"A more powerful and accurate web search agent for use with Cursor" matching MCP servers:

Enables searching through vectorized Cursor IDE chat history via a FastAPI service powered by LanceDB and Ollama.

Provides retrieval-augmented generation (RAG) capabilities by ingesting various document formats into a persistent ChromaDB vector store. It enables semantic search and retrieval using either OpenAI or Ollama embeddings for processing local files, directories, and URLs.

MCP server for semantic search over PDF documents using RAG, pgvector, and OpenAI embeddings. Includes a web app for document upload and management.

Enables semantic search over your Cursor IDE chat history by vectorizing prompts and storing them in LanceDB. Provides a Dockerized API to perform vector similarity searches against your chat history.

Enables natural-language search over locally indexed files such as markdown, text, images, videos, and PDFs, and retrieves indexed text or media metadata by path. It lets Cursor query a local embedding index built with Gemini and SQLite.

A semantic search system for Claude Code transcript history, exposed as an MCP server so Claude Code can query its own past sessions.

Local offline semantic search over documents (txt, md, pdf, docx, pptx, csv). Indexes folders into a LanceDB vector database with multilingual embeddings and supports hybrid vector + keyword search via Reciprocal Rank Fusion. No API keys, no cloud, no Docker required.

Enables AI agents to search local Markdown documents using natural language, with automatic indexing and section-level retrieval.

Indexes codebases using semantic embeddings for natural language search, enabling developers to find code with queries like 'how does authentication work'.

Semantic, on-demand skill retrieval for Claude Code that saves tokens and improves skill discovery by replacing the native skill listing with vector embedding search.

A tiny RAG-lite retrieval engine that indexes files on disk and provides semantic search via MCP, returning relevant text chunks (file, line, score) without generating answers.

An MCP server that enables hybrid semantic and keyword retrieval over your documents, using PostgreSQL and pgvector as the backend. It fuses rankings from both methods to provide high-quality search results to the language model.

Enables semantic search over personal Markdown journal entries by indexing them with Ollama embeddings into a SQLite vector store and exposing retrieval through a Streamable HTTP MCP endpoint at /mcp.

A Model Context Protocol server that searches transcript segments in a Turso database using vector similarity, allowing users to find relevant content by asking questions without generating new embeddings.

An MCP server for semantic code search using Qdrant vector database, enabling natural language queries to find relevant code snippets across indexed codebases.

A local-first MCP server that enables semantic search over PDF and DOCX documents using structure-aware parsing and vector storage. It allows users to query their local knowledge base through Claude Code without cloud dependencies or GPU requirements.

A Python-based local indexing server that creates semantic search capabilities for codebases using ChromaDB, allowing Cursor IDE to perform vector searches on your code without sending data to external services.

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).

An MCP server that retrieves resume/experience evidence relevant to a job description via vector RAG, and tracks fit-analysis results in a configurable tracking store (Notion or SQLite), with tools like match_job, push_to_tracker, and list_applications.

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.

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.

Enables devices and agents to share a self-hosted memory store over a remote Streamable HTTP MCP endpoint, with hybrid retrieval, scoped access tokens, and encrypted vault references for sensitive data.

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.

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.

MCP server that searches documents in Qdrant using embeddings from LMStudio. Takes a text query, converts it to a vector via LMStudio's OpenAI-compatible API, and performs semantic search in Qdrant.

An unofficial MCP server that provides semantic search capabilities for Hugging Face models and datasets, enabling Claude and other MCP-compatible clients to search, discover, and explore the Hugging Face ecosystem using natural language queries.

Enables Claude to store and retrieve information with semantic search using Qdrant vector database, providing persistent memory for conversations, code, and documentation.

Enables semantic search across indexed documents using vector embeddings. Index GitHub repositories and URLs to perform natural language queries with AI-enhanced contextual results.

A clean architecture backend API that registers and semantically searches data using vector embeddings, acting as an MCP context provider to connect AI models to external data.