todo-for-ai

Todo for AI provides an MCP-based MCP server that enables AI assistants to manage tasks and projects within the Todo for AI ecosystem. The MCP server exposes an API surface that AI agents can interact with to create, update, and query projects and tasks, allowing natural language interactions such as creating a project, adding a task, or querying progress. To integrate, run the MCP server and configure your MCP client (e.g., Claude, GPT, or another AI assistant) to connect to the server using a configuration block that points to the server and provides the necessary API endpoint and authentication details. The included example shows how to configure a local MCP setup with the server exposed via the Todo for AI API and how an AI agent can invoke common actions via natural language prompts.

Prerequisites:

• Node.js and npm installed on your machine
• Internet access to fetch packages from npm

Installation steps:

1. Install the MCP package globally (or install locally in your project):

# Global installation
npm install -g @todo-for-ai/mcp

# Or local installation
npm install @todo-for-ai/mcp

2. Run the MCP server using a configuration file (config.json) or inline arguments. A minimal run with the example configuration:

todo-for-ai-mcp --config config.json

If you prefer the Quick Start approach used by Todo for AI, you can also start via npx as shown in the MCP configuration:

npx -y @todo-for-ai/mcp

3. Ensure the API endpoint is reachable and your environment variables are set (see config in mcp_config). If you’re following the Docker-based deployment, refer to the Docker instructions in the project for containerized setup.

Notes and tips:

• Environment variables: Keep TODO_API_URL and TODO_API_KEY secure; do not expose them in public endpoints.
• MCP integration: The MCP server expects a standard MCP configuration block. Adjust the env values to point to your Todo for AI API and authentication credentials.
• Networking: When running locally, ensure the API URL (TODO_API_URL) is accessible to the MCP server and any AI agents you connect.
• Authentication: Use a strong API key or token per your security policy; rotate keys periodically.
• Debugging: If the MCP server isn’t responding, check that Node.js runtime is functioning, dependencies are installed, and that the API endpoint is reachable from the server environment.
• Ports and exposure: If running in Docker or cloud environments, map ports appropriately and consider securing traffic with TLS where possible.