"A server for WeChat messaging and chat" matching MCP servers:

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.

Enables AI agents to read the local, already-logged-in WeChat account's chat history: fetch recent messages per contact for reply suggestions, bulk-export all conversations into a local SQLite index for full-text and semantic search, and export media or Moments timelines as offline HTML. Everything runs locally and read-only on macOS, exposing 15 MCP tools plus a matching CLI.

MCP server that enables AI agents to fetch and summarize WeChat public articles, supporting export to HTML, Markdown, Obsidian, Notion, and OneNote.

A conversational chat interface that uses RAG over a synthetic test corpus and a domain-knowledge corpus, exposing retrieval via MCP.

An AI conversation management layer that enables creating chat sessions, persisting message history to GitHub, and performing semantic searches over past interactions. It supports multi-turn threading and context injection to integrate external memory sources into Claude conversations.

An MCP server that enables analyzing and querying GitHub repositories through the GitHub Chat API, allowing users to index repositories and ask questions about their code, architecture and tech stack.

Neo4j 기반 챗봇 MCP 서버로, 도메인 특화 tool과 Text2Cypher 방식을 조합해 안전하고 정확한 지식그래프 질의응답을 제공합니다.

A basic Model Context Protocol server implementation that demonstrates core functionality including tools and resources for AI chat applications.

An MCP server that turns the Hugging Face Hub into a conversational model picker with a tool-use eval harness to prove the agent actually uses it correctly.

Enables agents to run cross-runtime, cross-machine BM25 full-text search over historical conversations from Hermes, Google Antigravity, OpenClaw, Codex, and Claude Code, and to retrieve any past session's full transcript by ID. Queries return only ranked matching snippets, so recall costs no extra tokens or embeddings.

Enables any MCP-compatible AI assistant to search, filter, and retrieve information from a local document collection using a hybrid search pipeline with vector, BM25, reranking, and LLM enrichment.

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.

Enables AI coding agents to persistently store and retrieve conversation history with hybrid semantic and keyword search, cross-encoder reranking, session filtering, and archiving through MCP tools.

Enables interactive chat with AI models via the Anthropic API, supporting document retrieval, command-based prompts, and extensible MCP tool integrations.

Enables interactive AI chat with document retrieval and command-based tool integrations via the Model Control Protocol.

Enables interactive chat with AI models via Anthropic API, supporting document retrieval and extensible command-based tool integrations.

MCP for Azure DevOps Boards is a MCP server that lets your favourite AI browse, query and update Azure DevOps work items as if it were a project manager. Written in Rust and optimized for tokens usagem, It runs via stdio or HTTP mode and uses standard Azure authentication with az login.

A lightweight RAG system that provides an MCP server for searching and interacting with vector-based knowledge bases. It enables users to perform retrieval-augmented generation and search across Qdrant collections through a standardized interface.

A proof of concept MCP server for Apache OFBiz

Production-ready MCP server providing RAG, hierarchical memory, and 8+ tools for AI agents via the Model Context Protocol.

Google Chat MCP server that lets AI assistants like Claude and Cursor participate directly in team conversations - search messages, help teammates, share files, and coordinate across chat platforms.

Enables asking questions and performing semantic searches against an Inkeep knowledge base. Deployable to Vercel with Next.js.

A command-line interface application that enables interactive chat with AI models through the Anthropic API, supporting document retrieval, command-based prompts, and extensible tool integrations.

An Operit-compatible adapter of the Exa MCP server, enabling web search, code search, and company research capabilities in AI assistants. It fixes MCP handshake compatibility issues, allowing tools like web_search_exa and web_fetch_exa to load and run reliably.

MCP server that enables searching a TOTVS Protheus ERP knowledge base using vector similarity and reranking.

Enables AI agents to maintain persistent, searchable two-layer memory with 37 tools, hybrid search, knowledge graphs, and enterprise features like authentication and backups.

Enables querying PDF documents using natural language with grounded answers and source citations via a local RAG pipeline.

A Python-based MCP server that enables document-based question answering by processing PDF, TXT, and Markdown files through OpenAI's API. It provides hallucination-free responses based strictly on document content using semantic search and includes a web interface for management.

Enables document-based question answering using OpenAI's GPT-4 with semantic search and embeddings. Upload PDF, TXT, or Markdown files and get answers strictly based on document content with source attribution and confidence scores.

This MCP server provides a cost-optimized personal-knowledge brain, enabling AI agents to query imported documents via hybrid search and synthesis at minimal monthly spend.