Infinigen MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| generate_nature_sceneC | Generate a photorealistic natural outdoor scene using Infinigen. Creates terrain, vegetation, and natural elements. |
| generate_indoor_sceneC | Generate a photorealistic indoor scene with furniture and decorations using Infinigen. |
| check_infinigenB | Check if Infinigen is properly installed and accessible |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 3 tools
Each tool has a clearly distinct purpose: one checks installation status, one generates indoor scenes, and one generates outdoor scenes. There is no overlap or ambiguity between these functions, making tool selection straightforward for an agent.
All tool names follow a consistent verb_noun pattern (check_infinigen, generate_indoor_scene, generate_nature_scene). The naming is uniform and predictable, using snake_case throughout without any deviations.
With only 3 tools, the server feels thin for a domain like scene generation, which might benefit from more operations (e.g., editing scenes, listing generated assets, or configuring parameters). However, it covers basic functionality without being excessive.
The tools cover installation checks and two core generation tasks (indoor and outdoor scenes), but there are notable gaps. Missing operations include scene editing, asset management, or parameter tuning, which could limit agent workflows in more complex scenarios.