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    A comprehensive MCP server for SEO, performance, GEO, and UX audits with 37 tools covering technical SEO, Lighthouse performance, AI search optimization, content analysis, accessibility, security, and more.
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    MIT
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    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.
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    Apache 2.0
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    Enables AI-first accessibility testing by investigating pages, explaining findings, and applying fixes via natural language, integrated with Playwright tests and local or cloud AI models.
    MIT
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    Release gate agents call before they tell users a public website is ready to ship. Scans public websites across security, SEO, accessibility, legal compliance, and sustainability.
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    MIT
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    Accessibility-first LaTeX publishing engine with an embedded MCP server. Tools to list registered documents, render template-driven documents, and audit built PDFs for PDF/UA-2 / WTPDF / PDF/A-4f conformance with veraPDF. MIT-licensed.
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    MIT
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    AI-powered exploratory QA agent. Explores web apps like a real user — 18 MCP tools for clicking, filling forms, and navigating. Automatically verifies that actions persist (fake deletes, failed edits). Runs 16 detection types including dead links, SEO, accessibility, and performance checks.
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    MIT
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    MCP server for managing test cases, cycles, and results in Zephyr Scale on self-hosted Jira. Enables AI agents to create, search, update, and delete test artifacts through natural language.
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    MIT
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    Umbra is the trust layer for AI-generated code. It scores any repo 0-100 with file:line evidence (security, slop, Docker-verified build/boot, and claim receipts that catch agents lying about tests), and guards agent file writes mid-stream blocking leaked keys, alg:none JWTs, and git-hook plants before they land. Tools: scan_repo, guard_content, get_score.
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    2,282
    MIT
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    Diffgate MCP server acts as a code review engine, enabling AI coding agents to analyze and validate code diffs before application. It enhances AI workflows by providing self-checking capabilities to optimise and secure code changes.
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    Apache 2.0
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    Enables AI agents to inspect Shadow Monitor capture files locally, with tools to load bundles, find errors, search network requests, and replay user actions.
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    MIT