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"Automating Testing and Debugging for LLMs in Production" matching MCP servers:

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    Enables fast, token-efficient access to large documentation files in llms.txt format through semantic search. Solves token limit issues by searching first and retrieving only relevant sections instead of dumping entire documentation.
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    MIT
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    A production-grade MCP server and client implementation with comprehensive features including structured logging, health checks, metrics, authentication, and RAG capabilities with PostgreSQL vector search. Supports both stdio and SSE transports with containerization and security features for enterprise deployment.
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    MIT
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    A production-grade Model Context Protocol server for PostgreSQL. Lets AI agents safely inspect, query, operate, and tune a Postgres database — over 100 tools spanning catalog introspection, query intelligence, natural-language SQL, structural diffs, hybrid search, graph queries, data movement, live ops, and more.
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    MIT
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    An integration server implementing the Model Context Protocol that enables LLM applications to interact with Milvus vector database functionality, allowing vector search, collection management, and data operations through natural language.
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    Apache 2.0
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    A local Retrieval-Augmented Generation system that enables users to ingest markdown files into a FAISS-powered vector knowledge base for semantic search. It provides tools for document indexing and context retrieval to support informed LLM queries without external dependencies.
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    A server implementation that allows secure communication between MCP clients and privateGPT, enabling users to chat with privateGPT using knowledge bases and manage sources, groups, and users through a standardized Model Context Protocol.
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    MIT
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    Munin is a high-performance, pragmatic memory layer for AI agents (Cursor, Claude Code, OpenClaw, Gemini CLI,...). Unlike other solutions, Munin focuses on developer productivity with: * Multi-Project Support: Isolate memories into separate "brains" (Context Cores). * GraphRAG: Automatically builds a knowledge graph from your context. * Sub-200ms Search: Blazing fast Hybrid & Semantic
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    Multi-modal RAG engine for AI assistants. Stores conversation history, conclusions, diffs, error traces, and other development artifacts in LanceDB with vector search, multi-factor scoring, and an LLM-driven consolidation pipeline.
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    MIT
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    Semantic memory for AI builders: capture the tacit engineering know-how that never reaches your docs, recall it the moment it applies. Built in Rust on Postgres and pgvector.
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    Provides persistent memory for AI agents using hybrid search (vector embeddings + BM25) with neural reranking, enabling storage and retrieval of insights, debugging solutions, and patterns across coding sessions.
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    MIT
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    An MCP server that enables LLMs to perform semantic and fulltext searches within Neo4j while executing complex, search-augmented Cypher queries for GraphRAG applications. It provides tools for database schema discovery and supports multi-provider embeddings to facilitate advanced graph traversals.
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    MIT
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    Enables LLMs to interact with Zvec vector database through tools for collection management, document operations, vector search, and AI-powered embeddings.
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    Apache 2.0
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    An MCP server for semantic search and retrieval of indexed Slack messages stored in Qdrant using Cohere reranking via AWS Bedrock. It enables users to search through Slack history, retrieve full message threads, and access channel or user statistics through natural language.
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    Long-term and multimodal memory for AI agents. Store facts and conversations with add_memory, recall them with search_memories — 8 tools over stdio/SSE/HTTP. Per-character memory isolation, LLM-based semantic deduplication, FAISS + JSON storage, and a fully local option (Ollama, no API key). Drop-in compatible with Mem0.
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    Apache 2.0