"A simple test or quiz" matching MCP servers:

Enables Claude to generate executable test cases from decision tables in CSV, JSON, or Markdown formats. Provides intelligent test planning guidance and generates Playwright/API test code with TypeScript support.

MCP server for deterministic local test execution and normalized test result reporting, supporting pytest and Jest with coverage summaries.

A simple MCP server implementation in TypeScript that communicates over stdio, allowing users to ask questions that end with 'yes or no' to trigger the MCP tool in Cursor.

A Model Context Protocol server that enables LLMs to interact with VS Code's Simple Browser, allowing for browser control, JavaScript execution, console monitoring, and navigation through natural language.

Ephemeral MicroVM-isolated code execution for AI agents. Run Python, Node, or bash — fresh hardware-isolated VM per call, hard-purged after. No state persists between calls.

MCP server that gives AI agents isolated, named Linux computers with stable IDs and lifecycle control. Supports multiple backends like Docker, Fly Machines, Modal, and E2B, with a dashboard for managing sandboxes.

Run DeepSeek as a real sub-agent inside Claude Code / Codex CLI — not just a single LLM call. DeepSeek gets its own 7-tool agent loop (Read/Write/Edit/Bash/Glob/Grep/NotebookEdit) inside a sandboxed workspace.

Enables AI coding agents to safely run commands, install dependencies, test, and modify code inside disposable, policy-enforced Docker sandboxes isolated from the host, returning results and diffs.

Enables an LLM or agent to drive a VUnit HDL unit-testing project end to end: list tests, compile, run, and inspect reports and per-test logs.

Provides an isolated workspace for testing candidate code, runs tests, and returns deterministic pass/fail verdicts. Enables automated grading of software engineering solutions by ensuring reproducible test runs.

Enables agents to accomplish multi-step work by writing a single sandboxed JavaScript program that drives shell, filesystem, HTTP, and every tool of any configured MCP server, returning only a small summary instead of raw JSON. This collapses dozens of advertised tool schemas into one tool, cutting per-request context by roughly 26× while keeping disabled upstream servers reachable.

An MCP server for Task Trellis that provides tools for AI coding agents to manage tasks, currently featuring a simple hello_world demonstration tool.

Provides agents with real Linux VMs to run commands, manage files, expose ports, and track costs, all through an ephemeral sandbox service.

A secure, containerized workspace for LLMs to autonomously code, test, and debug projects without risking the host machine.

AI-optimized Vitest interface that provides structured test output, visual debugging context, and intelligent coverage analysis for more effective AI assistance with testing.

Enables AI coding agents to compile, test, run, hot-swap, and debug Java applications incrementally through MCP, providing runtime feedback for verifying code changes.

Enables AI agents to run Jest/TypeScript tests in isolated workspaces for Test-Driven Development workflows.

Gives LLM agents spatial awareness inside Autodesk Maya, enabling scene inspection and measurement, aesthetic and engineering audits, camera planning, checkpoint/rollback safety, asset import, and viewport or render previews. Agents can also execute arbitrary Python in Maya to build and verify 3D scenes against engineering rules instead of writing blind.

Remote-control Unity Editor from any AI IDE via file-based IPC — 62 tools, zero dependencies

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.

Provides direct, unsandboxed local machine access via filesystem, Python, Node.js, and shell commands for MCP agents.

MCP server that gives AI coding agents full control over Cypress test execution, allowing them to run, debug, and iterate on E2E tests directly from MCP-compatible agents.

Chain of Draft Server is a powerful AI-driven tool that helps developers make better decisions through systematic, iterative refinement of thoughts and designs. It integrates seamlessly with popular AI agents and provides a structured approach to reasoning, API design, architecture decisions, code r

Enables AI assistants to write and debug integration tests by providing live access to DOM structure and executing code directly in test environments.

A simple and extendable MCP server that currently provides basic addition functionality and can be easily extended by defining new tools with docstrings.

Language-owned MCP gateway and execution-host coordination for Hara, exposing read-only tools for runtime introspection, evaluation, calls, and checks, with an optional deterministic test fixture for development.

Provides structured, sandboxed test and lint feedback for coding agents, returning compact typed verdicts with failure fingerprints instead of raw runner output. It enables impact-selected test execution in isolated containers, distinguishing pre-existing failures from regressions.

Enables AI agents to run a project's pytest, unittest, jest, or vitest suites and get back compact structured results with trimmed tracebacks, collection errors kept apart from real failures, and the slowest tests. Agents can then re-run just the failures, expand one failure with local variables, repeatedly execute a single test to judge flakiness, and inspect Python coverage gaps, all under a hard timeout that kills the whole process tree.

Enables agents to run and manage SageMath computations through a persistent kernel over stdio, including executing code, polling long-running jobs, interrupting or resetting sessions, running files, and driving notebooks.

Enables coding agents to build, test, archive, and run iOS apps on a remote Mac fleet from any environment, with XcodeBuildMCP-compatible tools and live simulator access.

A Docker-based MCP server that enables AI to securely run Foundry test commands in isolated containers, with automatic dependency management and cleanup.

Enables coding agents to inspect and mutate code in zero-copy RAM shadow workspaces, explore competing fixes in parallel, and commit only syntax- and test-validated changes to disk through a compact MCP tool surface.

Enables AI assistants to run, list, and analyze Lupa test suites, returning structured JSON results for debugging.

MCP server that enables AI assistants to run multi-step agent pipelines (e.g., Issue Analyst → Code Writer → Test Runner → PR Opener) from conversations, with support for Devin, shell, Python, and HTTP agents.

Enables AI agents to generate dimensionally accurate mechanical parts (gears, enclosures, brackets, assemblies) in headless Blender from plain-English specs or JSON parameters, then verify them against manifold, bounding-box and print-production gates and export glTF/STL assets. It also scaffolds interactive Three.js CAD viewer pages with orbit controls, wireframe toggles and exploded-view scrubbing.

MCP server for Dameng database, providing tools to test connection, list tables, count tables, and execute SQL via environment variables.

Provides a headless session runtime for reactive compute graphs, enabling MCP-driven cell operations without a DOM or browser.

A Model Context Protocol server that enables AI assistants to analyze images using OpenRouter vision models through a simple interface.

Gives coding agents in-memory access to the language's specifications, diagnostic and fault catalogue, verified code recipes, and university curriculum, plus toolchain tools to check, evaluate, and test source when a local binary is installed.

The toolchain of Renyi, a programming language for AI agents: check, format, run and test programs, and read the project map with every function's effects, semantic diffs and chunks for retrieval. One binary, renyi mcp.