dcc-diagnostics · diff
git:20260422.2082784 to git:20260429.44de7dc
1 added, 95 removed. Audit A to A.
---
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
dcc-mcp.version: "1.0.0"
dcc-mcp.layer: infrastructure
dcc-mcp.search-hint: "screenshot, capture, audit log, metrics, performance, process monitor, diagnostics, debug, health check, observability"
dcc-mcp.tags: "diagnostics, observability, screenshot, audit, metrics, debug, infrastructure"
- tools:
- - name: screenshot
- description: "Capture a screenshot of the current display or a specific window. Returns the image as a base64-encoded PNG. Useful for visual debugging — capture what's visible on screen when an error occurs."
- input_schema:
- type: object
- properties:
- format:
- type: string
- description: "Image format: 'png' (default), 'jpeg', or 'raw_bgra'"
- default: png
- scale:
- type: number
- description: "Scale factor 0.0-1.0 (default 1.0 = native resolution). Use 0.5 to halve the size."
- default: 1.0
- jpeg_quality:
- type: integer
- description: "JPEG quality 0-100 (default 85). Only used when format is 'jpeg'."
- default: 85
- window_title:
- type: string
- description: "Capture only the window whose title contains this substring. If omitted, captures the full screen."
- save_path:
- type: string
- description: "If provided, save the image to this file path in addition to returning base64."
- timeout_ms:
- type: integer
- description: "Maximum milliseconds to wait for a frame (default 5000)."
- default: 5000
- read_only: true
- idempotent: false
- source_file: scripts/screenshot.py
- next-tools:
- on-success: []
- on-failure: [dcc_diagnostics__audit_log, dcc_diagnostics__process_status]
-
- - name: audit_log
- description: "Query the dcc-mcp-core sandbox audit log — list recent action invocations, filter by outcome (success/denied), or search by action name. Helps diagnose why an action was blocked or what the agent did recently."
- input_schema:
- type: object
- properties:
- filter:
- type: string
- description: "Filter entries: 'all' (default), 'success', 'denied', or 'error'"
- default: all
- action_name:
- type: string
- description: "Only return entries for this specific action name."
- limit:
- type: integer
- description: "Maximum number of entries to return (default 50)."
- default: 50
- read_only: true
- idempotent: true
- source_file: scripts/audit_log.py
- next-tools:
- on-success: [dcc_diagnostics__tool_metrics]
- on-failure: []
-
- - name: tool_metrics
- description: "Show performance metrics for registered tools — invocation counts, success rates, average and P95/P99 latencies. Use to identify slow or failing tools."
- input_schema:
- type: object
- properties:
- action_name:
- type: string
- description: "If provided, return metrics only for this tool. Otherwise return all."
- sort_by:
- type: string
- description: "Sort results by: 'name', 'invocations' (default), 'avg_ms', 'p95_ms', or 'failure_rate'"
- default: invocations
- limit:
- type: integer
- description: "Maximum number of tools to return (default 20)."
- default: 20
- read_only: true
- idempotent: true
- source_file: scripts/tool_metrics.py
- next-tools:
- on-success: [dcc_diagnostics__process_status]
- on-failure: []
-
- - name: process_status
- description: "Check the health of tracked DCC processes — list running PIDs, check if a specific process is alive, and inspect crash recovery policy. Use when a DCC tool stops responding."
- input_schema:
- type: object
- properties:
- pid:
- type: integer
- description: "Check status of a specific process ID. If omitted, returns summary of all tracked processes."
- read_only: true
- idempotent: true
- source_file: scripts/process_status.py
- next-tools:
- on-success: []
- on-failure: [dcc_diagnostics__audit_log]
+ dcc-mcp.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
```python
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
```