How do I use Task Tracker MCP Server?

1. Click on "Deploy Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Task Tracker MCP Server Add a task to buy groceries" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

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Task Tracker MCP Server

by azamafridi23

Overview Schema Source Related Servers Discussions Admin

Python

Remote

📋 Task Tracker MCP Server

A practical Model Context Protocol (MCP) server built in Python using FastMCP and managed with uv. This server provides AI assistants (like Claude Desktop, Cursor, Antigravity, etc.) with structured task management capabilities.

🌟 Overview

The Model Context Protocol (MCP) is an open standard that allows LLMs and AI applications to interact safely and seamlessly with external tools and data sources.

This project implements the three core MCP primitives:

🛠️ Tools: Callable functions allowing the model to perform actions (add_task, complete_task, delete_task).
📦 Resources: Read-only data URIs allowing the model to inspect state (tasks://all, tasks://pending).
💡 Prompts: Predefined prompt templates that guide the AI to perform complex workflows (e.g., task analysis & prioritization).

flowchart LR
    Host["AI Host / Application<br/>(Claude Desktop / Cursor / Antigravity)"] 
    Client["MCP Client<br/>(Protocol Handler)"]
    Server["Task Tracker MCP Server<br/>(FastMCP)"]
    
    Host <--> Client
    Client <--> Server
    
    subgraph ServerCapabilities ["Server Capabilities"]
        Tools["🛠️ Tools<br/>add_task, complete_task, delete_task"]
        Resources["📦 Resources<br/>tasks://all, tasks://pending"]
        Prompts["💡 Prompts<br/>task_summary_prompt"]
    end
    
    Server --- ServerCapabilities

Related MCP server: MCP Task Management Server

🚀 Features & MCP Primitives

1. Tools (Actions)

Tool	Arguments	Description
`add_task`	`title: str`, `description: str = ""`	Adds a new task with a unique ID and ISO timestamp.
`complete_task`	`task_id: int`	Marks a task status as `"completed"` and adds a completion timestamp.
`delete_task`	`task_id: int`	Removes a task by ID and returns the deleted object.

2. Resources (Read-Only Data)

Resource URI	Description
`tasks://all`	Formats and returns all tasks with emojis (`✅` for completed, `⏳` for pending).
`tasks://pending`	Filters and returns only active/pending tasks.

3. Prompts (Guided Workflows)

Prompt	Description
`task_summary_prompt`	Guides the AI assistant to analyze pending vs completed tasks, identify overdue items, and recommend next actions using `tasks://all`.

📦 Getting Started with `uv`

This project is built and managed with uv, an extremely fast Python package and project manager written in Rust by Astral.

1. Install `uv`

macOS / Linux:

curl -LsSf https://astral.sh/uv/install.sh | sh

Windows:

powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

Verify installation:

uv --version

2. Clone and Setup Repository

git clone https://github.com/<your-username>/task-tracker-mcp.git
cd task-tracker-mcp

3. Install Dependencies

uv will automatically create a virtual environment (.venv) and install all required dependencies:

uv sync

🧪 Testing the Server

You can run the built-in async client (test_client.py) which exercises every tool, resource, and prompt:

uv run test_client.py

Expected Output:

🚀 Starting FastMCP Test Client...
==================================================

1. Listing Available Tools:
   Found 3 tools: ['add_task', 'complete_task', 'delete_task']

2. Calling 'add_task' Tool:
   Task 1 Response: {"id":1,"title":"Learn MCP", ...}
   Task 2 Response: {"id":2,"title":"Master uv", ...}

3. Listing Available Resources:
   Found 2 resources: [AnyUrl('tasks://all'), AnyUrl('tasks://pending')]

4. Reading 'tasks://all' Resource:
   Current Tasks:
   ⏳ [1] Learn MCP
   ⏳ [2] Master uv

5. Completing Task ID 1:
   Completed Result: {"id":1, "status":"completed", ...}

6. Reading 'tasks://pending' Resource:
   Pending Tasks:
   ⏳ [2] Master uv

7. Listing Available Prompts:
   Found 1 prompts: ['task_summary_prompt']

8. Deleting Task ID 2:
   Delete Result: {"success": true, "deleted": {"id": 2, ...}}

==================================================
✨ All MCP Server tests completed successfully!

