Todo, priority, estimation, burndown for AI agents
Valid MCP server (2 strong, 4 medium validity signals). 2 known CVEs in dependencies (0 critical, 2 high severity) Package registry verified. Imported from the Official MCP Registry.
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Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-rog0x-task": {
"args": [
"-y",
"@rog0x/mcp-task-tools"
],
"command": "npx"
}
}
}From the project's GitHub README.
MCP server providing task and project management tools for AI agents.
In-memory task list management. Add, complete, update status, delete, and list tasks with filtering by status, priority, and tags. Data persists within the session.
Sort tasks using established prioritization frameworks:
Estimate task duration using PERT methodology. Break work into subtasks, provide optimistic/realistic/pessimistic estimates, and get weighted expected time with configurable buffer.
Generate daily standup updates from the current task list. Outputs yesterday (completed), today (in progress), and blockers with summary statistics.
Calculate sprint burndown data. Given tasks with story points and sprint duration, computes ideal vs actual burndown, velocity metrics, and completion forecast.
npm install
npm run build
Add to your Claude Desktop config:
{
"mcpServers": {
"task-tools": {
"command": "node",
"args": ["path/to/mcp-task-tools/dist/index.js"]
}
}
}
MIT
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