RenderDoc MCP Server

renderdoc-mcp

⚠️ Disclaimer

$\Large\color{#ff69b4}{\textsf{This is a personal }}$ $\Large\color{#ff69b4}{\mathtt{✨ vibe\text{-}coding ✨}}$ $\Large\color{#ff69b4}{\textsf{project —}}$ $\Large\color{#ff69b4}{\textsf{built for fun, not for production use.}}$

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MCP server for RenderDoc — let AI assistants analyze GPU frame captures (.rdc files) for graphics debugging and performance analysis.

Built on the Model Context Protocol, works with Claude Desktop, Claude Code, and any MCP-compatible client.

Features

• 42 tools covering the full RenderDoc analysis workflow
• 10 high-level tools for one-call analysis (draw call state, frame overview, diff, batch export, pixel region sampling, etc.)
• 3 built-in prompts for guided debugging
• Human-readable output — blend modes, depth functions, topology shown as names not numbers
• GPU quirk detection — auto-identifies Adreno/Mali/PowerVR/Apple-specific pitfalls from driver name
• Headless — no GUI needed, runs entirely via RenderDoc's Python replay API
• Pure Python — single pip install, no build step
• Supports D3D11, D3D12, OpenGL, Vulkan, OpenGL ES captures

Quick Start

1. Prerequisites

• Python 3.10+
• RenderDoc installed (download)
  • You need the renderdoc.pyd (Windows) or renderdoc.so (Linux) Python module
  • It ships with every RenderDoc installation

2. Install

git clone https://github.com/Linkingooo/renderdoc-mcp.git
cd renderdoc-mcp
pip install -e .

3. Find your `renderdoc.pyd` path

The Python module is in your RenderDoc install directory:

You need the directory containing this file.

4. Configure your MCP client

{
  "mcpServers": {
    "renderdoc": {
      "command": "python",
      "args": ["-m", "renderdoc_mcp"],
      "env": {
        "RENDERDOC_MODULE_PATH": "C:\\Program Files\\RenderDoc"
      }
    }
  }
}

{
  "mcpServers": {
    "renderdoc": {
      "command": "python",
      "args": ["-m", "renderdoc_mcp"],
      "env": {
        "RENDERDOC_MODULE_PATH": "C:\\Program Files\\RenderDoc"
      }
    }
  }
}

# Set the module path
export RENDERDOC_MODULE_PATH="/path/to/renderdoc"   # Linux/macOS
set RENDERDOC_MODULE_PATH=C:\Program Files\RenderDoc  # Windows

# Run
python -m renderdoc_mcp

Usage Examples

Once configured, just talk to your AI assistant:

"Open frame.rdc and show me what's happening in the frame"

"Find the draw call that renders the character model and check its pipeline state"

"Why is my shadow map rendering black? Check the depth pass"

"Analyze performance — are there any redundant draw calls?"

Typical Tool Flow

open_capture("frame.rdc")                     # Load the capture
├── get_capture_info()                         # API, GPU, known_gpu_quirks
├── get_frame_overview()                       # Frame-level stats and render passes
├── get_draw_call_state(142)                   # Complete draw call state in one call
├── diff_draw_calls(140, 142)                  # Compare two draw calls (with implications)
├── export_draw_textures(142, "./tex/")        # Batch export all bound textures
├── save_render_target(142, "./rt.png")        # Save render target snapshot
├── analyze_render_passes()                    # Auto-detect render pass boundaries
├── find_draws(blend=True, min_vertices=1000)  # Search by rendering state
├── sample_pixel_region(rt_id, 0,0,512,512)   # Scan RT region for NaN/Inf/negatives
├── pixel_history(id, 512, 384)               # Debug a specific pixel
├── export_mesh(142, "./mesh.obj")             # Export mesh as OBJ
└── close_capture()                            # Clean up

For performance and diagnostic analysis:

get_pass_timing(granularity="pass")      # Find most expensive render passes
analyze_overdraw()                        # Fill-rate pressure estimate
analyze_bandwidth()                       # Memory bandwidth estimate
analyze_state_changes()                   # Batching opportunities
diagnose_negative_values()               # Find NaN/Inf/negative color values (爆闪)
diagnose_precision_issues()              # R11G11B10, D16, SRGB mismatches
diagnose_reflection_mismatch()           # Reflection artifact diagnosis