"Integrating DaVinci Resolve API" matching MCP servers:

API Tester AI - MCP server providing AI-powered tools and automation by MEOK AI Labs

Enables natural-language chat-driven API testing, letting users upload API specs, run deterministic pytest checks against live targets, inspect results, create defects, and view failure trends.

Starter scaffold for a workshop to build an MCP API testing server, intended to be extended through incremental steps.

An MCP Server that provides access to Google's Cloud Tool Results API, enabling applications to programmatically interact with test results from Firebase Test Lab and mobile app testing through natural language.

Enables MCP-compatible clients to discover API endpoints from OpenAPI/Swagger specs, send HTTP requests, validate responses, and analyze status codes for automated API testing.

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.

Generate realistic relational test data with 156 field types, 22 locales, and foreign key integrity. One API call seeds your entire database.

Zero-Config, Fully AI-Managed End-to-End Testing for all code gen platforms.

MCP server for email compatibility analysis. Analyze, preview, diff, and fix HTML emails across 15 email clients — plus capture real screenshots and create shareable links with an optional API key.

Deterministic fluff detector for AI-generated prose. No model, no API key.

Audits local package folders to generate publish-readiness reports and previews of NPM tarball contents. It helps developers identify and resolve configuration issues like missing types or broken exports before releasing code.

MCP server exposing SuperAudit’s free website audit as a callable tool, enabling AI agents to audit any public URL and receive scores across SEO, security, legal, performance, and more. No API key required; respects rate limits and caches results.

Turns natural-language API descriptions into runnable k6 load tests, executes them, and returns structured performance metrics for AI-assisted reporting.

Enables management of TestMu AI test projects, test cases, test runs, and integration with Jira, HyperExecute, and AI insights through natural language.

MCP server for instruckt visual annotations. Enables AI agents to retrieve pending annotations, view screenshots, and resolve annotations.

CLI-first API testing tool with MCP server. Tests are .tarn.yaml; failures come back as structured JSON for AI agents to branch on. Tools: tarn_run, tarn_validate, tarn_fix_plan, tarn_inspect, tarn_rerun_failed.

Enables reading, listing, searching, commenting on, and updating status of ZenTao bugs via its REST API, with write operations defaulting to dry-run.

An MCP server that makes backend API requests and packages request+response pairs as evidence to prove an endpoint works.

Analyzes Playwright test results to provide structured QA insights, including test metrics, failure analysis, bug report drafts, and release-quality recommendations.

MCP server that scans Salesforce Agentforce metadata for security vulnerabilities using 61+ SAST rules, integrating into AI coding workflows to guard against OWASP LLM top 10 risks.