"QA review sentinel test marker" matching MCP servers:

Keyless, local MCP server bringing ISTQB / OWASP / IEEE / ISO / EU AI Act QA standards into your AI client. Standards-grounded retrieval, deterministic QA effort estimation, automated QA document quality review (0-100 rubric), and JUnit/CSV test-results flakiness analysis.

Analyzes Playwright test results to provide structured QA insights, including test metrics, failure analysis, bug report drafts, and release-quality recommendations.

Turns JUnit XML test reports from CI into queryable QA metrics, letting AI assistants answer plain-English questions about failures, flaky tests, regressions pinned to a commit, root-cause clusters, and performance drift. Also generates ready-to-share QA health reports and self-contained HTML dashboards without logging into separate tools.

An MCP server that brings senior-QA discipline to AI coding assistants, enabling test planning, TDD, mutation testing, and code review.

MCP server for end-to-end QA automation: generates test scenarios, discovers Playwright locators, creates TypeScript test code, executes tests, and creates GitHub issues for failures.

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.

Enables Claude Desktop and Claude Code users to get AI-powered legal risk classifications for small business decisions, returning structured GREEN/YELLOW/RED verdicts with statute citations and compliant alternatives.

A local MCP server that routes QA reviews through deterministic profiles and risk-based escalation while keeping source evidence and final decisions with the host.

A code review tool that generates detailed HTML review documents by comparing branches or commits, with author filtering and intelligent analysis.

MCP server providing three QA tools: parse JUnit XML reports, detect flaky tests across multiple runs, and evaluate quality gates with pass/fail decisions.

Exposes surface-bound annotations placed on a Blender model to AI agents, letting them read pending change requests with stable IDs, revisions, positions and visibility, then submit completion summaries by marker ID after editing. This enables batch, review-oriented workflows where the user approves or rejects each agent change in Blender, without requiring object selection, screenshots, or a fixed port bridge.

An MCP server that runs curated adversarial prompts against local Ollama models to test guardrails, scoring responses with heuristic verdicts for human review.

An MCP server that reads test reports and provides regression analysis tools for comparing runs, identifying regressions, fixes, and persistent failures.

Evaluates MCP servers by running read-only checks and returning a graded report with an A-F letter grade.

An MCP server that scans code for exposed secrets (API keys, tokens, private keys, high-entropy strings) with placeholder-aware allowlisting and fully redacted reports, enabling agents to detect leaks before committing.

MCP server exposing IAMF conformance validation and packaging tools, enabling agents to validate IAMF files and compile/manage packaging plans through natural language, with read-only operation by default and optional execution.

MCP server that ranks and deduplicates your GitHub pull request review queue across direct requests, team requests, and your own waiting PRs into a single prioritized list via one get_review_queue tool.

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.

Unifies prediction markets, global news events, aviation state, and internet-infrastructure telemetry into a single async MCP server for macro and financial analysis.

Provides coding agents with visibility into test health through tools for flaky test detection, test quality linting, and LLM evaluation harness, enabling them to triage failures, review test quality, and check prompt changes for regressions.

Enables AI coding agents to audit Python and npm dependency licenses against your distribution context before shipping, returning CLEAN, REVIEW, or BLOCK verdicts with plain-language reasons and supporting notice generation.

An MCP server that lets Claude Code delegate coding tasks such as code generation, review, refactoring, and test writing to a local Ollama instance running a Qwen3 model.

An MCP server that provides local code quality analysis for AI coding assistants, supporting file analysis, git diff review, and full project scanning with quality scoring.

Enables an AI assistant to open 3D model files (STL, 3MF, Bambu Studio and OrcaSlicer projects) in a local browser viewer where the user clicks exact spots to leave feedback. Each note comes back with the part name, millimetre coordinates, surface direction and a numbered screenshot, so the assistant can revise the design and show the new version.

An MCP server that gives AI coding agents the ability to analyze a Python repository's test health: coverage, flaky tests, and ML-based pull-request risk prediction.

An MCP server that enables automated quality control for generated images, evaluating them against reference images and rules to return structured scores and issues.

A basic MCP server built with the FastMCP framework that provides fundamental mathematical operations like addition, subtraction, multiplication, and division. It serves as a demonstration for integrating math-based tools into MCP-compatible environments like Cursor IDE.

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

Enables searching and retrieving Sri Lankan business reviews and doctor profiles from Review Guru, with tools for listings, reviews, search, and categories.