiles, and trigger auth-related operations. Integrates with Supabase's JWT-based auth system to validate sessions and access user claims without re-authenticating, using the Supabase client's built-in session management.
Unique: Integrates Supabase's JWT-based auth system directly into MCP tool interface, allowing agents to inspect and act on auth state without managing separate credential stores or re-authentication flows
vs alternatives: More seamless than generic auth MCP servers because it leverages Supabase's built-in session management and avoids redundant credential passing between agent and auth system
Invokes Supabase Edge Functions (serverless TypeScript/JavaScript functions) through MCP tools, passing parameters and receiving results with optional streaming support. Uses Supabase's edge function HTTP API to trigger functions with automatic authentication headers and response parsing, enabling agents to execute custom business logic without embedding it in the agent itself.
Supabase scores higher at 42/100 vs FHIR at 25/100.
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Unique: Exposes Supabase Edge Functions as MCP tools with automatic authentication and response parsing, allowing agents to invoke custom serverless logic without managing HTTP clients or credential injection
vs alternatives: More integrated than generic HTTP MCP tools because it handles Supabase-specific authentication, error handling, and response formatting automatically
Subscribes to real-time changes on Supabase tables through MCP's event streaming interface, using Supabase's PostgreSQL LISTEN/NOTIFY mechanism to push INSERT, UPDATE, and DELETE events to agents. Maintains persistent WebSocket connections and filters events by table and row-level policies, enabling agents to react to database changes without polling.
Unique: Bridges Supabase's PostgreSQL LISTEN/NOTIFY real-time system with MCP's tool interface, enabling agents to subscribe to database changes without managing WebSocket connections or event serialization
vs alternatives: More efficient than polling-based approaches because it uses Supabase's native real-time infrastructure rather than repeated database queries
Manages files in Supabase Storage buckets through MCP tools, supporting upload, download, list, and delete operations with automatic authentication and path-based access control. Uses Supabase's S3-compatible storage API with built-in support for public/private buckets and signed URLs for temporary access, enabling agents to handle file I/O without managing cloud storage credentials.
Unique: Exposes Supabase Storage's S3-compatible API as MCP tools with automatic authentication and signed URL generation, eliminating the need for agents to manage cloud storage credentials or generate temporary access tokens
vs alternatives: More integrated than generic S3 MCP tools because it leverages Supabase's built-in bucket policies and authentication rather than requiring separate AWS credentials
Performs semantic similarity searches on vector embeddings stored in Supabase PostgreSQL using pgvector extension, translating natural language queries into embedding vectors and executing cosine/L2 distance searches. Integrates with embedding providers (OpenAI, Cohere) or uses pre-computed embeddings, enabling agents to retrieve semantically similar documents or records without full-text search limitations.
Unique: Integrates pgvector directly into MCP tools with automatic embedding generation and distance calculation, enabling agents to perform semantic search without managing separate vector database infrastructure
vs alternatives: More efficient than external vector databases (Pinecone, Weaviate) for Supabase users because it colocates embeddings with relational data, reducing network latency and simplifying data synchronization
Exposes Supabase database schema information through MCP tools, allowing agents to discover table structures, column types, constraints, and relationships without manual schema documentation. Queries PostgreSQL information_schema and Supabase metadata tables to dynamically generate schema descriptions, enabling agents to construct valid queries and understand data relationships.
Unique: Queries Supabase's PostgreSQL information_schema directly through MCP tools, enabling agents to dynamically discover and adapt to database schemas without pre-configured schema definitions
vs alternatives: More flexible than static schema definitions because it reflects live database state, including recent migrations or schema changes
Enforces Supabase Row-Level Security policies within agent queries, ensuring that agents can only access rows permitted by RLS rules defined in the database. Evaluates policies based on authenticated user context (JWT claims, user ID) and applies WHERE clause filters automatically, preventing unauthorized data access at the database layer rather than application layer.
Unique: Delegates authorization enforcement to PostgreSQL RLS policies rather than implementing authorization in agent code, ensuring that data access rules are centralized and cannot be bypassed by agent logic
vs alternatives: More secure than application-level authorization because RLS is enforced at the database layer, preventing accidental data leaks even if agent code has bugs
+1 more capabilities