How can an AI agent control Katana?

If you mean an MCP-compatible AI agent operating a live Katana session—not merely explaining a tutorial or generating a one-off script—DCC-MCP can inspect node graphs and automate look development, lighting, scene assembly, and render setup. DCC-MCP performs work through discoverable typed tools, instance routing, and result validation.

What can an AI agent do in Katana?

• inspect the scene graph and active node graph.
• apply a bounded look-development change.
• validate assignments and a test render.

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 Katana 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 Katana 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 "apply a bounded look-development change", then follow every returned next_step. Do not delete, overwrite, or publish existing work. Make the smallest verifiable change, validate validate assignments and a test render, and report the instance, tool, result, and evidence path.

Current availability and official source

The current release catalog uses katana as the host identifier. Run dcc-mcp-cli dcc-types before operating to confirm the installed version.

• Katana 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.