dcc-mcp-renderdoc 0.3.2

RenderDoc capture and replay automation skills for DCC-MCP

dcc-mcp-renderdoc

Agent workflow

AI agents should use the shared gateway through dcc-mcp-cli; IDE users may continue to use the MCP endpoint. Prefer typed skills and tools over raw scripts.

Install or update the CLI

dcc-mcp-cli is the preferred control path for every shell-capable agent. If it is missing, ask the user before installing the latest official release:

# Linux/macOS
curl -fsSL https://raw.githubusercontent.com/dcc-mcp/dcc-mcp-core/main/scripts/install-cli.sh | sh

# Windows PowerShell
powershell -ExecutionPolicy Bypass -c "irm https://raw.githubusercontent.com/dcc-mcp/dcc-mcp-core/main/scripts/install-cli.ps1 | iex"

Keep an official build current through the release manifest:

dcc-mcp-cli update check
dcc-mcp-cli update apply

update apply downloads and stages the latest CLI for the next launch. It does not update a running dcc-mcp-server; update that server in its own environment.

dcc-mcp-cli dcc-types
dcc-mcp-cli list
dcc-mcp-cli search --query "<task>" --dcc-type renderdoc
dcc-mcp-cli describe <tool-slug>
dcc-mcp-cli call <tool-slug> --json '{"key":"value"}'

dcc-types reports release-catalog support; list reports live sessions. If a tool belongs to an inactive progressive skill, call dcc-mcp-cli load-skill <skill-name> --dcc-type renderdoc before retrying. For post-task improvement, attach a stable session id with --meta-json, query dcc-mcp-cli stats --range 24h --session-id <task-id>, then pass the bounded evidence to the review_skill_improvement prompt from dcc-mcp-skills-creator.

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.

Each adapter instance uses an OS-assigned MCP port and registers it for CLI discovery. Connect through the stable gateway at http://127.0.0.1:9765/mcp; set DCC_MCP_RENDERDOC_PORT only when a fixed direct endpoint is required.

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.