"file system" matching MCP servers:

Read visual bug reports from Feedbug in your AI coding agent: screenshot, console logs, failed network requests, session replay and the DOM context of the element the tester clicked. Trace the bug to its source file, comment and resolve without leaving the editor.

Free MCP tools for context engineering audits of AI agents, offering on-demand analysis of system prompts, tool definitions, and context packing.

Measures what MCP servers cost in context tokens: probes a URL or a local command now, measures a whole config file, and looks up the daily results for every server in the official MCP registry on mcpcost.ulehla.dev.

Enables AI agents to generate Korea Government Design System components and page templates with KWCAG 2.2 accessibility requirements built in, then validate rendered pages and block task completion until remaining accessibility violations are corrected.

tailtest-cline is a Cline plugin that automatically generates and runs tests every time the agent edits a source file. https://github.com/avansaber/tailtest-cline

Enables AI agents to score live URLs against a deterministic design contract, validate DTCG tokens and Lottie animations, audit accessibility, and retrieve design-system contracts, catalogs, and review rubrics.

Zero-dependency token optimizer, test-failure triage gate, and System 1.5 semantic guardrail for AI coding agents. Intercepts compiler errors, missing dependencies, and doom loops in < 500µs.

Eyes for AI coding agents: deterministic MCP server that tells the LLM which CSS rule wins, in which file, on which line — and why. Cascade verdicts, blast radius, interaction timelines, pixel-perfect audits.

Automates the evaluation of GitHub hackathon submissions by providing tools to collect issue data, apply scoring rubrics, and generate ranking reports. It enables a multi-agent system to manage scoring consistency, statistical outlier detection, and feedback generation through the GitHub CLI.

Admission-control MCP server for AI coding agents — rejects bad file edits without coaching the LLM.

Enables Cursor AI to query SonarQube for pull request metrics, issues, file issues, and duplication reports directly, without manual data export.

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 AI coding agents to audit web pages for WCAG accessibility and color-contrast issues, and file dev-ready issues directly into OverlayQA projects.

Adversarial review system that spawns three independent contrarian reviewers to catch issues before AI coding agents execute critical changes.

A MCP server that enables AI assistants to control Android devices via ADB, supporting device info, screen control, input simulation, app management, shell execution, file transfer, and UI parsing.

Turn any .NET test-automation solution into a queryable map in one SQLite file — features, steps, API clients, page objects, and their dependencies. Zero config, no AI, no network, 100% deterministic.

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

Enables agents and users to validate Kubernetes manifests (YAML/JSON, multi-document) against the API of a chosen Kubernetes version without needing a cluster. It reports removed or deprecated APIs with replacements, unknown or misspelt fields, misconfigurations the API server would reject, Pod Security Standard violations, and common risks, each with file, line, and fix.

Enables AI assistants to access RenderDoc capture data and assist in graphics debugging via a file-based IPC bridge.

A unified MCP server for multi-stand infrastructure testing, enabling REST API calls, PostgreSQL queries, and OpenSearch searches through a single config file.