"MCP Client by Zapier - automation and integration tool" matching MCP servers:

Agent memory for facts that change. When a user corrects something, Recall replaces the old fact, tells the agent what it replaced, and keeps the old value in a history. Ask what was believed on any date, see every change to a fact, or diff two dates. Runs locally on polign_db, Postgres with pgvector, MongoDB, or Qdrant. Apache 2.0.

An MCP server for semantic search and retrieval of indexed Slack messages stored in Qdrant using Cohere reranking via AWS Bedrock. It enables users to search through Slack history, retrieve full message threads, and access channel or user statistics through natural language.

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.

Local-first, source-traceable memory for AI agents — no LLM at ingest, $0 per message, zero data egress. Gives Claude Code, Cursor, and any MCP client one shared persistent memory with semantic recall, belief revision, selective forgetting, and a provenance guard that blocks acting on stale or unconfirmed memories.

Enables any MCP client to index files, directories, and arbitrary text into a local SQLite-backed vector database and perform semantic search with language-aware chunking, filters, and context expansion, all offline without network calls.

Enables semantic retrieval over locally indexed PDF manuals and books, exposing tools to list available sources and search excerpts by query with page, chapter, source, and relevance metadata.

Enables agents to run semantic search across one or more local project directories by automatically maintaining a LAN-local Qdrant index with Ollama embeddings. Indexing, staleness checks, and incremental updates happen transparently, so users can query code by meaning without managing collections, chunks, or hashes.

Myelin provides persistent, brain-inspired memory for AI agents, enabling cross-session and cross-tool context retention with local, private storage.

An MCP server that enables RAG-powered AI chat integration for websites by crawling content, building local vector stores, and generating embeddable chat widgets. It simplifies the setup of local chat servers with support for various LLM and embedding providers.

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

Enables AI assistants to interact with a Qdrant vector database by exposing collection, point, vector, payload, snapshot, search, recommendation, discovery, and observability operations as MCP tools.

Enables MCP-capable coding agents to semantically search, store, and inspect a single local, cross-tool index of session histories and project docs. Agents can retrieve what other tools and prior sessions already discovered.

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.

A server that provides data retrieval capabilities powered by Chroma embedding database, enabling AI models to create collections over generated data and user inputs, and retrieve that data using vector search, full text search, and metadata filtering.

Provides semantic search capabilities by connecting Claude Desktop to a Cloudflare Workers backend powered by Vectorize. It enables natural language querying of knowledge bases using vector similarity and edge-based embedding generation.

Local-first memory for Claude Code and any MCP client: hybrid vector + keyword search and a bi-temporal knowledge graph in one SQLite file. Local embeddings, no API key, $0/token.

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.

Enables AI agents to find local files by meaning rather than exact filename, across Korean and English queries, using a fully local multilingual embedding index on top of voidtools Everything. It also diagnoses and repairs misconfigured Everything settings that cause hangs, with no es.exe or SDK DLL required.

A ChromaDB-based document storage MCP server that enables adding, storing, and summarizing notes with a custom note:// URI scheme and a single tool for note creation.