"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.

Deployment-readiness harness for AI document-QA and extraction systems. Scaffolds grounding checks, privacy redaction and cost tracking into a project, composing OpenGATE, Redacta and PubCrawl.

Natural language to SQL engine with multi-connector support (PostgreSQL, MySQL, Snowflake, BigQuery, DuckDB), document QA, semantic caching, and self-hosted MCP server.

Get access to real-time SEO data, including: keyword insights, backlink data, traffic estimates and more. Allow AI tools and Large Language Models (LLMs) to tap into the real-time SEO Review Tools API with natural language commands.

Multi-model adversarial design review MCP server that distributes design documents to multiple LLMs, retrieves project knowledge, and produces calibrated consensus summaries.

Connects an LLM to specialized MCP servers via stdio, enabling authenticated GitHub repository, issue, file, and code operations alongside TF-IDF retrieval over employee PDF records and QA test-case generation and result validation. It exposes tools, a read-only resource, and reusable prompt templates so a client can discover and orchestrate multiple capabilities in one workflow.

Enables generating QA datasets from documents and evaluating RAG systems with detailed metrics, failure diagnosis, and visual reports through natural language.

A Model Context Protocol server that enables Claude to collaborate with Google's Gemini AI models, providing tools for question answering, code review, brainstorming, test generation, and explanations.

Enables querying PDF documents using natural language with grounded answers and source citations via a local RAG pipeline.

A Python-based MCP server that enables document-based question answering by processing PDF, TXT, and Markdown files through OpenAI's API. It provides hallucination-free responses based strictly on document content using semantic search and includes a web interface for management.

Enables document-based question answering using OpenAI's GPT-4 with semantic search and embeddings. Upload PDF, TXT, or Markdown files and get answers strictly based on document content with source attribution and confidence scores.

Enables interactive GitHub pull-request review from Claude Code, with tools to fetch PRs, inspect files and repo structure, post review comments, and query a RAG index of the codebase.

Enables MCP clients such as Claude Desktop and Claude Code to query an indexed source repository through path-guarded tools for hybrid keyword-and-vector code search, file snippets, symbol explanations, reference lookups, and test-skeleton suggestions. Serves those results over stdio with cited file:line locations so agents can answer questions about a codebase and drive grounded code review.

An AI-powered MCP server for analyzing Japanese legal contracts and identifying risks through a RAG-enhanced workflow. It enables clients to search legal knowledge, analyze clause risks, and generate automated contract review reports.

An academic paper management tool that enables multi-source research searching, batch PDF analysis, and the generation of comprehensive, cross-literature reviews. It features intelligent token compression and quality evaluation to streamline the literature review workflow for researchers.

A personal RAG MCP server that indexes your GitHub commits and review comments, surfacing them as retrieval hits to help Claude Code write or review code in your style.

Enables automated GitHub Pull Request reviews using local Ollama, Cursor CLI, or Gemini CLI as AI providers. Supports customizable review prompts, comprehensive PR analysis, and optional auto-posting of reviews to GitHub.

Enables humans and AI agents to share canonical, versioned knowledge through MCP, with auditable provenance, review workflows, and reconciliation.

An MCP server that exposes grounded, source-attributed question-answering over a collection of PDF documents.

Enables any MCP-compatible AI assistant to search, filter, and retrieve information from a local document collection using a hybrid search pipeline with vector, BM25, reranking, and LLM enrichment.

This MCP server enables intelligent API testing automation by combining RAG knowledge retrieval with tool execution capabilities. It allows QA engineers to perform natural language-driven API testing with contextual knowledge support.

A local MCP server that answers natural language questions over a collection of research PDFs, using semantic retrieval and Gemini for grounded answers with citations.

Enables users to ingest PDF/DOCX/TXT/MD documents and ask natural language questions about them, using local embeddings and Groq-powered retrieval-augmented generation.

A conversational chat interface that uses RAG over a synthetic test corpus and a domain-knowledge corpus, exposing retrieval via MCP.

test

test

This is test server

Enables automated GitHub pull request review through a multi-agent system using LangGraph and MCP tools, analyzing PR metadata, files, patches, comments, and policies with RAG, guardrails, HITL approval, and Langfuse observability.

MCP server exposing lease Q&A via the lease_qa tool, querying a LeaseClear API to return grounded answers about specific lease documents.

Enables CI/CD failure triage for GitHub Actions by inspecting failed runs, classifying root causes with LLM assistance, retrieving relevant runbook guidance, estimating flakiness, and either preparing dry-run fix PRs or publishing structured diagnoses to Discord for human review.