"A customizable and widely supported Minecraft server" matching MCP servers:

MCP server enabling Claude Code to capture screenshots of Windows applications through WSL2, allowing AI to diagnose UI errors and verify layouts.

Enables local authoring and testing of Minecraft Java adventure maps through validated MCP tools, including structure building, snapshot/restore, and automated QA.

Connects AI agents to Minecraft servers via RCON to execute commands, monitor logs, and perform read-only SQLite database queries. It is specifically designed to facilitate AI-assisted plugin development, live debugging, and automated testing workflows.

A validation layer for AI coding assistants that enforces explicit LLM evaluations on plans, code diffs, and tests to ensure safer and higher-quality code.

Enables building and evaluating LangGraph agents over a protocol-level MCP server to measure failure rates across four agent topologies.

Provides a hand-written MCP server integrated with a LangGraph agent to benchmark multiple agent topologies and measure their failure rates.

Enables generating NBT structures, placing them in superflat worlds, capturing labeled screenshots, and exporting clean datapacks through MCP.

MCP-first A/B testing server that enables agents to manage experiments (create, update traffic splits, read results, apply winning variants) from tools like Claude Code, with self-hosted Cloudflare backend.

Enables LLMs to interact directly with generated REST services by discovering and invoking CRUD tools and reading exposed resources for list and get-by-id data.

A local, stdio-based MCP server that lets multiple coding agents share one project database for tasks, verification evidence, signals, knowledge, and conversation indexes. It enforces workflows with allowlisted verification runners and gate checks on acceptance criteria, evidence, dependencies, and high-risk approval before work can be closed.

Validates .mcp.json and claude_desktop_config files to catch duplicate server names, missing binaries, invalid environment variables, and unreachable endpoints. Reports each problem along with how to fix it, so silently failing MCP servers can be diagnosed and repaired.

Enables MCP clients to retrieve lab performance scores and field Core Web Vitals data for web pages via Google APIs.

Generates a pytest suite from acceptance criteria withheld from the coding agent, so the tests can disagree with the code; qikly_run never returns the criteria. The CLI also scores an existing suite by planting faults, and ships as an agent Skill (qikly --install-skill). Runs locally (uvx --from "qikly[mcp]" qikly-mcp): your code and API key stay on your machine.

Checks MCP servers by URL, npm package, or official MCP Registry search, reporting availability, response speed, exposed tools, resources, and prompts. It flags changes since prior runs as breaking, warning, or info using saved snapshots.

Windows桌面截图与窗口自动化工具，支持后台操作、不抢焦点，可截取窗口/屏幕、模拟鼠标键盘、点击菜单等。

Zero-dependency MCP server and CLI that proxies, inspects, and analyzes JSON-RPC message streams between MCP clients and servers.

Provides a framework for running multiple MCP servers using FastAPI, stdio, and streamable-http transports, with an echo tool for testing and integration with MCP Inspector.

Maps Electron app IPC handlers to MCP tools and provides 33 built-in CDP tools for DOM automation, screenshots, interaction, and debugging, enabling Claude Code to directly control your Electron app.

Enables users to run synthetic support-agent evaluations, preserve and inspect traces, score results deterministically, inject failures, and enforce release review gates through MCP tools, resources, and prompts.

A production-oriented reliability, benchmarking, and evaluation system that layers multi-version protocol negotiation, adaptive streaming, connection pooling, and resilience onto an MCP agent runtime.

A shared, concurrency-safe ledger for LLM code audits. It enables multiple agents to collaboratively report, verify, and resolve bugs with deduplication and hallucination resistance.

Verificate combines 17 applications each designed to address a separate issue with AI Coding supported with a Frontier AI Model. This allows you to Vibe Code with confidence and even dropped down to a more basic model with a better outcome.

Enables AI assistants to run code in 24 programming languages on HackerEarth's sandboxed servers and read the printed output or errors, without installing local toolchains. It also lists supported languages with exact identifiers for use when submitting code.

Enables AI agents to verify that every number, money amount, percentage, count, date and time in an AI-written Japanese summary or meeting minutes actually appears in the source text, normalizing kanji numerals, 万/億, full-width digits, Japanese era years, 午前/午後 times and 割/分 rates before comparison. Exposes a read-only check tool that reports each fact as supported, approximate, mismatched or unsupported, plus an extraction tool that lists the quantities in a text.

Enables MCP clients to discover tools, configure a Rigol oscilloscope over LAN, take measurements, and retrieve waveform captures via a containerized stdio server.

Enables AI agents to capture and analyze digital signals from DSLogic Plus and sigrok-supported devices, with archived captures, channel statistics, edge excerpts, and deterministic protocol decoding.

Enables management of TestRail projects, test cases, runs, and results directly through MCP-supported clients. It provides a comprehensive set of tools to interact with the TestRail API for seamless test cycle management within AI environments.

Enables building, saving, inspecting, and previewing supported circuits, plus running headless harmonic-balance simulations and comparing parameter variants without using the desktop UI.

Enables reading, validating, and managing Minecraft resource packs, including structure checks, asset listing, pack comparison, and Modrinth queries.

Lets an AI agent drive IPG CarMaker on your machine: start sessions, run test runs, read .erg results, and change vehicle or Simulink controller parameters. Both MATLAB-connected and standalone runs are supported, with every change reversible.

Enables managing TestRail projects, suites, cases, sections, runs, tests, results, plans, milestones, and shared steps directly from MCP-supported AI clients.

A mock MCP server for testing MCP client implementations and development workflows. Supports tools, prompts, and resources across multiple transport protocols (stdio, HTTP, SSE).

A Model Context Protocol (MCP) server that provides a simple sleep/wait tool, useful for adding delays between operations such as waiting between API calls or testing eventually consistent systems.

Generates fixed-length test data records with customizable field types including strings, enums, integers, dates, and fillers. Helps developers create realistic test datasets with specified formats and constraints.

A testing server that demonstrates Model Context Protocol features, providing access to user data, todos, and system information through resources, tools for user management and calculations, and prompt templates for various analyses.

Enables AI coding agents to sniff a deployed app's homepage and receive a free Sniff Score out of 100, a verdict, structured findings, and a paste-ready fix prompt, plus retrieval of earlier results without spending a new sniff. It also reports what paid Smoke and Deep runs would add beyond the free homepage check.

Lets an AI agent add websites, set up and tune uptime, user-flow, security, speed, SEO, domain and compliance monitors, run scans, and review results. It also explains what broke in a failed run and proposes fixes the agent can apply with the user.

Enables safe evaluation of trading-oriented MCP tools through a no-execution stdio mock with read-only signal listings, deterministic paper previews, and confirmation requests that always fail closed.

A lightweight Python-based microservice that provides simple echo functionality, receiving messages and returning them back to the client for diagnostic and testing purposes within the MCP framework.

Enables agents to turn written acceptance requirements into explicit browser-check plans, inspect web apps for supported locators and assertions, and run deterministic checks that capture evidence reports. Each requirement is linked to observed pass/fail/unverified evidence, with write actions and target origins restricted unless explicitly allowed.