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.
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
.rdccapture. - 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.
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。