"Exploring Computer Usage" matching MCP servers:

A TypeScript MCP server for TypeSafe Jev, exposing one tool for batched classification, rubric scoring, and yes/no judgments with probabilities and token usage. Includes Codex compatibility checks and requires a TypeSafe API key.

MCP server for the Ghost Inspector API that lets agents inspect and manage end-to-end browser tests, offering read-only tools for inventory, module usage, and stale test analysis, plus opt-in guarded write operations.

Enables AI agents to perform software engineering tasks inside an isolated, deterministic sandbox—exploring repositories, reproducing failures, applying patches, running tests, and verifying solutions against hidden suites through MCP tools.

A proxy MCP server that wraps MCP-for-Unity, correcting transport inaccuracies and restricting tool usage for reliable Unity integration.

Provides tools to test regex patterns for correctness, performance (ReDoS), and memory usage, and suggests safe rewrites. Enables LLMs to iterate on regex generation with verifiable feedback.

Provides comprehensive web analysis and auditing tools including HTML extraction, markdown conversion, screenshots, debugging, and Lighthouse-based performance, accessibility, and SEO audits, with token optimization for reduced usage.

A Model Context Protocol (MCP) server that provides comprehensive documentation and usage examples for the AutoFixture testing library.

Enables AI agents to control Android devices and emulators through direct UI interaction, allowing app navigation, automated testing, and real-world task execution via ADB without computer vision or scripts.

Provides deterministic application truth for Hugomemo Android QA, including server sessions, queue rounds, attempt details, skill ratings, memory score, entitlement, and usage, while leaving emulator interaction to a separate Android MCP.

Enables building and running custom LLM benchmarks with multi-judge evaluation, supporting GUI, MCP client, and CLI usage for ranked, auditable results.

A lightweight bridge enabling AI agents to perform real-world tasks on Android devices such as app navigation, UI interaction, and automated QA testing without requiring computer-vision pipelines or preprogrammed scripts.

An MCP server that runs developer tooling commands (tests, linters, etc.) and returns compact, grouped error summaries to AI agents, reducing token usage and context window pollution. It supports multiple adapters for different tools and provides tools like run_check, diff_runs, and get_log_slice for iterative debugging.

Enables AI assistants to surgically extract and analyze code, reducing token usage and costs by up to 200x compared to reading entire files.

An MCP server for external computer-use that launches, observes, and interacts with any X11 application via AT-SPI2 accessibility tree and xdotool input injection.

Enables coding agents and CI to verify rendered web UIs against design token usage, layout constraints, and accessibility rules, returning concise pass/fail verdicts with actionable findings.

Enables OpenCode to safely automate and verify Windows desktop and web browser interactions through 83 MCP tools, including UI automation, screenshots, visual diffing, workflow recording, and secret handling, with strict allowlist-based security.

Enables AI assistants to interact with the Scorecard REST API for exploring endpoints, making test requests, and integrating the SDK into applications.

UI interaction verifier for computer-use agents — tracks which UI elements exist and their expected behavior to catch ghost actions.

Enables AI agents to inspect, automate, and test desktop GUI applications on Wayland through window/desktop streaming, semantic AT-SPI accessibility tree inspection, and boundary-clamped mouse, keyboard, drag, and scroll input injection. It also manages application process lifecycle, captures window frames, and registers Python GUI scripts as native Linux desktop launchers.

Enables AI assistants to inspect and automate Windows desktop UI elements by exploring UI trees, checking properties, performing actions like clicking and typing, and generating Python automation scripts.