"Setting up SQL Server connections for agent querying" matching MCP servers:

Gives Claude Code read-only semantic search over an Obsidian vault by reading the embedding files the Smart Connections plugin already produces, so no API key or network is needed at query time and it works across the WSL/Windows boundary. Exposes tools for meaning-based note and block search with folder scoping, related-note discovery, note reading, and index health checks.

Enables Claude to perform semantic search across your Obsidian vault using Smart Connections vector database. Provides meaning-based search, related note discovery, and context retrieval for RAG queries instead of basic keyword matching.

A local-first knowledge base for LLM coding agents that indexes repository documentation, concept ontology, and build targets into Qdrant and exposes retrieval as MCP tools (search, get, list sources, reindex).

Exposes tools for AI agents to check the connected object store's source and index status, and to search a synced bucket prefix for source-backed chunks using keyword, vector, or hybrid retrieval. Results include source URI, chunk and score so citations can be preserved.

Enables hybrid search over policies using Reciprocal Rank Fusion and provides grounded, context-aware answers via a LangGraph agent with COSTAR prompting.

Enables external clients to query a vector knowledge base about skills and roles through an MCP retrieval tool, supporting RAG-based question answering with tool-calling.

Enables AI agents to search and analyze clinical trial data from ClinicalTrials.gov using both structured SQL queries for filtering trials by status, phase, and conditions, and semantic vector search for exploring detailed protocol information like exclusion criteria.

A Machine Control Protocol (MCP) server that enables storing and retrieving information from a Qdrant vector database with semantic search capabilities.

A self-evolving RAG system that enables AI agents to autonomously read and write memory, continuously learning and adapting user preferences, daily logs, and knowledge graphs across applications.

Gives MCP clients persistent personal memory and a versioned skill library, letting agents store facts, preferences, and decisions and recall them through hybrid semantic-plus-keyword search with automatic time weighting. Everything runs inside Cloudflare's free tier, with no server or database to operate.

A benchmarking MCP server that stores facts as both vector RAG and a knowledge graph, enabling head-to-head comparison of memory retrieval methods.

Enables cross-agent incremental memory retrieval and indexing for long coding sessions, preserving raw conversation logs across multiple CLI agents and recovering lossless context after compaction without local LLM generation.

Enables users to ingest documents into a PostgreSQL/pgvector knowledge base, run semantic search over them, and get grounded answers through a retrieval-augmented generation pipeline backed by a free LLM. It also lets clients spin up specialized AI agents on demand and exposes knowledge-base stats and configuration as resources for tutoring workflows.

Provides persistent memory for AI coding agents, enabling them to save, search (hybrid vector/BM25/concept graph), list sessions, and forget memories via MCP tools.

Retrieval-only MCP server that turns any knowledge source (Obsidian vault, notes, reference sets) into searchable Qdrant-backed skills, exposing list_skills, search_vault, and search_skill tools for agents to query via stdio or SSE.

Exposes search_knowledge and create_quote over Streamable HTTP with JWT validation, enabling permission-scoped knowledge retrieval and deterministic simulated quote creation while keeping pricing and approvals outside model control.

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.

MCP server enabling Claude Desktop to answer questions from local Word and PDF documents by searching a vector index built from their contents.

MCP server that provides secure read-only access to a local folder, enabling file listing, reading, semantic search (RAG), and indexing status via natural language, integrated with Claude Desktop and a custom agent loop.

Enables AI agents to maintain long-term, cross-session memory by extracting facts, reconciling state conflicts, and retrieving relevant memories via vector search.

Enables MCP-capable coding agents to semantically search, store, and inspect a single local, cross-tool index of session histories and project docs. Agents can retrieve what other tools and prior sessions already discovered.

Integrates R2R (Retrieval-Augmented Generation) with Claude Desktop, enabling semantic search across knowledge bases and RAG-based question answering with support for vector, graph, web, and document search.

Provides persistent memory management for AI agents with 14 tools, including semantic search, context synthesis, auto-context middleware, and write-back actions to external services like Slack.

Enables AI assistants to retain atomic facts, decisions, and episodic lessons in a persistent shared SQLite graph memory, then recall them through hybrid BM25 plus dense-vector search with spreading activation and progressively packed context. Also supports reflection-based belief revision so stale or contradictory memories are superseded rather than returned alongside current ones.

Enables AI agents to read, write, organize, and link persistent long-term memory across model providers using embeddings and pgvector retrieval.

A local-first AI backend that combines Model Context Protocol, Retrieval-Augmented Generation, and an agent runtime to answer questions from a private knowledge base.

A privacy-preserving local RAG system integrated with MCP, enabling natural language queries over ingested documents and a SQLite database through vector search and local database tools.

Enables an AI agent to recall past approvals, edits and rejections that are similar to the task at hand before drafting, and to record each new decision along with the human's reason. Memory lives in a local Postgres (PGlite + pgvector) store, so corrections carry across sessions without hosting anything.

Enables LLM applications and agents to persist and recall scoped memories in a local SQLite store, with semantic/hybrid/metadata search, review-first corrections and summaries, and controlled export, backup, and purge operations over stdio.

A local-first Graph-RAG system combining ChromaDB with metadata-based graph relationships and Gemini 2.5 Flash for intelligent Q&A over Obsidian vaults, supporting MCP clients like Claude Desktop, Cursor, and Raycast.