🔌 Connecting & Inspecting

1. FastMCP CLI Inspector & Dev Tools

FastMCP 3.x provides built-in CLI commands to inspect and debug your server:

Interactive Web Inspector:

uv run fastmcp dev inspector task_server.py

Inspect Server Summary:
```
uv run fastmcp inspect task_server.py
```
List All Tools:
```
uv run fastmcp list task_server.py
```
Run Standalone Server:
```
uv run fastmcp run task_server.py
```

2. Claude Desktop Integration

Add the server configuration to your claude_desktop_config.json:

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

{
  "mcpServers": {
    "task-tracker": {
      "command": "uv",
      "args": [
        "--directory",
        "/ABSOLUTE/PATH/TO/task-tracker-mcp",
        "run",
        "task_server.py"
      ]
    }
  }
}

📁 Project Structure

Task Tracker MCP/
├── pyproject.toml         # Dependency & project metadata (managed by uv)
├── task_server.py         # MCP Server definitions (tools, resources, prompts)
├── test_client.py         # FastMCP async automated test client
├── src/
│   └── task_tracker_mcp/  # Python package entrypoint
│       └── __init__.py
├── .python-version        # Locked Python version
├── .gitignore             # Python & uv exclusions
└── README.md              # Documentation

💡 Key `uv` Commands Cheat Sheet

Command	Description
`uv init`	Initialize a new Python project with `pyproject.toml`
`uv add <pkg>`	Add a dependency to `pyproject.toml` and install it in `.venv`
`uv remove <pkg>`	Remove a dependency
`uv run <script.py>`	Run any Python script within the isolated project environment
`uv sync`	Sync installed packages with `uv.lock`
`uv venv`	Create a virtual environment explicitly

🛠️ Next Steps & Extensions

Persistent Storage: Replace in-memory list with SQLite via aiosqlite or sqlite3.
Priority & Due Dates: Add task priority flags (low, medium, high) and due date filters.
Search Tool: Add a search_tasks(query: str) tool to search title and descriptions.
Authentication: Secure endpoints with FastMCP auth providers.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

Available Tools

3 tools

add_taskC

Add a new task to the task list.

ParametersJSON Schema

Name	Required	Description	Default
`title`	Yes
`description`	No

Output Schema

ParametersJSON Schema

Name	Required	Description
No output parameters

TDQS

C2.4/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full behavioral burden. 'Add' implies a mutation, but nothing is said about required permissions, duplicate handling, defaults for description, or state side effects — a significant gap for a write tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence with no wasted words, but it is under-specified rather than appropriately concise for a mutation tool with two undocumented parameters.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists, so return values need not be described. Still, with no annotations and 0% parameter coverage, the definition leaves an agent without enough behavioral or parameter context to reliably invoke a write operation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% and the description never mentions any parameter. It does not clarify that 'title' is required or that 'description' defaults to an empty string, so it adds no parameter meaning beyond the raw schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a clear verb ('Add') and resource ('a new task to the task list'), so the agent can tell it apart from delete_task and complete_task by operation. However, it offers no differentiation language or scope detail beyond the obvious create/delete/complete split.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No when-to-use guidance, no prerequisites, and no mention of alternatives such as complete_task for status changes. The agent gets a bare purpose statement and must infer all invocation context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

complete_taskC

Mark a task as completed.

