"General search for the term 'task'" matching MCP servers:

Skill librarian for coding agents: describe a task and get the few matching skills from a large local skill collection, then report whether they worked. Runs locally with Ollama and SQLite.

Enables an agent to submit a task brief and receive a short, validated list of skills from its full catalogue that are worth reading before starting.

Provides LLMs with full PostgreSQL database access, including tools for query execution, schema management, and data export. It also features a dedicated insights system for storing business memos and supports both local stdio and remote HTTP transport.

Provides AI coding agents with a read-only live view of a project's task state, including progress, blockers, and evidence, via zero-token conditional polling and an optional widget for rendering a dashboard card.

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 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.

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.

Provides a stable, side-effect-free MCP tool contract that enables AI workflows to be prototyped, demoed, tested, or dry-run without connecting to databases, APIs, files, or other real data sources. It enables swapping in a real data server later without changing the AI-facing interface.

Provides persistent project memory, task lists, time tracking, and shared rules and learned preferences so coding agents can recall decisions and conventions across sessions and coordinate multi-step work.

MCP server for OpenClaw task lifecycle management, enabling starting, listing, and checking status of tasks via a Gateway API.

A Model Context Protocol implementation that provides a standardized interface for task management, supporting both STDIO mode for CLI/AI applications and HTTP+SSE mode for browser-based clients.

Enables coding agents to publish HTML pages and small sites over MCP, returning a stable link and keeping every version, with access limited to specific people, an organization, or anyone with the link.

A campaign and task management MCP server for AI coding assistants, enabling dependency tracking, acceptance criteria, testing strategies, and progress monitoring for projects.

Enables intelligent task management with status tracking, dependency resolution, and automatic next task discovery based on preconditions and priorities. Supports hierarchical task structures with subtasks and flexible JSON-based configuration.

A Model Context Protocol server for intelligent task management in AI-powered development environments, providing file-based storage, dependency management with cycle detection, and an interactive CLI.

Enables MCP clients to read bounded workspace files, validate exact replacement plans for the VS Code extension, and check recent typing progress without modifying source or running shell commands.

A secure MCP server providing controlled access to pixi tasks and commands, eliminating agent bash circumvention. It exposes 11 pixi tools through 4 meta-tools for ~95% context savings.

A task manager MCP server that demonstrates core MCP concepts (resources, tools, prompts, stdio) and syncs data with a Next.js web app.

A secure, vector-based task management server for Claude Desktop using sqlite-vec and sentence-transformers. It provides intelligent task tracking with semantic search capabilities to organize and retrieve development tasks efficiently.

Enables MCP-compatible AI clients and coding agents to turn vague GitHub requests into structured, testable task contracts, inspect repository evidence, simulate execution, validate readiness, and preview or publish Gibwork bounties only after explicit confirmation.

Enables AI agents to decompose high-level goals into structured task plan DAGs with dependencies, acceptance criteria, and quality gates, and to validate or export those plans in multiple formats.

Enables reviewing and dispatching existing Codex tasks across projects through a lightweight control panel, including scanning task inventories, confirming Continue or Optimize prompts, and tracking dispatch progress.

A local task management MCP server that enables users to create, update, and manage tasks through natural language conversations with Claude. It provides nine tools for comprehensive task management including creation, filtering, searching, and daily planning without requiring a separate UI or backend service.

An MCP server that automates project task breakdown, dependency management, and smart task recommendations, integrating with LLMs like Gemini and OpenAI.

MCP server for Nova Agent progress reporting. Enables updating execution sessions and managing steps within tasks.

Exposes Task CLI operations to AI clients as structured MCP tools, enabling task creation, listing, completion, and read-only project status while sharing the same local storage as the command-line interface.

A task manager MCP server that demonstrates all three MCP primitives (tools, resources, prompts). Enables users to manage tasks, read task summaries and details, and run structured planning/review prompts through natural language.

MCP server for managing i18n translation files — gives your AI agent full control over your app's translations without dumping entire locale files into context.

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.