"Tools Compatible with Cursor IDE" matching MCP servers:

MCP server for compressing AI embeddings by 5-7x using TurboQuant (PolarQuant + QJL), with tools to compress, decompress, estimate savings, and embed+compress vectors.

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

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.

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.

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.

Self-hosted MCP-native agent memory server. Gives AI agents persistent, decay-weighted memory via 83 MCP tools — no cloud, full control. RocksDB+HNSW backend. Works with Claude Code, Cursor, and any MCP-compatible agent.

Enables ingestion and semantic search over text documents using PostgreSQL + pgvector and OpenAI-compatible embeddings, allowing any LLM agent to retrieve relevant chunks for grounded answers.

Provides fast static embedding, similarity, and search capabilities via MCP tools and an OpenAI-compatible HTTP API using a tiny 16M-parameter English embedding model.

Enables Claude to interact with core AWS services like S3, EC2, RDS, and CloudWatch, along with a generic SDK wrapper for any AWS operation. It also supports cost monitoring and optional vector store capabilities for document ingestion and search.

A lightweight, local-first MCP server that automatically watches folders, chunks and embeds files using Transformers.js, and exposes semantic search capabilities to VS Code and Cursor. Runs completely offline with SQLite vector storage, designed for resource-constrained environments.

Connect your Pinecone projects to Cursor, Claude, and other AI assistants

Enables AI agents to interact with INFINI Easysearch (compatible with Elasticsearch/OpenSearch APIs) through 121 tools covering cluster management, index operations, document manipulation, search queries, snapshots, and monitoring.

Provides a plug-and-play persistent memory layer for MCP-compatible AI assistants, enabling them to store, retrieve, and delete memories across multiple databases simultaneously using semantic vector search.

Mem0-compatible persistent memory for AI agents - write facts once, recall them semantically in any session. Self-hostable open-source server, or managed cloud with a remote MCP endpoint at https://deepmem.dev/mcp.

Enables AI agents to access hybrid vector, reasoning-based tree retrieval, and agent memory through the Model Context Protocol (MCP), supporting Claude Desktop and other MCP-compatible clients.

Model Context Protocol (MCP) server for TigerGraph that lets AI agents interact with TigerGraph through the MCP standard using pyTigerGraph's async APIs.

MCP Memory is a MCP Server that gives clients like Cursor and Claude the ability to remember user preferences and behaviors across conversations using vector search.

Official Kimss MCP server — call your Kimss agents, models, files, and vector stores from Cursor and other MCP clients using your API key.

Persistent AI memory server with 3-layer hybrid search (vector + FTS5 + keyword), confidence scoring via Reciprocal Rank Fusion, episodic/profile memory, and 16 tools. Zero LLM dependency. Works standalone with Claude Desktop and Claude Code. MIT licensed.

Enables natural language search and analysis of uploaded PDF, CSV, and Excel documents using retrieval-augmented generation and MCP tools, providing contextual answers to user queries.