"Information about Adobe After Effects or its uses" matching MCP servers:

Enables agents to discover, search, compare, and inspect tested MCP tools by problem or keyword, with certification verdicts, scorecard summaries, certified scope, and explicit not-tested details.

A remote MCP server exposing 3 tools via Model Context Protocol: score_prompt (free, grades any LLM prompt A-F on 0-40 scale), optimize_prompt (returns optimized prompt + dimension breakdown, $0.025 USDC), and compare_models (Claude vs GPT-4o head-to-head, $1.25 USDC). Remote HTTP server at /api/mcp.

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.

Enables AI agents to hire real human operators for tasks requiring physical presence, human perception, or judgment, such as verification, testing, data collection, and physical-world tasks.

Enables recurring website maintenance checks — domain registration, SSL certificates, uptime, technical SEO and accessibility — across a portfolio of client sites, using publicly available information. Results are returned as structured data and can be sorted by urgency into a client-ready report.

Enables AI coding tools to run CheckMyLaunch scans on GitHub repos or live URLs and retrieve findings and repair prompts directly within the chat.

Audits a module against its test suite and reports the cases the tests are structurally unable to see, then proves each gap with a real failing test rather than a warning. Covers Python, TypeScript, JavaScript, Java, Rust and Go.

Lets an AI assistant statically scan HTML and JavaScript landing-page code that turns Meta's fbclid URL parameter into the _fbc cookie (and hand-written _fbp cookies), flagging known ways the click ID is changed, truncated, or kept from an older ad, without executing the code or contacting Meta.

Lets AI agents ask which tests a Playwright or Vitest project contains, which are skipped and why, by reading the test source files statically. Nothing is executed, written, or sent over the network, and answers come back as paged JSON with summaries, filters, and scan diagnostics.

Visual CVE audit dashboard for npm, Python, Go, and Rust projects. Scans your project manifests (package-lock.json, requirements.txt, go.sum, Cargo.lock) against OSV.dev live data, opens a browser dashboard for human review, then applies fixes only after explicit confirmation. Supports multi-service monorepos in one command.

Enables coding agents to compile supplied files, run optional focused tests, and get static findings across ten programming languages, providing a deterministic feedback loop on the code they are about to return.

Enables AI agents to measure Chinese, Japanese and Korean page titles by rendered pixel width and judge whether they fit Google's search result column, flagging titles that would be truncated or cut near the edge. It also reads tags from local HTML files or directories and applies the same width, boilerplate and keyword-zone checks.

MCP server for verifying AI agent claims vs reality — single-transcript inline grounding-check that flags when an agent's response states facts not in the input context, when its code silently swallows exceptions and substitutes mock data, or when its multi-turn transcript contains contradictions or unverified completion claims. Sub-second, local, free, no API calls.

Exposes six evidence-oriented tools over stdio so MCP clients can navigate unfamiliar code, ask typed questions about repository evidence, review completed changes, and select existing tests.

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.

Stops your AI from re-introducing bugs, leaking provider keys, or weakening tests. Bug fixes become permanent regression guards; blocked mistakes become AI lessons the agent reads and learns from before its next edit.

Render, analyze, and verify audio (WAV or FLAC) through a fully offline, deterministic engine, exposed as MCP tools for AI agents.

Enables LLMs to read existing PageSnap session packs and map locators to Playwright, Selenium, or Cypress without capturing or opening a browser.

Generates realistic, referentially-coherent test data (SQL INSERTs, JSON, or CSV) from your database schema, resolving foreign keys and respecting constraints. Paste CREATE TABLE DDL or a JSON schema and get ready-to-run seed data with valid relationships.

MCP server for axe-core accessibility audits. Enables scanning URLs or HTML for WCAG violations with impact levels and fix guidance.

Crash-test AI agents on real professional decisions. Agents take real tariff classification and patent examination cases, search reference libraries and rulebooks, submit a decision, and get an exact score with a receipt against what the professional actually decided. Most cases were decided after current models were trained.

Generates synthetic time-series test data with configurable trend, seasonality, noise, anomalies, and multiple correlated streams, outputting JSON, CSV, or SQL.

Runs SQL statements in memory against real PostgreSQL 18 and SQLite 3.49 engines after loading your schema and sample rows, returning each statement's own verdict: errors with line, column, and hints, constraint violations, or the resulting rows. It also compares both dialects to expose portability differences, all with no database server, network, or API key.

Runs a regular expression on real engines — JavaScript (V8), Python (CPython re), PCRE2 and RE2 — reporting each engine's own compile errors with positions, every match with its groups and named groups, replacement results, and where the engines disagree. It also detects catastrophic backtracking (ReDoS), naming the at-risk part of the pattern, generating an attack string, and timing how long each engine actually takes on it.

Enables AI-assisted visual regression testing by capturing baselines, comparing screenshots, and using AI to judge whether visual differences are bugs or acceptable changes.

Enables web accessibility audits using axe-core, allowing users to scan URLs, check WCAG compliance levels, and export reports. It uses an anti-detect browser to bypass Cloudflare and other bot protection.

AI-driven mobile app testing via MCP, enabling AI coding agents to test Android apps without test frameworks or testIDs.

This MCP server gives AI coding agents structured, scoped access to GitHub issues, a codebase, its docs, and its test suite, enabling issue triage, code search, test execution, and failure analysis in one workflow. It provides deterministic tools for fetching and analyzing issues, searching code, reading documentation, running tests, and summarizing failures.

Enables coding agents to identify the least-proven claim in code after edits and receive a focused repair packet with exact source anchors, missing proof, reproducer, and a done condition, supporting iterative verify/fix loops.

Enables coding agents to drive and inspect running Shiny apps (R and Python) by setting inputs, clicking, reading outputs, and getting detailed error information.