AI-powered chat system with multiple MCP servers.

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

FAQ from AI-powered chat system with multiple MCP servers.

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).

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.

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.

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.

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.