AI Assistant Hub Server

AI Assistant Hub is a production-ready Model Context Protocol (MCP) server that exposes a collection of tools (like Weather, GitHub, and Slack) to any MCP-compatible AI assistant or client.

Getting Started

Follow these steps to set up and run the server.

1. Set Up the Python Environment

First, create a virtual environment and install the required packages.

# Create and activate a virtual environment
python -m venv .venv
source .venv/bin/activate  # On Windows, use: .venv\Scripts\activate

# Install dependencies
pip install --upgrade pip
pip install -r requirements.txt

2. Configure API Keys with a .env File

The server needs API keys for the tools it provides. The recommended way to provide them is with a .env file.

1. Create a new file named .env in the root of the project (/home/harsh/Documents/MCPServer/ai-hub/.env).
2. Copy and paste the following into the .env file, replacing the placeholder values with your actual keys.

# --- API Keys and Secrets ---

# Weather (OpenWeatherMap)
TOOL_WEATHER_CONFIG__API_KEY=your-openweathermap-api-key

# GitHub Issues (optional for public repositories)
TOOL_GITHUB_ISSUES_CONFIG__TOKEN=ghp_your_token

# Slack
TOOL_SLACK_POST_MESSAGE_CONFIG__TOKEN=xoxb-your-slack-token


# --- Tool Toggles (all enabled by default) ---
TOOL_WEATHER_ENABLED=true
TOOL_GITHUB_ISSUES_ENABLED=true
TOOL_SLACK_POST_MESSAGE_ENABLED=true

3. Run the Server

Once the environment is set up and configured, run the server from your terminal:

ai-assistant-hub

or

python3 -m ai_assistant_hub.server.main

The server will start and wait for a client to connect. You should see output like:

MCP server started with stdio transport. Waiting for client...
Tools available: ['weather', 'github_issues', 'slack_post_message']

Connecting Your LLM Client

To use the tools, you need to connect your AI assistant or LLM client to this server. Most clients support connecting to an MCP server using a JSON configuration.

1. Find your client's server configuration settings (e.g., "Tool Servers", "MCP Servers").
2. Add a new server and provide the following JSON.

{
  "mcpServers": {
    "ai-hub": {
      "command": "/home/harsh/Documents/MCPServer/ai-hub/.venv/bin/ai-assistant-hub",
      "args": [],
      "cwd": "/home/harsh/Documents/MCPServer/ai-hub",
      "env": {
        "TOOL_WEATHER_CONFIG__API_KEY": "your-openweathermap-api-key",
        "TOOL_GITHUB_ISSUES_CONFIG__TOKEN": "ghp_your_token",
        "TOOL_SLACK_POST_MESSAGE_CONFIG__TOKEN": "xoxb-your-slack-token"
      }
    }
  }
}

Important:

• Make sure the command and cwd paths in the JSON match the location of your project.
• The env block in the JSON is an alternative way to provide the API keys if you prefer not to use a .env file. You don't need to fill it out if you've already created a .env file.

3. Save and activate the new server. Your client can now use the tools.

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Project Details

Features

• ✅ Built on the official MCP server runtime from Hugging Face.
• ✅ Modular tools for Weather, GitHub Issues, and Slack.
• ✅ Resilient HTTP clients with built-in retries.
• ✅ Centralised configuration via .env files or environment variables.

Architecture

The project is structured to be modular and extensible.

• ai_assistant_hub/server/main.py: The main entrypoint that starts the server.
• ai_assistant_hub/tools/: Contains the definition for each tool.
• ai_assistant_hub/integrations/: Contains the logic for communicating with third-party APIs (like OpenWeatherMap).

Adding New Tools

1. Create an "adapter" in ai_assistant_hub/integrations/ to handle the external API communication.
2. Define a new tool in ai_assistant_hub/tools/ that uses the adapter.
3. Enable your new tool and provide its configuration in your .env file.

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Need help or want to add more adapters? Open an issue or submit a pull request!