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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 6 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Exposes Inkeep's RAG search infrastructure as an MCP server, allowing Claude and other MCP-compatible clients to perform semantic searches over indexed documentation without direct API calls. The server implements the Model Context Protocol specification, translating search queries into Inkeep's backend vector search and returning ranked results with source attribution. This enables LLM agents to retrieve contextually relevant documentation snippets during reasoning without leaving the MCP transport layer.
Unique: Implements MCP protocol binding for Inkeep's proprietary RAG backend, enabling zero-code integration with Claude via the MCP transport layer rather than requiring direct HTTP API integration in application code
vs alternatives: Simpler than building custom RAG pipelines with LangChain/LlamaIndex because it delegates indexing and vector search to Inkeep's managed service, and integrates directly with Claude via MCP without SDK boilerplate
Implements the Model Context Protocol (MCP) server specification in Python, exposing Inkeep search as a callable tool resource that MCP clients can discover and invoke. The server handles MCP message serialization/deserialization, tool schema registration, and request routing to Inkeep's backend. This allows any MCP-compatible host (Claude Desktop, custom agents, IDEs) to treat Inkeep search as a native capability without custom client code.
Unique: Provides a minimal, production-ready MCP server implementation that handles protocol compliance and Inkeep API bridging, eliminating the need for developers to implement MCP message handling themselves
vs alternatives: Lighter weight than building a full Claude plugin or REST API wrapper because MCP handles tool discovery and schema negotiation automatically, reducing boilerplate
Wraps Inkeep's HTTP API behind a Python client interface, handling authentication, request formatting, response parsing, and error handling. The server uses this abstraction to translate MCP search requests into Inkeep API calls and marshal results back to the client. This decouples the MCP protocol layer from Inkeep's backend API, allowing independent evolution of both.
Unique: Provides a thin Python wrapper around Inkeep's HTTP API that integrates seamlessly with the MCP server, handling authentication and response marshaling without imposing architectural constraints
vs alternatives: Simpler than using requests directly because it handles Inkeep-specific authentication and response parsing, but lighter weight than full SDK frameworks like LangChain that add dependency overhead
Registers Inkeep search as a discoverable tool in the MCP server's tool registry, exposing a JSON schema that describes the search function's parameters, return types, and documentation. MCP clients use this schema to understand how to invoke the tool and validate arguments before sending requests. The server automatically generates and serves this schema based on Inkeep's API capabilities.
Unique: Automatically generates MCP-compliant tool schemas from Inkeep's API definition, eliminating manual schema maintenance and ensuring client/server schema consistency
vs alternatives: More maintainable than manually writing JSON schemas because schema generation is automated, reducing the risk of client/server schema mismatches
Formats Inkeep search results into structured, context-rich responses that include snippets, source URLs, relevance scores, and metadata. The server enriches raw API responses with formatting logic that makes results more useful for LLM consumption, including truncation of long snippets, deduplication of similar results, and source attribution. This ensures Claude receives well-structured, actionable search results.
Unique: Implements result formatting logic tailored for LLM consumption, including snippet truncation and source attribution, rather than returning raw API responses
vs alternatives: More useful for LLM agents than raw API responses because it includes source URLs and truncates snippets to fit context windows, reducing the need for post-processing in client code
Handles Inkeep API authentication by managing API keys and credentials, supporting multiple authentication methods (environment variables, config files, or runtime injection). The server securely stores and uses credentials to authenticate requests to Inkeep's backend without exposing them to MCP clients. This ensures credentials are never transmitted over the MCP protocol.
Unique: Isolates credential management from MCP protocol layer, ensuring API keys are never exposed to clients and are only used for backend authentication
vs alternatives: More secure than passing credentials through MCP because it keeps secrets server-side, but less robust than dedicated secret management systems that provide encryption and rotation
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 Inkeep at 20/100.
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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