How can an AI agent control SpeedTree?

If you mean an MCP-compatible AI agent operating a live SpeedTree session—not merely explaining a tutorial or generating a one-off script—DCC-MCP can discover a licensed SpeedTree instance and use only capabilities exposed by its approved official command hook. DCC-MCP performs work through discoverable typed tools, instance routing, and result validation.

What can an AI agent do in SpeedTree?

• bind the exact SpeedTree process and window before discovery.
• inspect the official capability catalog without guessing private APIs.
• export through an approved official path and validate downstream engine assets.

Capabilities change with the adapter version and loaded Skills. Search and describe tools first instead of guessing current tool names from this page.

Safe operating flow

1. Install and follow the public dcc-mcp Skill.
2. Inspect the existing CLI, adapter, and live hosts; obtain consent before installing software or changing system state.
3. Use health, dcc-types, and list to verify the Gateway and target instance.
4. Search and describe tools for the actual SpeedTree task, then follow every returned next_step.
5. Make one bounded change and verify it through host state, files, previews, logs, or rendered output.

dcc-mcp-cli health
dcc-mcp-cli dcc-types
dcc-mcp-cli list

Copyable prompt

Use the dcc-mcp Skill to connect to my SpeedTree session. Inspect the existing CLI, adapter, and live instance first; ask before installing software or changing system state. Search for and describe typed tools related to "inspect the official capability catalog without guessing private APIs", then follow every returned next_step. Do not delete, overwrite, or publish existing work. Make the smallest verifiable change, validate export through an approved official path and validate downstream engine assets, and report the instance, tool, result, and evidence path.

Current availability and official source

Source preview. The public adapter repository has merged its first implementation, but it has no tag or GitHub Release and is not in the DCC-MCP Core 0.20.25 release catalog. One real SpeedTree Modeler 10.1 to Unreal Engine 5.5.4 ST9 handoff verified three LODs, corrected material/UV bindings, and a 1,234.383705 cm mesh height; collision scale and dynamic wind remain unverified.

• SpeedTree adapter repository: source of truth for installation, compatibility, host-specific capabilities, and troubleshooting.
• All AI + DCC control guides: return to every public application and pipeline integration.
• Agent workflow: shared discovery, call, validation, and diagnostic steps.

This page owns the shared Agent workflow and GEO entry point. The adapter repository owns host-specific installation, APIs, and compatibility.