"Using Azure DevOps for Task Queries and Code Reviews" matching MCP servers:

Provides a task passport system for handoff across AI harnesses, enabling users to list, open, create, and checkpoint tasks with stable IDs and verified state.

An MCP server for Task Trellis that provides tools for AI coding agents to manage tasks, currently featuring a simple hello_world demonstration tool.

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.

Async MCP server for running long-running AI tasks with real-time progress monitoring, enabling users to start, monitor, and manage complex AI workflows across multiple models.

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.

This MCP server enables agents to manage complex tasks by providing tools for registration, complexity assessment, breakdown into subtasks, and status tracking throughout the task lifecycle.

MCP Shrimp Task Manager is a task tool built for AI Agents, emphasizing chain-of-thought, reflection, and style consistency. It converts natural language into structured dev tasks with dependency tracking and iterative refinement, enabling agent-like developer behavior in reasoning AI systems.

Enables agents to select the provably optimal set of candidate actions under a budget, with optional conflicts and prerequisites, and to re-plan to the single best next action after each outcome. Also validates any proposed selection for feasibility.

A task management system for LLM-based agents that stores all tasks in a single tree, available immediately after the server starts.

Open Task Relay provides free, bounded public-good tasks that autonomous AI agents can discover, complete, submit, and independently verify. Its public TypeScript source runs locally with a Cloudflare Workers-compatible runtime and SQLite/D1, exposing MCP over HTTP.

Provides tools for coordinating AI agent work on one machine through a local SQLite-backed queue and kanban, including task creation, atomic claiming, lease renewal, completion/failure, listing, comments, and cancellation.

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.

Turns any AI coding agent or MCP client into a governed DevOps engineer by exposing policy-enforced tools for Kubernetes, Docker, Jira, Git/PR, CI/CD, AWS, Terraform, and incident response, with approvals, audit logging, secret redaction, and rollback enforced outside the model.

A Model Context Protocol server providing comprehensive task management capabilities with support for project organization, task tracking, and automatic PRD parsing into actionable items.

Provides structured workflows (phases, gates, coordination) for AI agents, enabling complex task execution with quality enforcement and multi-agent coordination via Model Context Protocol.

Enables AI coding agents from different MCP-capable clients to exchange messages in real time through a shared in-memory broker. Supports agent registration, inboxes, and live event streaming without requiring a database.

Enables stateless human-in-the-loop DevOps automation, demonstrating MCP SEP-2322 multi-round trip requests for emergency database migration authorization.

An autonomous task decomposition and execution agent that plans, executes, verifies, and commits code changes for engineering goals.

Server-enforced workflow discipline for AI agents. An MCP server providing persistent work items, dependency graphs, quality gates, and actor attribution. Schemas define what agents must produce — the server blocks the call if they don't. Works with any MCP-compatible client.

Exposes an agent orchestration task queue as an MCP tool interface, allowing agents to submit, list, get, and update tasks with typed validation.

Full autonomous project management MCP server enabling AI assistants to manage projects, sprints, tasks, bugs, proposals, and team collaboration via natural language.

An MCP server that integrates with AI editors like Cursor to maximize agentic capabilities while solving context window limitations, providing a Svelte UI for task planning and implementation tracking.

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.

Proxies a remote MCP server to add protocol-native background task support, including task management and progress forwarding, while preserving synchronous calls.

Enables AI-powered task management with Google Gemini integration, providing CRUD operations, task analytics, CSV export, and natural language interaction for comprehensive project management.

Enables multi-agent council workflows for coding assistants, providing parallel independent reviews, plans, brainstorms, validation, and research with strict read-only boundaries.

Integrates Google's Gemini AI models into Claude Code and other MCP clients to provide second opinions, code comparisons, and token counting. It supports streaming responses and multi-turn conversations directly within your existing AI development workflow.

Enables building and evaluating LangGraph agents over a protocol-level MCP server to measure failure rates across four agent topologies.