| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Enables semantic search and document retrieval from AWS Knowledge Base using the Bedrock Agent Runtime API, implementing MCP server protocol to expose KB queries as callable tools. The server translates MCP tool requests into Bedrock Agent Runtime calls, handling authentication via AWS credentials and returning structured search results with document metadata and relevance scores.
Unique: Implements MCP server protocol as a bridge to AWS Bedrock Agent Runtime, allowing LLM clients to query Knowledge Base without direct AWS SDK dependencies. Uses MCP's standardized tool-calling interface to abstract Bedrock API complexity, enabling seamless integration into multi-tool agent workflows.
vs alternatives: Tighter AWS ecosystem integration than generic RAG solutions, but archived status and Bedrock dependency limit portability compared to self-hosted vector DB alternatives like Pinecone or Weaviate.
Implements the Model Context Protocol (MCP) server specification to expose AWS Knowledge Base as a callable tool within MCP-compatible clients. The server handles MCP transport (stdio or HTTP), tool schema registration, request/response serialization, and error handling according to MCP specification, enabling any MCP client to discover and invoke KB retrieval without AWS SDK knowledge.
Unique: Provides a reference implementation of MCP server pattern for AWS services, demonstrating how to bridge cloud provider APIs into the MCP ecosystem. Uses MCP's standardized tool registry and request routing to abstract service-specific details.
vs alternatives: More standardized than custom AWS integrations, but archived status means it may lag behind current MCP spec evolution compared to actively maintained servers.
Handles authentication and API calls to AWS Bedrock Agent Runtime service, managing AWS credentials (IAM roles, access keys, or STS tokens) and translating MCP tool requests into Bedrock-compatible invocation payloads. The server constructs agent invocation requests with query parameters, handles response parsing, and manages session state across multiple queries.
Unique: Abstracts AWS credential management and Bedrock API complexity behind MCP tool interface, allowing clients to invoke agents without handling authentication details. Uses AWS SDK's built-in credential chain (IAM roles, environment variables, credential files) for secure credential handling.
vs alternatives: Simpler credential management than custom HTTP clients, but tightly coupled to Bedrock API contract compared to generic agent frameworks like LangChain.
Parses Bedrock Agent Runtime responses containing Knowledge Base search results, extracting document metadata (source, relevance score, content excerpt), and reformatting results into a standardized structure for MCP clients. The server handles variable response formats from Bedrock, normalizes document references, and includes source attribution for RAG transparency.
Unique: Implements Bedrock-specific response parsing that preserves document metadata and relevance signals, enabling RAG transparency. Normalizes variable Bedrock response formats into a consistent schema for downstream MCP clients.
vs alternatives: More transparent than black-box search APIs, but tightly coupled to Bedrock schema compared to generic vector DB clients that expose raw embeddings.
Maintains conversation history and session state across multiple KB queries, allowing clients to build multi-turn interactions where each query can reference previous results. The server manages session tokens from Bedrock Agent Runtime, preserves context across invocations, and enables follow-up queries that build on prior KB searches without re-querying the same documents.
Unique: Leverages Bedrock Agent Runtime's native session management to maintain conversation context across KB queries, enabling stateful RAG interactions without explicit conversation storage in the MCP server.
vs alternatives: Simpler than custom conversation management, but limited by Bedrock's session lifecycle compared to frameworks like LangChain that offer explicit memory abstractions.
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 AWS KB Retrieval 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
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