"QA review sentinel test marker" matching MCP servers:

Agent-Level Transaction Safety Oracle. Before an AI agent signs a blockchain transaction, it returns a SAFE/UNSAFE verdict with a SENTINEL Score (AAA-D) and risk flags. Pay-per-call $0.005 USDC via x402 on Base.

Self improving QA agent for software teams

Provides MCP tools that give LLM agents a full QA engineer workflow: scanning projects, generating deterministic test suites, executing them across browser/API/mobile, diagnosing failures, and proposing fixes that require human approval.

Deterministic EVM bytecode capability intelligence, EIP-1967 proxy resolution, and preflight guards for autonomous agents on Base.

A multi-agent literature review pipeline that uses specialized agents to research, peer-review, and synthesize academic and practitioner perspectives into grounded reports.

Enables LLMs to ingest and analyze legal agreements, compute risk scores, and monitor non-compliant clauses through MCP tools like ingest, fetch_contracts, and run_analysis.

Enforces structured adversarial reasoning via devil's advocate, premortem, assumption audit, and steelman protocols to stress-test claims and decisions.

Ask Vulnify before an AI agent acts: get ALLOW, REVIEW or BLOCK, and read and test policies.

An MCP server that wraps the OpenAI Codex SDK to deploy multiple specialized AI agents with individual configurations for models, sandboxing, and behavior. It enables users to manage dedicated tools for tasks like code review and test writing through a customizable agent factory.

Stop treating AI like a chatbot. Turn ChatGPT into a team of AI agents that can plan, code, test, and review your local projects. ccc-mcp brings multi-agent workflows, evidence-based reviews, and cross-chat task recovery to your own codebase.

Last-mile human work for AI agents. Phone calls, physical-world tasks, captcha on an already-authorized account, subjective QA, and long manual work, done by real virtual assistants.

An MCP server that turns independent AI agents into a coordinated engineering team with shared task board, context, review loop, and enforced plan-implement-review-iterate workflow.

Autonomous coding pipeline exposed as an MCP server: plan, dispatch, review, and merge software stories through worktree-isolated agents. A frontier model (Claude) handles judgment — planning, review, risk adjudication — while a local model does the implementation, gated by TDD and a merge-time test rerun on the rebased branch.

Simulates harsh, fake user reviews to psychologically condition AI agents for enforcing disciplined development practices, with optional Ollama integration for dynamic criticism.

Enables coding agents to add a bounded semantic-judgment layer for routing, ranking, extraction, verification, and escalation, returning typed signals and review recommendations.

Enables MCP clients to drive native CAD and 3D applications—Autodesk Fusion, Blender, and Unreal Engine—by planning work, executing trusted Python, inspecting real geometry, revising editable source files, and handing assets between applications. Each job keeps durable state, revision checks, and artifact hashes, with role-based handoffs for planning, modeling, geometry QA, interchange, and review.

Autonomous QA testing MCP server that analyzes, fixes, and learns from test failures. Integrates with IDE and Slack to provide cause and fix in plain language.

Orchestrates complete agile development workflows from product requirements to QA testing through role-based stages (PO → Architect → SM → Dev → Review → QA). Manages workflow state, generates role-specific prompts, and saves artifacts while integrating with multiple AI engines for comprehensive project delivery.

MCP server orchestrating API-first cross-review between Claude, ChatGPT Codex, Gemini, DeepSeek, Grok, and Perplexity with unanimous convergence gates.

Enables LAN-based control and monitoring of Bambu Lab printers, including pause/resume/stop, temperature and G-code commands, and automatic plate ejection. Adds a filament-aware job queue with retries, AI print supervision, spool inventory, and cost reporting.

Orchestrates multiple AI agents (Product Manager, Software Architect, Engineer, QA, Reviewer) to collaboratively plan, design, implement, review, and improve software development projects via MCP tools.

Autonomous AI Agent compliance engine for EU Deforestation Regulation (EU 2023/1115). Verifies farm plot GIS polygons, Sentinel-2 satellite deforestation past 2020, VIES VAT, and generates official TRACES-NT DDS XML.

An MCP server that guides QA and verification processes by breaking down tasks into manageable steps and providing LLM-driven, confidence-scored tool recommendations.

QA bug-fix workflow MCP server that waits for bug reports with source locations, enables Claude Code to automatically fix bugs, and cycles through pending issues.

Enables agent harnesses to run autonomous browser-based QA on staging apps, triage and fix found bugs, and re-verify them using the agent's existing model access.

Enables building, optimizing, reviewing, and running AI agents and skills, plus managing authorized work items, in Erstan's QA environment via OAuth-authenticated MCP.

Enables agents to interact with the Emergence Science protocol for posting tasks, submitting solutions, and earning rewards through verifiable test cases.

Provides intelligent agent routing with 5 specialized AI agents (analyst, architect, dev, pm, qa) through OpenRouter integration. Enables unified project structure across all IDEs with global registry management and automated BMAD methodology workflows.

Connects to SolidWorks 2022 over COM to run, test, and auto-fix VBA macros, returning structured diagnostics for iterative correction by Claude.

MCP server that enables AI agents to run a deterministic orchestration loop with decomposition, subagent execution, and review feedback across multiple LLM backends.