dcc-mcp-renderdoc

dcc-mcp-renderdoc

Enables automated graphics capture and replay with RenderDoc, allowing agents to launch executables, inspect captures, and export thumbnails or timeline JSON for regression triage.

Category
Visit Server

README

dcc-mcp-renderdoc

RenderDoc capture and replay automation for the DCC Model Context Protocol ecosystem.

The adapter is headless-first: it reuses the official renderdoccmd executable for capture and conversion, so agents can automate graphics regression triage without keeping the RenderDoc GUI open or installing a second bridge.

Install

pip install dcc-mcp-renderdoc

Install RenderDoc separately, then expose its command line tool with either PATH or:

export DCC_MCP_RENDERDOC_CMD=/opt/renderdoc/bin/renderdoccmd
dcc-mcp-renderdoc

On Windows, set the variable to renderdoccmd.exe.

Agent workflows

  • Launch a game or test executable under RenderDoc and wait for a typed .rdc capture.
  • Trigger F12 automatically after a configurable delay, with optional child-process window focus.
  • Inject into a visible Windows process that had to be launched by a platform client, then trigger and collect a capture.
  • Inspect capture driver, machine identity, chunk version, API-call counts, and representative calls.
  • Export a capture thumbnail for visual review.
  • Export Chrome trace JSON for timeline tooling.

The capture tool launches only the explicit executable and arguments supplied by the caller. It never invokes a shell. Analysis tools are read-only with respect to the .rdc input.

For interactive Windows programs, pass trigger_after_secs to capture_program. When a launcher creates the rendered child process, also pass trigger_process_name. Use capture_process only when the target is already running; late injection may not capture graphics devices created before RenderDoc was attached.

Real CI

CI discovers the current stable RenderDoc build from the official downloads page. It compiles a small OpenGL program, captures a real frame under Xvfb, calls the MCP analysis tool against the resulting .rdc, and verifies thumbnail and timeline exports.

Development

uv sync --extra dev
uv run python -m pytest
uv run ruff check src tests tools
uv run python tools/lint_skills.py

RenderDoc is an MIT-licensed graphics debugger maintained independently at renderdoc.org. This adapter is not affiliated with the RenderDoc project.

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured
E2B

E2B

Using MCP to run code via e2b.

Official
Featured