"Google Earth Engine" matching MCP servers:

Multi-modal RAG engine for AI assistants. Stores conversation history, conclusions, diffs, error traces, and other development artifacts in LanceDB with vector search, multi-factor scoring, and an LLM-driven consolidation pipeline.

A Model Context Protocol (MCP) server that provides a local-first RAG engine for your markdown documents. It uses a file-based Milvus vector database to index your notes, enabling LLMs to perform semantic search and retrieve relevant content from your local files.

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

MCP server for semantic search in an Obsidian Second Brain vault using self-hosted Qdrant and Google Gemini embeddings.

Local-first agent-memory MCP server with a why() tool: recall a fact together with its connected subgraph (multi-hop), so linked memories surface even when they share no words with the query. remember/recall/relate/forget/why over one fused vector + graph + columnar engine a single offline Rust binary.

Provides persistent context management for AI agents by storing and querying semantic information using Upstash Vector DB and Google AI embeddings. It enables semantic search, batch operations, and metadata filtering to help agents retrieve relevant stored knowledge.

Connects AI assistants to a persistent memory engine with Neo4j knowledge graph and ProMem extraction, enabling long-term context and associative memory across chats and workspaces.

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

An ultra-rational A2A protocol for zero-token edge pre-filtering and FEP-driven deadlock prevention. Uses Cloudflare Vectorize (384d cosine similarity) with a 24h deposit model, restricting bargaining to a 4-rally limit before forcing HTTP 402 dimension jumps.

Headless geometric memory engine for AI agents — no Vector DB, no cloud, no API key. Store and retrieve by meaning using native Vector Symbolic Architecture (NVSA) math over O_DIRECT NVMe mapping. Runs entirely on your machine via MCP.

Long-term persistent memory for AI agents with hybrid search (DiskANN + FTS), bi-temporal knowledge graph, and 81.1% LongMemEval accuracy.

Exposes a shared graph-based context engine as MCP tools for Claude, Copilot, and other AI agents, enabling knowledge ingestion, recall, search, and LLM-ready context assembly across sessions.

A Retrieval Augmented Generation system that enables AI assistants to perform semantic searches and manage document indices for markdown files. It supports PostgreSQL with pgvector and integrates both Google Gemini and Ollama for intelligent embedding generation.

A cloud-based vector memory service that provides AI assistants with persistent storage, semantic search, and entity management via the Model Context Protocol. It features multi-tenant isolation and bidirectional synchronization with macOS and Google contacts and calendars.

An MCP server that gives Claude (and any MCP client) a shared team knowledge base with semantic search. Backed by ChromaDB and Google Gemini embeddings — works across languages (RU + EN).

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.

Turns your Readwise library into a blazing-fast semantic search engine with a streaming MCP server for LLM clients, supporting vector search, nightly syncs, and deployment on serverless platforms like Vercel.

Enables Claude to search and retrieve documents from Azure AI Search indexes with intelligent summarization and analysis using LangGraph workflows and optional Google Gemini integration.

Enables unified access to SQL, semantic vector search, and columnar analytics through a single MCP interface.

A persistent long-term memory system that enables AI clients to store and recall notes, code, and research via semantic search. It utilizes Google Gemini embeddings and Supabase pgvector to provide a secure, searchable 'Second Brain' for MCP-compatible applications.