Overview
What is Task Manager MCP Server?
Task Manager MCP Server is a task management tool that integrates with a database and uses LangChain for AI‑driven assistance. It provides APIs for adding, viewing, completing, and deleting tasks, and includes a Streamlit‑based user interface. This server is designed for developers and users who want to manage tasks programmatically or through a visual interface with AI support.
How to use Task Manager MCP Server?
Clone the repository, install dependencies with pip install -r requirements.txt, then start the MCP server by running python server.py. To use the graphical interface, run streamlit run streamlit.py. Debugging configurations are available in .vscode/launch.json.
Key features of Task Manager MCP Server
- Add, list, complete, and remove tasks.
- Streamlit‑based user interface for visual interaction.
- LangChain integration for intelligent task management.
- Tool‑based architecture connecting to external tools.
- Structured project with separate modules for server, agent, and UI.
Use cases of Task Manager MCP Server
- Rapidly build a task management application with a backend server.
- Manage tasks through an AI agent that processes natural language queries.
- Debug and test task operations using a Streamlit frontend.
- Integrate task management into larger LangChain‑powered workflows.
FAQ from Task Manager MCP Server
What does the server do?
The server manages tasks using tools connected to a database. It provides APIs for adding, viewing, completing, and deleting tasks, and integrates with LangChain for AI‑driven task management.
How do I run the server and the UI?
Start the MCP server by running python server.py. For the graphical interface, run streamlit run streamlit.py.
What are the dependencies?
All dependencies are listed in requirements.txt. Install them with pip install -r requirements.txt.
What tools are available on the server?
The server provides four tools: list_tasks (list all tasks), add_task (add a new task), remove_task (remove a task by ID), and mark_complete (mark a task as complete).
How does the AI agent work?
The AI agent in agent.py uses LangChain to process user queries and interact with the task management tools, enabling intelligent responses and task operations.
