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AI-powered chat system with multiple MCP servers.

@kira1228

Repository

Basic information

Category

AI & Agents

Last updated

License

MIT

Transports

stdio

Author

kira1228

About AI-powered chat system with multiple MCP servers.

AI-powered Chat System with multiple MCP servers.

AI-powered chat system with multiple MCP servers. config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "mcp-chat-system": {
      "command": "uv",
      "args": [
        "venv"
      ]
    }
  }
}

AI-powered chat system with multiple MCP servers. tools

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Overview

What is AI-powered chat system with multiple MCP servers.?

This system is a client‑server architecture based on the Model Context Protocol (MCP). It connects a host application (Claude) with three specialized MCP servers: Filesystem (access local folders), Slack (access workspace conversations), and Brave‑Search (real‑time web search). It is intended for users who want to query local files, Slack history, and the web through a single AI interface.

How to use AI-powered chat system with multiple MCP servers.?

Install the uv package manager, clone the repository, create a .env file with your API keys (ANTHROPIC_API_KEY, SLACK_BOT_TOKEN, SLACK_TEAM_ID, BRAVE_API_KEY), create a virtual environment with uv venv, activate it, and install dependencies with uv sync. Run the client with uv run client.py path/to/dir/you/want/to/use. The system automatically selects the appropriate MCP server based on your query.

Key features of AI-powered chat system with multiple MCP servers.

  • Supports three MCP servers: Filesystem, Slack, and Brave‑Search
  • Intelligent routing – Claude chooses the server automatically
  • Works with local file directories, Slack workspaces, and web search
  • Requires minimal setup with uv and a .env file
  • Follows the MCP client‑server architecture
  • Open source under the MIT License

Use cases of AI-powered chat system with multiple MCP servers.

  • Search and retrieve information from your local documents and folders
  • Access and reference Slack conversations, channels, and shared resources
  • Perform real‑time web searches to incorporate the latest online information
  • Combine local, team, and web data in a single AI chat session

Frequently asked questions

1

What API keys are required?

You need four keys: `ANTHROPIC_API_KEY` (for Claude), `SLACK_BOT_TOKEN` and `SLACK_TEAM_ID` (for Slack integration), and `BRAVE_API_KEY` (for Brave web search).

2

What runtime and dependencies are needed?

Python and the `uv` package manager. The project uses Python virtual environments and a `pyproject.toml` managed by `uv`.

3

How does the system decide which server to use?

Claude automatically analyzes your query and determines whether to search local files, check Slack history, or perform a web search – no explicit instruction required.

4

Where should I place the files I want to search?

You provide the directory path as a command‑line argument when running `client.py`. The Filesystem server will search that folder and its subfolders.

5

What transport or authentication does the system use?

The client runs locally over stdio. Authentication is handled via environment variables (API keys and tokens); no additional transport configuration is described in the README.

Comments

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