| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 7 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Parses OpenAPI 3.0 and Swagger 2.0 specifications from URLs or local files and automatically registers each API endpoint as an MCP tool. Uses schema introspection to extract operation metadata (parameters, request/response types, authentication requirements) and generates tool definitions compatible with the Model Context Protocol specification, enabling LLM clients to discover and invoke REST APIs without manual tool definition.
Unique: Automatically generates MCP tool definitions from OpenAPI specs without manual tool coding, using schema introspection to map REST endpoints directly to callable LLM tools with parameter validation and type safety derived from the spec
vs alternatives: Eliminates manual tool definition boilerplate compared to writing custom MCP tools for each API, enabling rapid integration of any Swagger-documented service into LLM workflows
Constructs HTTP requests from MCP tool invocations by mapping tool parameters to OpenAPI operation definitions (path parameters, query strings, request bodies, headers). Executes requests against the target API using the HTTP method and endpoint specified in the schema, handling content negotiation (JSON, form-encoded, XML) and returning raw or parsed responses. Implements retry logic and timeout handling for resilient API calls.
Unique: Automatically binds MCP tool parameters to OpenAPI-defined request formats (path, query, body) without manual request construction code, using schema metadata to determine content types, serialization formats, and parameter locations
vs alternatives: Reduces boilerplate compared to manual HTTP client code by deriving request structure from OpenAPI specs, enabling parameter validation and type coercion at the MCP layer before sending requests
Manages API authentication credentials (API keys, basic auth, bearer tokens) and injects them into HTTP request headers according to the OpenAPI security scheme definitions. Supports multiple authentication methods per API and selects the appropriate credentials based on the operation's security requirements. Stores credentials securely (environment variables or encrypted config) and applies them transparently to all tool invocations.
Unique: Derives authentication requirements from OpenAPI security scheme definitions and automatically injects credentials without exposing them in tool parameters, using environment-based credential storage for secure handling
vs alternatives: Separates credential management from tool definitions compared to embedding credentials in MCP tool schemas, reducing security risk and enabling credential rotation without tool redefinition
Validates MCP tool parameters against OpenAPI schema constraints (required fields, type validation, enum constraints, min/max values, pattern matching) before constructing HTTP requests. Coerces parameter types (string to number, boolean parsing) based on the schema definition and rejects invalid inputs with detailed error messages. Implements JSON Schema validation using a schema validator library to ensure type safety and catch errors early.
Unique: Uses OpenAPI schema definitions to automatically validate and coerce tool parameters before API invocation, implementing JSON Schema validation to enforce type safety and constraint checking derived from the spec
vs alternatives: Provides schema-driven validation without manual validation code, catching parameter errors before they reach the API and reducing failed requests compared to runtime API error handling
Parses HTTP responses from REST APIs and extracts structured data based on OpenAPI response schema definitions. Handles multiple content types (JSON, XML, plain text) and deserializes responses into typed objects matching the schema. Implements error handling for malformed responses and provides fallback parsing strategies. Optionally filters or transforms responses to extract only relevant fields defined in the schema.
Unique: Automatically parses and validates API responses against OpenAPI schema definitions, handling multiple content types and providing typed output that matches the schema without manual parsing code
vs alternatives: Eliminates manual response parsing and validation code by deriving parsing logic from OpenAPI schemas, ensuring responses match expected types and reducing errors from malformed data
Implements the MCP server protocol lifecycle (initialization, tool discovery, tool invocation) and exposes registered tools through the MCP interface. Handles MCP client requests for tool listing, tool metadata retrieval, and tool execution. Manages server state (loaded specs, registered tools, authentication context) and provides introspection endpoints for clients to discover available tools and their schemas. Implements graceful shutdown and resource cleanup.
Unique: Implements the MCP server protocol to expose REST APIs as discoverable tools, handling the full lifecycle from initialization through tool invocation with state management and introspection support
vs alternatives: Provides a standardized MCP interface for REST API access compared to custom tool implementations, enabling compatibility with any MCP-compatible client without client-specific code
Loads and manages multiple OpenAPI specifications simultaneously, registering tools from each spec and aggregating them into a single tool namespace. Handles spec conflicts (duplicate operation IDs, overlapping paths) by namespacing or renaming tools. Supports dynamic spec loading (adding/removing specs at runtime) and maintains a registry of all loaded specs and their associated tools. Enables LLM clients to interact with multiple APIs through a single MCP server instance.
Unique: Aggregates tools from multiple OpenAPI specs into a single MCP server namespace, handling spec conflicts and enabling dynamic spec loading without server restart
vs alternatives: Eliminates the need to run separate MCP servers for each API compared to single-spec servers, reducing operational complexity and enabling unified tool discovery for multi-API workflows
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 swagger-mcp at 25/100. swagger-mcp leads on ecosystem, while Supabase is stronger on quality.
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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
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