"Registering the revanthlevaka/OpenYantra repository" matching MCP servers:

Provides a stable, side-effect-free MCP tool contract that enables AI workflows to be prototyped, demoed, tested, or dry-run without connecting to databases, APIs, files, or other real data sources. It enables swapping in a real data server later without changing the AI-facing interface.

Exposes 11 agent-native QA tools over stdio so coding agents can discover a project, analyze risk, select the smallest relevant test set, generate and dry-run tests, triage failures, propose tiered heals, analyze flakes, generate quality reports, and evaluate release readiness. Planning and healing tools stay read-only or propose-only, while execution defaults to dry-run, keeping agent actions safe and evidence-backed.

Enables AI agents to inspect Git repositories for changes across committed, staged, unstaged, and untracked scopes, and run allowlisted validation commands with structured results.

Enables MCP clients and agents to query synthetic football match state, key moments, win probability, and player profiles, and to verify claims and on-screen overlay numbers against match state.

Enables coding agents to audit public or localhost pages, gather structured SEO evidence, and retrieve compact reports, finding details, and next actions for triage.

Enables AI agents to safely run test suites over MCP, returning concise JUnit-based results, trimmed tracebacks, rerun-failed support, and flaky-test detection while handling timeouts, process cleanup, and bounded output.

MCP server for automatic Storybook story generation, component analysis, and validation. Auto-detects React frameworks and syncs component documentation.

Enables AI agents to create webhook URLs, wait for incoming deliveries, inspect payloads, and replay or send signed webhook events to a local handler.

Enables MCP clients to review Ceramic and ordinary websites with desktop/mobile browser QA, deterministic design and accessibility checks, optional visual judgement, and repeatable fix verification backed by preserved screenshots and reports.

Enables an AI to statically lint Dockerfiles and compose files without Docker by parsing their true structure, reporting line-numbered findings with concrete fixes, and analyzing cache order, .dockerignore matching, and safe fix previews as diffs.

Enables agents to lint OpenAPI specifications and detect breaking changes between two versions of an API, normalising Swagger 2.0 and OpenAPI 3.0/3.1 into a single model with safe $ref resolution. It exposes schema-walking and example-validation tools over stdio, classifying each change as breaking, potentially breaking, or safe based on request-versus-response variance.

Exposes a set of CLI tools (test generation, documentation generation, linting, test running, code search) to AI assistants via MCP, allowing them to perform these tasks through natural language.

Read-only MCP server that exposes the agentic-os governance, SDLC, and Quality Engineering methodology to any MCP host. It never writes to your repository and never executes code — it serves the methodology, plans an install, and verifies it, handing any commands back to the host to run.

Exposes six evidence-oriented tools over stdio so MCP clients can navigate unfamiliar code, ask typed questions about repository evidence, review completed changes, and select existing tests.

Enables AI agents to safely read, search, and inspect a local git repository, including PR-style diffs and changed-file summaries. It also supports opt-in allowlisted test runs.

Enables querying Codecov coverage data including file, commit, repository, and pull request coverage.

Enables Claude to independently check an AI agent's work in a local git repository by recording a baseline, running only the checks relevant to the changed files, and cross-checking what the agent claimed. Returns a compact, evidence-labelled report with a verdict and a ready-to-paste next prompt, so unverified success claims can't slip through.

Enables agents and IDEs to generate and verify NestJS microservices with hexagonal architecture, JSON:API contracts, and mandatory security headers. Supports declarative spec-based module generation, refactoring existing services, quality checks, and per-repository inventory and auditing.

Enables AI agents and developers to independently verify code and documentation claims against the repository, producing evidence-backed audit reports and pull-request checks.

Enables MCP-compatible agents to audit an entire repository through one question, detecting stacks and running parallel checks for dead code, dependencies, security, CVEs, complexity, and lint across Laravel/PHP, Python, TypeScript/React, and Flutter/Dart. It returns normalized, severity-sorted findings with false positives flagged and secret values kept out of the output.

ocal-first, read-only MCP server that audits repository test architecture across ten language ecosystems. It returns deterministic evidence-backed findings, blockers, rankings, and repository-native test plans without modifying source files.

A deterministic, network-free MCP server for validating repository release hygiene and version alignment in local projects. It enables automated repository health checks and generates standardized release checklists based on project state.

Enables reading and analyzing CodeFactor repository issue pages, providing normalized issue data, summaries, and prompts for code improvements.

Exposes nine read-only tools that let a coding agent audit a repository for fabricated fallbacks, hollow or weakened tests, silently disabled checks and missing authorization scoping. Agents can run the six deterministic engines on changed or whole-repository code and get structured findings back without modifying anything.

Bridges Katalon Studio with Claude AI for intelligent QA automation, enabling project analysis, test case creation, execution, and object repository management.

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.

A simple server implementing the Model Context Protocol (MCP) that echoes messages back, designed for testing MCP clients.

Enables AI code reviews through the CodeRabbit CLI by exposing a run_review tool that executes CodeRabbit with full flag coverage for analyzing committed, uncommitted, or all changes in a repository.

Enables an agent to validate Tick work orders, inspect their instructions, repository/base, territory, checks, budget, deadline, and acceptor, rehearse delivery against the order's checks, and verify receipt signatures and roles before handing in work.

Enables access to Codacy's code quality platform through natural language, providing repository management, security analysis, pull request reviews, and local CLI-based code analysis. Supports comprehensive code quality monitoring including issues, coverage, security vulnerabilities, and technical debt assessment across organizations and repositories.