"Slack Bot with RAG Document Retrieval and Automated Response System" matching MCP servers:

Enables structured team communication for Claude Code agents through Slack-like channels and direct messages. Supports project isolation, subscription management, and agent notes for sophisticated multi-agent collaboration workflows.

Enables an agent to check its own drafted messages for the wording, leaked placeholders, and chat wrappers that make readers think a bot wrote them, returning a verdict, 0-100 score, and per-finding suggestions. It also applies the safe fixes that need no judgement and reports what still requires a human or model rewrite before anything is sent.

MCP server for posting messages to Slack channels via webhooks or bot API. Supports configurable usernames, emojis, and both webhook and bot token authentication modes.

MCP coordination primitive for multi-agent workflows. Lets multiple AI coding agents (Claude Code, Cursor, n8n, custom clients) discover each other and exchange messages and tasks via a shared SQLite-backed message bus.

A Model Context Protocol server implementation that enables AI assistants to interact with Slack workspaces, allowing them to browse channels, send messages, reply to threads, add reactions, and retrieve user information.

Enables self-hosted AI gateway and agent control plane with governed MCP tools, virtual-key budgets, caching, and audit, supporting OpenAI, Anthropic, Gemini, MCP, and A2A protocols. It provides deterministic prompt enhancement and governed execution with a zero-credential first run.

Slack Agent Mesh is an open-source MCP server that turns a single Slack app into a mesh of virtual agent identities, providing a transport and coordination layer where agents can be addressed by name and collaborate through durable, human-visible Slack threads.

Provides a standardized protocol for tool invocation, enabling an AI system to search the web, retrieve information, and provide relevant answers through integration with LangChain, RAG, and Ollama.

MCP server providing tools for entity extraction, query refinement, and relevance checking to build Agentic RAG applications.

An agentic AI system that orchestrates multiple specialized AI tools to perform business analytics and knowledge retrieval, allowing users to analyze data and access business information through natural language queries.

Human-in-the-loop approval gate for MCP agents, classifying tool calls by risk and requiring Slack-based human approval for medium/high risk actions.

Implements a RAG workflow that integrates with any custom knowledge base and can be triggered directly from the Cursor IDE.

Listens for Slack @mentions and publishes them as real-time events to the KĀDI event bus, enabling event-driven architectures with low latency.

Connects a RAG application to open-webui using Model Context Protocol (MCP), enabling server-to-client communication for context retrieval and tool usage in remote environments through Server-Sent Events (SSE).

Enables personalized AI tutoring by allowing students to upload PDF/DOCX study materials that are processed and indexed for semantic search. Provides intelligent responses based on the student's own learning materials using RAG technology.

Enables building and evaluating LangGraph agents over a protocol-level MCP server to measure failure rates across four agent topologies.

Domain-Driven MCP server for ed-tech workflows, exposing validated MCP tools backed by LangGraph agents, Supabase document retrieval, web search, and YouTube video discovery.

Cryptographic identity and trust protocol for AI agents. 38 MCP tools across 8 protocol layers: Ed25519 identity, delegation chains, values compliance, signed communication, policy engine, task coordination, cross-layer integration, and agentic commerce. 264 tests passing.

Enables agents to search across the pooled tools of many connected MCP servers and invoke them through a single endpoint, returning only the handful of tools relevant to each request. Filters by the caller's permissions before ranking, so agents never see or plan around tools their user is not allowed to use.

Enables AI assistants to perform retrieval-augmented generation with hybrid search, reranking, multi-modal image processing, and RAG evaluation through standardized MCP tools.