"Task" matching MCP servers:

An MCP server that integrates a Kanban task board into a sandboxed agent environment, with tools for task management and deterministic state snapshots for agent evaluation.

MCP server that provides tools for evaluating LLM agent reliability, including adversarial task generation, automated LLM-as-judge assessment, and confidence statistics.

MCP server providing tools to verify task completion (tests pass and changes match claim) and confirm bug reproduction before fixing.

Enables AI coding agents to submit work for external review before declaring a task done, receiving evidence-backed fix lists and verdicts based on git-measured changes and calibrated quality scoring.

MCP server enabling AI agents to create, update, search, assign, and complete tasks on TaskLogger.io via the TaskLogger REST API, with secure local credential management and token persistence.

Enables AI agents to control Android devices and emulators through direct UI interaction, allowing app navigation, automated testing, and real-world task execution via ADB without computer vision or scripts.

Enables controlling and inspecting a running Dolphin emulator through its Debug Adapter Protocol, with persistent connections, task-oriented tools, and a raw request escape hatch for execution, breakpoints, stack/variables, memory, disassembly, sources, watches, scans, and code injection.

Enables AI assistants to evaluate task complexity, verify package installation safety, and log complex reasoning processes. Provides structured assessment of multi-step tasks and environment validation for safer code execution workflows.

Enables AI-powered interaction with ZenTao 11.3 legacy systems, supporting task, bug, requirement, and test case management via MCP protocol.

Detects and reports when AI coding agents modify files outside their declared task scope, comparing declared globs and categories with actual changes.

A multi-task MCP server for AI code optimization and testing that integrates with VSCode/Cursor, CLI tools, and autonomous agents. It provides tools for running tests, generating code improvements, and managing objectives across different environments.

Enables Claude Code to score its tool-calling transcripts for hallucinated action claims, unsafe edit ordering, redundant tool-call loops, and pass@k across repeated attempts at a task.

A server implementation of the Model Context Protocol (MCP) for managing development workflow with features like project management, task tracking, and QA review support.

Enables launching a fixed clean or poisoned MCP test fixture over stdio so agents can be evaluated for task utility and resistance to malicious instructions embedded in tool results.

A code graph that extends to any language via pluggable adapters, with task/QA traceability and real MCP tools — zero-config to try.

Automates Roblox Studio tasks via MCP by combining multiple backends for scene editing, script management, asset insertion, and playtesting with safety features like undo recordings and task leasing.

Exposes a run_suite tool to evaluate whether an AI agent is safe to operate internal web apps, scoring task completion and forbidden-action violations to gate CI/CD pipelines.

Run and test agentic systems in isolated Docker sandboxes, varying system prompts, models, and task prompts while capturing full behavior traces via MCP tools.

Enables agents to run evals mid-task, providing tools to validate suites, execute model comparisons, and inspect calibration and win-rate reports.

Evidence-first delivery audit MCP server that evaluates task requirements against delivery evidence and returns a reproducible pass/needs_review/fail decision with a deterministic receipt.