"A search for information about Cursor (likely the AI code editor or computer cursor)" matching MCP servers:

A MCP server to allow the LLM in Cursor to access Rust Analyzer, Crate Docs and Cargo Commands.

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

Enables web search, content extraction, question answering, and RAG context generation using Tavily API with Cursor-compatible underscore-named tools.

Enables cost-effective repository analysis, code search, file editing, and task planning by wrapping the cursor-agent CLI through focused tools. Reduces token usage by offloading heavy thinking tasks from Claude to specialized operations with configurable output formats.

MCP server providing filesystem allowlist and pack-aware Qdrant RAG search. Enables reading files, listing directories, and performing filtered RAG searches with case and pack IDs.

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 AI coding assistants to semantically search codebases by meaning rather than exact text, with zero external daemons and fully local embeddings. Works out-of-the-box with Claude Code, Gemini CLI, Antigravity, and Cursor.

Enables semantic code search across indexed code folders using vector embeddings, with support for multiple embedding providers and automatic file watching. Provides an admin UI and integrates with MCP clients for natural language code queries.

An installable MCP server that reviews diffs/PRs against a target repository's own coding conventions, learned via RAG over its code, docs, and lint config, citing specific file:line sources.

Enables natural language search over indexed codebases with source citations and incremental indexing.

Provides AI-powered search and documentation tools using Google Vertex AI or Gemini API with real-time web search grounding, enabling technical queries, code analysis, documentation retrieval, and architecture recommendations to overcome LLM knowledge gaps.

Enables AI clients to search curated, cited knowledge bases built from founders', CEOs', and investors' public talks, essays, and posts, returning passages with source links and dates. It also exposes available source collections via MCP tools so users can ask how those individuals might approach business, product, or growth questions.

Provides an intelligent, graph-based memory system for LLM agents using the Zettelkasten principle, enabling automatic note construction, semantic linking, memory evolution, and autonomous graph maintenance with background optimization processes.

CLI-first web and code search for agents, with MCP support for integration with IDEs like Cursor, VS Code, and Claude Code.

A Model Context Protocol server that enables AI models to perform real-time internet and knowledge searches through Higress, enhancing model responses with up-to-date information from Google, Bing, Arxiv, and internal knowledge bases.

Enables coding agents to find code by what it does rather than what it's called, searching local projects and remote GitHub or Bitbucket repositories all at once. Indexing and semantic search run fully offline with a locally loaded model, so code never leaves the machine.

WordPress plugin that runs an MCP server on your own site: search and read published posts and pages, look up WooCommerce products, describe the site as schema.org types and query site figures, with drafts only when the site enables them. Signed-in access by default; anonymous access and OAuth are optional and off.

Multi-provider embedding MCP server with local Milvus for semantic code search. Works with Claude Code, Cursor, Windsurf, and other MCP-compatible IDEs.

Enables semantic search across documentation stored in Gemini FileSearchStores, returning AI-generated answers with source citations.

A local MCP server that provides semantic code search for Python codebases using tree-sitter for chunking and LanceDB for vector storage. It enables natural language queries to find relevant code snippets based on meaning rather than just text matching.