"How to Build a Knowledge Base" matching MCP servers:

Enables indexing a Markdown knowledge base with local sentence embeddings and serving retrieval over MCP, allowing LLM clients to query the indexed knowledge.

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).

A Model Context Protocol server for reading TiDB RAG knowledge base, enabling browsing, searching, and vector search of knowledge entries.

Enables users to build and manage a complete retrieval-augmented generation pipeline through conversation, including file ingestion, collection management, hybrid search, reranking, citations, and a guided setup wizard.

Provides LLM clients with a persistent, scalable knowledge graph memory system that supports semantic retrieval, contextual recall, and temporal awareness.

Enables building and querying vector-based knowledge graphs with node and edge management and semantic search.

An MCP server that enables semantic search over a private knowledge base via pgvector, with GitHub OAuth authentication and a RAG pipeline.

Enables semantic search over a Qdrant knowledge base through MCP, with optional domain filtering and stdio or Streamable HTTP transport.

A local knowledge base system based on ChromaDB that supports automatic chunking, vector storage, and efficient similarity retrieval of txt and pdf documents, with MCP protocol support allowing AI assistants to directly access knowledge management functions.

Enables AI assistants to build and query temporally-aware knowledge graphs from conversations and data. Supports adding episodes, searching entities and facts, and maintaining persistent memory across interactions.

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.

Enables Claude Desktop to perform local-first semantic search, ingest documents, and manage a private knowledge base with hybrid search, PII redaction, and multi-format support.

An MCP server that enables AI agents to query specialized, domain-specific knowledge bases built using the LightRAG framework for enhanced retrieval-augmented generation. It allows for managing and searching knowledge graphs and vector embeddings to provide accurate, context-aware information during an AI assistant's reasoning process.

Converts unstructured documents into a searchable knowledge base and exposes retrieval tools via MCP protocol for AI agents to query.

Enables Claude Code to search and retrieve from a local knowledge base of markdown notes using hybrid semantic+keyword search, keeping data entirely offline.

A read-only Model Context Protocol server that exposes a semantic knowledge base to AI agents via 27 tools. It enables querying of documents and data integrated from sources like Notion, SharePoint, HubSpot, and Slack.

A RAG knowledge base MCP server that adds vector search and reranking capabilities to opencode, supporting multimodal embeddings, multiple knowledge bases, and local storage.

A personal knowledge MCP server with vector database for the Opencode ecosystem. It enables semantic search, tag organization, and session memory logging, all running locally without external API keys.

Enables semantic search across text documents using vector embeddings stored in PostgreSQL. Provides multiple search modalities including semantic similarity, question/answer, and style-based search through a retrieval-augmented generation system.

A retrieval-only MCP server that searches a vectorized Wangsu Terraform Provider documentation knowledge base and returns relevant snippets with rules to guide the caller's LLM in generating accurate, provider-specific answers. It offloads all inference to the calling client, as it does not invoke any LLM or require API keys on the server side.