Task Manager MCP Server

A Python MCP server that demonstrates all 3 MCP primitives — built as a portfolio project after completing the Anthropic MCP course.

What's inside

Related MCP server: task-mcp-node

Setup

1. Clone / copy this project

git clone <your-repo-url>
cd task-manager-mcp

2. Create a virtual environment

python -m venv .venv
source .venv/bin/activate      # Windows: .venv\Scripts\activate

3. Install dependencies

pip install -r requirements.txt

4. Run the server

python server.py

Test with the MCP Inspector

The MCP Inspector lets you test all your tools, resources, and prompts in the browser — no client needed.

mcp dev server.py

Then open http://localhost:5173 in your browser.

From there you can:

• Call any tool and see the response

• Read any resource by URI

• Preview and run any prompt

Connect to Claude Desktop

Add this to your Claude Desktop config file:

Mac: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "task-manager": {
      "command": "python",
      "args": ["/absolute/path/to/task-manager-mcp/server.py"]
    }
  }
}

Restart Claude Desktop — you'll see the task manager tools available in the chat.

Example conversations with Claude

Once connected, try these:

Using tools:

"Create a high-priority task: finish portfolio README, due 2025-07-01"

"What tasks do I have pending?"

"Mark task abc12345 as complete"

Using resources:

"Read tasks://summary and tell me how I'm doing"

"Show me the details of task abc12345 using its resource URI"

Using prompts:

Run the daily_planning prompt to get your morning briefing

Run end_of_day_review in the evening

Project structure

task-manager-mcp/
├── server.py          # All MCP logic — tools, resources, prompts
├── requirements.txt   # Single dependency: mcp[cli]
├── README.md
└── tasks/
    └── tasks.json     # Auto-created on first task

Key concepts demonstrated

Tools (model-controlled)

Claude decides when to call these based on what the user asks. The decorator pattern means you write a plain Python function — no JSON schema needed:

@mcp.tool()
def create_task(title: str, priority: str = "medium") -> str:
    ...

Resources (app-controlled)

Exposed as URIs. Claude can read these to ground its responses in real data:

@mcp.resource("tasks://summary")
def get_task_summary() -> str:
    ...

Templated resources use {variable} in the URI:

@mcp.resource("tasks://{task_id}")
def get_task_by_id(task_id: str) -> str:
    ...

Prompts (user-controlled)

Pre-crafted conversation starters. They read live data and return a structured message:

@mcp.prompt()
def daily_planning() -> str:
    # reads current tasks, builds a structured prompt string
    ...

Built with FastMCP · Anthropic MCP course graduate project