MCP Server

A small task manager that AI agents can use through the Model Context Protocol (MCP). An agent can create tasks, list tasks, and update task status. The tasks are stored in a local SQLite file.

This is a learning project. It shows every layer of an MCP application in five small Python files, from the SQL statements to a chat agent.

I built this project by following the video Build MCP Server for AI application (Full Project, step-by-step).

What MCP does in this project

Without MCP, each AI application needs its own custom code for each tool. With MCP, a program publishes its tools once, and any MCP client can discover and call them.

In this project, three different clients use one set of tools:

flowchart LR
    A["client.py<br/>fixed Python calls"] -->|MCP over HTTP| S
    B["langchain_client.py<br/>Claude agent"] -->|MCP over HTTP| S
    C["MCP hosts<br/>configured by .mcp.json"] -->|MCP over HTTP| S
    S["server.py<br/>tool definitions"] --> D["database.py<br/>SQL"]
    D --> F[("data/tasks.db")]

Related MCP server: Productivity Tracker MCP Server

Project structure

.
├── src/mcp_server/
│   ├── __init__.py          # Package overview and a placeholder main()
│   ├── database.py          # SQLite functions. All the SQL is in this file
│   ├── server.py            # The MCP server and its three tools
│   ├── client.py            # A script that calls the tools without an LLM
│   └── langchain_client.py  # A terminal chat with a Claude agent
├── .env.template            # The environment variable that the agent needs
├── .mcp.json                # The server address for MCP hosts
├── LICENSE                  # The MIT License
├── pyproject.toml           # Project metadata and dependencies
└── uv.lock                  # Exact dependency versions

The database file, data/tasks.db, and your .env file are not in Git.

If you are new to this code, read the files in this order: database.py, server.py, client.py, and langchain_client.py. Each file starts with a docstring that explains its job and how to run it.

Set up and run the project

You need uv, which installs the correct Python version (3.12) for you. The chat agent also needs an Anthropic API key.

1. Clone the repository and install the dependencies.

   git clone https://github.com/AdamXiang/MCP_Server.git
   cd MCP_Server
   uv sync

2. Create the database and one sample task. Run this command only once, because a second run stops with a UNIQUE constraint failed error.

   uv run python -m mcp_server.database

   You see the sample task:

   [{'task_id': '1', 'title': 'new task', 'description': 'test', 'priority': 'low', 'status': 'todo'}]

3. Start the server. The server keeps running in this terminal, at http://127.0.0.1:8000/mcp.

   uv run python -m mcp_server.server

4. Open a second terminal and run the test client. The client lists the tools, creates a task, and sets the sample task to in progress.

   uv run python -m mcp_server.client

   The output starts with the tool names:

   create_task
   list_tasks
   update_task_status

5. Optional: chat with the agent. To do this, copy the template and add your API key to .env. Each model call uses your Anthropic API credits.

   cp .env.template .env
   uv run python -m mcp_server.langchain_client

   Try "Add a high priority task to write tests". Type q to exit.

To stop the server, press Ctrl+C in its terminal. To delete all the tasks, stop the server, delete data/tasks.db, and repeat step 2.

Connect an MCP host

.mcp.json tells an MCP host, such as Claude Code, where the server is. Claude Code reads this file from the project root and asks you to approve the server. Start the server before you use its tools from the host.

Tools reference

Task fields

All the columns in the tasks table have the type TEXT, and none of them can be null.

Future work

I found these problems by running the current code. None of them is fixed yet. The list starts with the problems that a new user meets first.

Technologies

• FastMCP for the MCP server and the test client.

• LangChain and LangGraph for the chat agent.

• Claude (claude-haiku-4-5) for the chat agent model.

• SQLite for storage, through the Python standard library.

• uv for dependencies and for running the code.

Acknowledgments

The idea and the structure of this project come from the video Build MCP Server for AI application (Full Project, step-by-step).

License

This project uses the MIT License.