"task management" matching MCP servers:

Enables comprehensive task management with groups, custom statuses, and task relationships, designed for AI assistants to manage tasks during conversations.

A local, read-only MCP server (plus CLI) over your own stock/ETF transaction log. A deterministic core computes drawdown-first risk with bootstrap confidence intervals and validates targets with walk-forward verdicts. The assistant narrates, but the number fence makes it structurally impossible for the model to produce a figure.

Enables log analysis for Tenable Patch Management (SaaS and on-prem) by parsing, de-duplicating, and decoding server/client logs into structured verdicts, ranked issues, and known-issue fixes.

Enables AI agents to autonomously manage and improve execution processes for repetitive task types by storing reusable task contexts with associated artifacts (practices, rules, prompts, learnings) and providing full-text search across historical best practices.

A task-board MCP server that provides tools to search, create, and update tasks, resources for reading the board, and prompts for standup and triage workflows.

A cross-AI-agent local task context sharing MCP server that allows clients like Codex, Claude Code, Hermes, and VS Code to read and write the same task state, enabling task tracking and context distillation.

MCP server for managing a task graph with live visualization. Enables Claude Code to create, update, and link nodes (tasks, subtasks, ideas, etc.) while a browser viewer displays the graph in real time.

Provides task and project management tools for AI agents, including todo list management, prioritization frameworks like Eisenhower Matrix and RICE scoring, time estimation using PERT, daily standup generation, and sprint burndown calculation.

A local Model Context Protocol server providing backend tools for AI agents to manage projects and tasks with persistent storage in SQLite, enabling structured tracking of project tasks with dependencies, priorities, and statuses.

Enables Claude to independently check an AI agent's work in a local git repository by recording a baseline, running only the checks relevant to the changed files, and cross-checking what the agent claimed. Returns a compact, evidence-labelled report with a verdict and a ready-to-paste next prompt, so unverified success claims can't slip through.

Enables AI agents to read and manage a user's Google Tasks, including creating, editing, completing, moving, and deleting tasks and task lists via the Google Tasks API.

Enables long-running coding or documentation tasks to save incremental checkpoints, resume after interruptions, restore any previous step, and hand off work across sessions or machines using only the Python standard library.

Enables task management and automated development workflow tracking with Eureka Labo, including creating/updating tasks, starting work sessions, and automatically capturing git changes with diff logging.

An MCP server for Taskwarrior that implements agent claim and lease semantics for task management. It enables agents to list, create, and modify tasks while ensuring mutual exclusion through a system where tasks must be claimed before they are updated.

Enables AI agents to manage live Firefox tabs and native tab groups via MCP tools over stdio, with exact-match verification and safety constraints. It does not read page content or send browser data remotely.

An MCP server that exposes outputty's task tracker as typed tools backed by GitHub Issues, allowing coding agents to manage tasks with two-way sync.

Local stdio MCP server that provides tools for calculating averages, evaluating scores into grades, and retrieving student names by student ID. It logs every call to JSONL files with timestamps and performance metrics.

Enables managing Google Tasks (list, create, update, complete, uncomplete, delete) via OAuth 2.0 authorization.

Enables AI assistants to manage tasks across multiple projects with structured Markdown files, supporting creation, updates, completion, and organization with metadata and dependencies.

Enables managing employee leave through natural language, including applying, approving, rejecting, and canceling leave, checking balances, and listing requests, using an in-memory data model.