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

Enables retrieval-augmented generation by embedding queries with a chosen provider (e.g., OpenAI) and searching supported vector stores (Pinecone, pgvector) to return relevant content.

Enables AI applications to access and contextualize organizational knowledge sources including GitHub repositories and internal documentation through standardized MCP protocol integration. Features OAuth 2.1 authentication, vector-based semantic search, and optimized context chunking for enterprise development workflows.

Enables comprehensive Slack workspace integration through AI assistants, allowing users to manage channels, send messages, upload files, search conversations, and interact with users through natural language commands.

Bridges AI agents with watsonx.data document libraries, enabling natural language queries and intelligent retrieval from document libraries.

Enables deterministic auditing of RAG retrieval faithfulness and hallucination scores, providing structured JSON output for AI agents and MCP-compliant clients.

The MCP server provides tools for entity extraction, query refinement, and relevance checking to enhance RAG applications by integrating with OpenAI and Gemini.

Enables Slack integration with tools to list channels, read messages, and post messages using a bot token.

MCP Server for the Slack API, enabling Claude to interact with Slack workspaces.

MCP server for Slack workspace integration, exposing 40+ Slack operations as tools for Claude Desktop and providing a clean async Python API.

Enables an AI assistant to act as the user in Slack through a user OAuth token, covering messaging and threads, search, users, channels, user groups and reactions, plus scheduled messages, canvases and Slack Lists. It also supports flexible channel addressing by ID, #name or @user, and matches existing tool names and parameters for seamless integration with established prompts and permission rules.

Provides LLMs full access to Slack: messages, channels, files, canvases, lists, search, reactions, and more via 220 tools and 36 API families.

Enables building AI-powered Slack apps using the Slack MCP server and OpenAI models for automated messaging and canvas creation.

Enables comprehensive interaction with Slack workspaces, allowing users to manage channels, send messages, and search history. It supports managing reminders, pins, reactions, and user information through the Model Context Protocol.

An AWS-native MCP server that posts to Slack (post_to_slack, post_file_to_slack) via an Amazon Bedrock AgentCore Gateway.

Read-only MCP server for Slack with OAuth 2.1 authentication, enabling message retrieval, thread replies, search, user and channel listing.

An evaluation harness that probes MCP-based retrieval servers for calibration, relevance, coverage, citation integrity, and more, also exposing the probes as MCP tools for assistants and CI agents.

Enables fuzzy searching and browsing of Slack messages, users, and channels via CLI or MCP server integration.

Enables AI agents to search and retrieve relevant document content from existing embeddings stored in Supabase vector database. Provides semantic search capabilities to find document chunks based on similarity to query text without generating new embeddings.

MCP server that exposes Slack Web API operations as tools, enabling users to list conversations, read history, post messages, and call any Slack Web API method.

MCP server for Slack that enables AI agents to interact with Slack workspaces, including listing channels, reading and posting messages, searching, managing users, reactions, and user groups.