ParametersJSON Schema

Name	Required	Description	Default
`task_id`	Yes

Output Schema

ParametersJSON Schema

Name	Required	Description
No output parameters

TDQS

C2.8/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations are absent, so the description carries the full behavioral burden. It conveys that this is a state mutation but omits whether the call is idempotent, what happens if the task is already completed, permission requirements, and whether the change is reversible.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence with zero filler, but so terse that it omits information an agent needs rather than being efficiently dense.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool is simple, one required integer parameter, and an output schema exists so return values need not be described. Still, a mutation with no annotations and no parameter documentation leaves gaps around idempotency and error conditions.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate for the undocumented task_id. It only implies 'a task' without stating the identifier format, valid range, or behavior when the id does not exist.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource: 'Mark a task as completed.' An agent immediately knows the operation. However, it offers no differentiation from siblings like delete_task or add_task, which are the plausible confusions for a task-status mutation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No when-to-use guidance, no mention of alternatives, and no note on the distinction between completing and deleting a task. Usage is only weakly implied by the verb.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

delete_taskC

Delete a task from the list.

ParametersJSON Schema

Name	Required	Description	Default
`task_id`	Yes

Output Schema

ParametersJSON Schema

Name	Required	Description
No output parameters

TDQS

C2.6/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. "Delete" implies a destructive mutation but the description says nothing about irreversibility, required permissions, or what happens to the task afterward — significant gaps for a destructive tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single short sentence with no waste and the action front-loaded, which suits a one-parameter tool. However, the brevity comes at the cost of substance rather than being a model of economical communication.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists, so return values need not be explained. But for a destructive mutation with zero annotations and an undocumented parameter, the description omits the safety and side-effect context an agent needs before invoking it.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% and the description adds no meaning for task_id beyond what the name implies. The single required integer parameter is nearly self-evident, but nothing explains its origin or format, so the description fails to compensate for the schema gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ("Delete a task"), which is unambiguous against siblings add_task and complete_task, whose verbs already differ. It stops short of explicitly naming those siblings or the scope of deletion, but an agent can distinguish it readily.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No when-to-use guidance, no prerequisites, and no alternatives named. The agent gets no signal about when deleting is appropriate versus completing or leaving a task alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

3 tool updatesv0.1.0Sep 11, 2026
- First observedadd_task
- First observedcomplete_task
- First observeddelete_task

Deploy Server

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TDQS

B3/5.0

Scored 2026-09-11 09:05 across 3 tools

Disambiguation5/5

Each tool targets a clearly distinct action on a task: add, delete, complete. There is no overlap or ambiguity between them, so an agent can easily select the right tool.

Naming Consistency5/5

All three tools follow the exact same verb_noun snake_case pattern (add_task, delete_task, complete_task). Naming is fully predictable.

Tool Count4/5

Three tools is a reasonable, well-scoped set for a minimal task tracker, and each earns its place. It is slightly thin, since a small surface like this leaves little room for the read/lifecycle operations expected of the domain.

Completeness3/5

The set covers create, delete, and a status update, but there is no way to list or retrieve tasks, which is arguably the most essential operation for a tracker. This is a notable gap that will leave agents unable to inspect the task list.

Maintenance

ActivitySlowing

ResponsivenessNo issues

Resources

GitHub Repository

Need Help?

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📋 Task Tracker MCP Server

🌟 Overview

🚀 Features & MCP Primitives

1. Tools (Actions)

2. Resources (Read-Only Data)

3. Prompts (Guided Workflows)

📦 Getting Started with uv

1. Install uv

2. Clone and Setup Repository

3. Install Dependencies

🧪 Testing the Server

Expected Output:

🔌 Connecting & Inspecting

1. FastMCP CLI Inspector & Dev Tools

2. Claude Desktop Integration

📁 Project Structure

💡 Key uv Commands Cheat Sheet

🛠️ Next Steps & Extensions

📄 License

Available Tools

Output Schema

Output Schema

Output Schema

Tool Schema Changelog

Maintenance

Resources

Related MCP Connectors

Related MCP Servers

📦 Getting Started with `uv`

1. Install `uv`

💡 Key `uv` Commands Cheat Sheet