Task Tracker MCP Server
📋 Servidor MCP de Seguimiento de Tareas
Un servidor práctico del Protocolo de Contexto de Modelo (MCP) construido en Python usando FastMCP y gestionado con uv. Este servidor proporciona a los asistentes de IA (como Claude Desktop, Cursor, Antigravity, etc.) capacidades estructuradas de gestión de tareas.
🌟 Descripción General
El Protocolo de Contexto de Modelo (MCP) es un estándar abierto que permite a los LLMs y aplicaciones de IA interactuar de manera segura y sin problemas con herramientas y fuentes de datos externas.
Este proyecto implementa las tres primitivas principales de MCP:
🛠️ Herramientas: Funciones invocables que permiten al modelo realizar acciones (
add_task,complete_task,delete_task).📦 Recursos: URIs de solo lectura que permiten al modelo inspeccionar el estado (
tasks://all,tasks://pending).💡 Indicaciones: Plantillas de indicaciones predefinidas que guían a la IA para realizar flujos de trabajo complejos (por ejemplo, análisis y priorización de tareas).
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 --- ServerCapabilitiesRelated MCP server: MCP Task Management Server
🚀 Funcionalidades y Primitivas MCP
1. Herramientas (Acciones)
Herramienta | Argumentos | Descripción |
|
| Añade una nueva tarea con un ID único y una marca de tiempo ISO. |
|
| Marca el estado de una tarea como |
|
| Elimina una tarea por ID y devuelve el objeto eliminado. |
2. Recursos (Datos de Solo Lectura)
URI del Recurso | Descripción |
| Da formato y devuelve todas las tareas con emojis ( |
| Filtra y devuelve solo las tareas activas/pendientes. |
3. Indicaciones (Flujos de Trabajo Guiados)
Indicación | Descripción |
| Guía al asistente de IA para analizar tareas pendientes vs completadas, identificar elementos vencidos y recomendar próximas acciones usando |
📦 Primeros Pasos con uv
Este proyecto está construido y gestionado con uv , un gestor de proyectos y paquetes de Python extremadamente rápido escrito en Rust por Astral.
1. Instalar uv
macOS / Linux:
curl -LsSf https://astral.sh/uv/install.sh | shWindows:
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"Verificar la instalación:
uv --version2. Clonar y Configurar el Repositorio
git clone https://github.com/<your-username>/task-tracker-mcp.git
cd task-tracker-mcp3. Instalar Dependencias
uv creará automáticamente un entorno virtual (.venv) e instalará todas las dependencias requeridas:
uv sync🧪 Probando el Servidor
Puedes ejecutar el cliente asíncrono integrado (test_client.py) que ejercita cada herramienta, recurso e indicación:
uv run test_client.pySalida Esperada:
🚀 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!🔌 Conectando e Inspeccionando
1. Inspector CLI y Herramientas de Desarrollo de FastMCP
FastMCP 3.x proporciona comandos CLI integrados para inspeccionar y depurar tu servidor:
Inspector Web Interactivo:
uv run fastmcp dev inspector task_server.pyInspeccionar Resumen del Servidor:
uv run fastmcp inspect task_server.pyListar Todas las Herramientas:
uv run fastmcp list task_server.pyEjecutar Servidor Independiente:
uv run fastmcp run task_server.py
2. Integración con Claude Desktop
Añade la configuración del servidor a tu 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"
]
}
}
}📁 Estructura del Proyecto
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💡 Hoja de Referencia de Comandos Clave de uv
Comando | Descripción |
| Inicializa un nuevo proyecto de Python con |
| Añade una dependencia a |
| Elimina una dependencia |
| Ejecuta cualquier script de Python dentro del entorno del proyecto aislado |
| Sincroniza los paquetes instalados con |
| Crea un entorno virtual explícitamente |
🛠️ Próximos Pasos y Extensiones
Almacenamiento Persistente: Reemplazar la lista en memoria con SQLite mediante
aiosqliteosqlite3.Prioridad y Fechas de Vencimiento: Añadir indicadores de prioridad de tareas (
baja,media,alta) y filtros de fecha de vencimiento.Herramienta de Búsqueda: Añadir una herramienta
search_tasks(query: str)para buscar en títulos y descripciones.Autenticación: Asegurar los endpoints con proveedores de autenticación de FastMCP.
📄 Licencia
Este proyecto está licenciado bajo la Licencia MIT; consulte el archivo LICENSE para más detalles.
Available Tools
3 toolsadd_taskC
Add a new task to the task list.
| Name | Required | Description | Default |
|---|---|---|---|
| title | Yes | ||
| description | No |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
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.
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.
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.
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.
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.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| task_id | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
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.
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.
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.
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.
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.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| task_id | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
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.
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.
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.
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.
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.
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 updates
v0.1.0- First observed
add_task - First observed
complete_task - First observed
delete_task
TDQS
Scored across 3 tools
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.
All three tools follow the exact same verb_noun snake_case pattern (add_task, delete_task, complete_task). Naming is fully predictable.
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.
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.
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