"Cinema 4D" matching MCP servers:

Lints local Markdown files or whole directory trees for broken relative links and images, empty link targets or text, heading hierarchy problems, and common README issues. Only relative targets on disk are checked, so external http(s) links are never fetched.

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

Enables AI agents to inspect UI screen recordings as token-efficient image sheets with changed regions boxed, single-frame flicker flagged, and speech aligned to frames. Exposes watch_video and zoom_video tools so agents can pull dense keyframes across any time range or one sharp full-resolution frame, all processed locally.

Enables Claude Code to benchmark llm-d from plain English, providing 35 tools and 5 workflow prompts for probing clusters, planning, deploying, running benchmarks, and analyzing results via the Model Context Protocol.

Enables AI assistants to interact with running Godot 4 C# games in real-time, providing scene tree inspection, signal debugging, runtime property modification, and enhanced logging capabilities through 48 MCP tools.

Enables checking whether a Meta, LinkedIn, or Google Ads campaign is structurally able to serve and convert before spending, by inspecting schedule, bid, destination, approval, geo, and budget fields through read-only tools. It also reconciles reported spend across the three platforms against a ledger to surface discrepancies, armed campaigns, and drift.

Enables browser automation through the MCP protocol, allowing AI agents to control a real browser using accessibility snapshots and natural language commands.

Enables an automated development loop where Google Antigravity IDE performs code generation and fixes while OpenAI Codex CLI acts as an independent QA auditor, with live dashboards, slash commands, and closed-loop remediation.

A deterministic, network-free MCP server for validating repository release hygiene and version alignment in local projects. It enables automated repository health checks and generates standardized release checklists based on project state.

Provides AI assistants with full browser DevTools access for debugging, including console, network, DOM, breakpoints, and step-through debugging, with a collaborative manual fallback when automation fails.

Integrates ProjectDiscovery security tools to perform automated reconnaissance, subdomain discovery, and vulnerability scanning. It enables comprehensive bug hunting workflows by chaining tools like subfinder, naabu, and nuclei into a unified pipeline.

An MCP-style stdio server for evaluating AI agent outputs, enabling CI-friendly quality gates, regression comparisons, and canary promotion decisions.