Alice MCP Server
by soapko
Alice MCP is a lightweight, local server designed for agile task management in AI coding environments. It provides a backend for managing projects, tasks, epics, and messages locally, featuring project isolation.
Last updated: N/A
What is Alice MCP Server?
Alice MCP is a local server that supports agile task workflows within AI coding environments using the Model Context Protocol (MCP). It manages projects, tasks, epics, and messages locally with project isolation.
How to use Alice MCP Server?
To use Alice MCP, clone the repository, set up a Python virtual environment, install dependencies, and start the server either automatically through MCP integration or manually. Interact with the server via API endpoints to create projects, tasks, and epics.
Key features of Alice MCP Server
Project Management
Task & Epic Tracking
Message Logging
Project Isolation
Local Operation
MCP Integration
Use cases of Alice MCP Server
Managing software development projects
Tracking tasks and progress within AI coding environments
Organizing work using epics
Logging messages and notes related to tasks
FAQ from Alice MCP Server
How do I create a new project?
How do I create a new project?
Send a POST request to /projects/ with a JSON payload containing the project name, e.g., { "name": "my-new-project" }.
How do I interact with tasks, epics, and messages?
How do I interact with tasks, epics, and messages?
When using the Alice MCP server tools, provide the project's name as the project_id argument. When using the FastAPI backend directly, use the numeric project_id in the URL path.
How do I run the server manually?
How do I run the server manually?
Start the FastAPI server using uvicorn app.main:app --reload. The server will be available at http://127.0.0.1:8000.
Where can I find the API documentation?
Where can I find the API documentation?
Access the interactive API documentation at http://127.0.0.1:8000/docs (Swagger UI) or http://127.0.0.1:8000/redoc (ReDoc).
How do I run the tests?
How do I run the tests?
Ensure you have the virtual environment activated and dependencies installed. Then run pytest.