dcc-diagnostics

name	dcc-diagnostics
description	Infrastructure skill — DCC-agnostic observability primitives: capture screenshots, query audit logs, inspect tool performance metrics, and monitor process health. Works in any DCC environment (Maya, Blender, Houdini, Unreal, etc.) or standalone Python. Use for debugging any skill failure or verifying DCC state. Not for primary task execution — use a domain skill for actual DCC operations.
license	MIT
metadata	{"dcc-mcp":{"dcc":"python","version":"1.0.0","layer":"infrastructure","search-hint":"screenshot, capture, audit log, metrics, performance, process monitor, diagnostics, debug, health check, observability","tags":"diagnostics, observability, screenshot, audit, metrics, debug, infrastructure","tools":"tools.yaml"}}

DCC Diagnostics

Cross-DCC observability and debugging tools powered by dcc-mcp-core.

All tools work in any DCC environment (Maya, Blender, Houdini, Unreal, 3ds Max) or standalone Python — no DCC-specific APIs required.

Tools

dcc_diagnostics__screenshot

Capture the current screen or a specific window as a PNG/JPEG image. Backed by the dcc_mcp_core.Capturer class which uses:

• Windows: DXGI Desktop Duplication API (<16ms per frame)
• Linux: X11 XShmGetImage
• Fallback: Mock synthetic backend (headless/CI)

dcc_diagnostics__audit_log

Query the sandbox audit log from dcc_mcp_core.SandboxContext. Returns recent tool invocations with outcome (success/denied) and timestamps.

dcc_diagnostics__tool_metrics

Inspect per-tool performance counters from dcc_mcp_core.ToolRecorder: invocation count, success rate, average latency, P95/P99 percentiles.

dcc_diagnostics__process_status

Check process health via dcc_mcp_core.PyProcessMonitor. Lists tracked PIDs and their liveness status.

Usage with any DCC MCP server

import os
os.environ["DCC_MCP_SKILL_PATHS"] = "/path/to/dcc-diagnostics"

from dcc_mcp_maya import start_server  # or dcc_mcp_blender, etc.
handle = start_server(port=8765)
# dcc_diagnostics__screenshot is now available as an MCP tool