"Collaboration Between Multiple AIs via MCP (Master Control Program)" matching MCP servers:

Provides intelligent, persistent memory for AI assistants with semantic search, natural language queries, and OAuth-based team collaboration, enabling context-aware conversations across multiple clients.

Provides persistent memory for AI coding agents via MCP, enabling teams to share and recall facts across sessions. Automatically captures, classifies, and curates knowledge from supported transcript sources.

A local-first personal RAG memory system that turns AI conversation history into a searchable, retrievable knowledge base via MCP, enabling LLMs to semantically search past conversations.

Enables LLM hosts to retrieve live, relevant documentation excerpts from official library docs sites via a search-and-RAG tool, avoiding reliance on training data.

Dual-mode tool providing long-term memory management for Claude Code and MCP clients, with semantic search and user/session isolation via Qdrant.

Provides SQL-backed semantic search over indexed notes using pgvector, exposing tools to search and list note sources via natural language.

Enables Claude to scrape, crawl, extract data, and sync to vector databases using scrapedatshi's RAG pipeline, with support for multiple embedding and vector DB providers.

A personal memory MCP server that ingests AI agent conversation logs from multiple platforms into a searchable PostgreSQL+pgvector database, enabling cross-session recall of past reasoning and decisions.

Enables answering Formula 1 FAQ questions via vector search and web search using Bright Data.

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

Turns a tools.yaml contract into typed, tenant-guarded vector database tools that agents can use via Python imports or MCP.

Intelligent knowledge base system that enables users to process documents in 25+ formats, perform semantic search and Q\&A through vector retrieval. Supports multiple AI models including OpenAI and DouBao with local processing capabilities.

An MCP server for aggregating multi-domain intelligence feeds, performing 80:20 crossover analysis, and publishing structured insights to multiple channels.

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.

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 a unified Graph-RAG memory layer combining vector similarity search via Qdrant with entity-relationship traversal via Kuzu, allowing memories to be stored, retrieved, and expanded along an entity graph.

Enables indexing and semantic search of codebases and documents via MCP, using Ollama embeddings and Qdrant vector store.

A server that provides access to Baidu Cloud Vector Database functionality through the Model Context Protocol, enabling LLM applications to perform vector searches and database operations via natural language.

Connects AI clients to MindsDB via the MySQL protocol to execute SQL queries, manage databases, and perform semantic searches within knowledge bases. It enables automated workflows through job scheduling and provides seamless integration with external data sources.

Enables a single MCP server for interacting with multiple vector databases through pluggable backends, starting with Pinecone. It supports schema-aware index management, upserts, hybrid search, embeddings, TTL conventions, and namespace operations.