What are the prerequisites to run the server?
Python 3.12+ is required, along with API keys for an LLM provider (OpenAI, OpenRouter, or Ollama). Docker is recommended but optional.
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Basic information
A task management MCP server that provides comprehensive project and task tracking capabilities
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"task-manager-mcp": {
"command": "uv",
"args": [
"pip",
"install",
"-e",
"."
]
}
}
}Tool list not available yet
This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.
We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
A template implementation of the Model Context Protocol (MCP) server for managing tasks and projects, providing task management, project organization, task tracking, and PRD parsing. It enables AI agents to manage tasks, track project progress, and break down Product Requirements Documents (PRDs) into actionable tasks. Serves as a practical template for building custom MCP servers with task management capabilities.
Install using uv or Docker, configure environment variables in a .env file (including TRANSPORT, HOST, PORT), then run with python3 src/main.py or the Docker container. Use the provided tools (e.g., create_task_file, add_task, update_task_status, parse_prd) to manage projects and tasks programmatically.
create_task_file, add_task, update_task_status, get_next_task for task managementparse_prd converts PRDs into structured tasks automaticallyexpand_task breaks tasks into smaller subtasksestimate_task_complexity and get_task_dependencies for project planninggenerate_task_file creates file templates from task descriptionssuggest_next_actions provides AI‑powered next‑step recommendationsPython 3.12+ is required, along with API keys for an LLM provider (OpenAI, OpenRouter, or Ollama). Docker is recommended but optional.
Set the `TRANSPORT` environment variable to `sse` or `stdio`. For SSE, also set `HOST` and `PORT` (default `0.0.0.0:8050`).
Use the provided JSON configuration for either SSE (URL `http://localhost:8050/sse`) or stdio (command `python3 src/main.py` with environment variables).
Yes. The template is designed to be extended—add custom tools with the `@mcp.tool()` decorator, implement project‑specific templates, or integrate with existing development tools.
The README does not specify a storage backend; data persistence is not described.

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