Loading ToolBench
MCP server for asset generation - image, video, audio, and 3D APIs for game development
The server defines 11 tools with reasonable descriptions and input schemas. Most tools have complete parameter definitions with types, enums, and constraints. However, there are significant gaps: (1) NO output schemas are documented anywhere, the rubric requires all tools to document return types; (2) error handling guidance is absent, no recovery hints or error classification; (3) descriptions could be more LLM-optimized (many are verbose or generic); (4) several parameters lack sufficient constraint details in descriptions; (5) no security guidance documented (these tools write files and call external APIs, so auth/validation/audit concerns are unaddressed). The server exhibits foundational quality but falls short of production-grade tool design in error handling, output specification, and security posture.
Edit images using FAL.ai's Qwen image editing model
Generate high-quality images using FAL.ai's Qwen image generation model. For transparency conversion, generate with solid white background (or black for dark objects/snowy scenes). The transparency converter uses color tolerance, so backgrounds close to pure white/black will be made transparent.
Generate images using Google's Gemini native image generation (supports 2.5 Flash and 3 Pro models), supports multiple input images for variations. For transparency conversion, generate with solid white background (or black for dark objects/snowy scenes). The transparency converter uses color tolerance, so backgrounds close to pure white/black will be made transparent.
Generate 3D models from text descriptions or image references using advanced 3D generation models. Supports multiple models (Trellis, Hunyuan3D, Hunyuan World) with different variants and automatic reference image generation.
Generate 3D models asynchronously with status tracking. Returns a job ID for polling progress on long-running 3D generation tasks.
NO output schemas documented. The rubric (D. Schemas & Output) mandates documenting return types so LLMs can plan downstream calls. Every tool in this server omits return schema specification, forcing LLMs to guess what fields and structure the tool produces.
No error handling guidance. Tools that call external APIs (OpenAI, Gemini, FAL.ai) will fail (rate limits, auth errors, timeouts), but there is no recovery hint or error classification in any tool description. Per the rubric, error responses must tell the LLM what to do next.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | C | 64 | 2026-07-28+ | v2 |
| 2026-03-09 | F | 0 | - | v1 |
Generate character sheets from text descriptions or reference images using any available model. Character sheets are generated with plain white backgrounds for transparency conversion support.
Generate character variations by combining reference images (e.g., character + outfit)
Generate object/item sprite sheets with multiple angles and states
Generate pixel art characters with specific dimensions for retro games, with optional transparent backgrounds. For transparency conversion, characters are generated with solid backgrounds (white by default, black for dark characters). The transparency converter uses color tolerance, so backgrounds close to pure white/black will be made transparent.
Generate seamless textures for game assets (wood, metal, stone, fabric, etc.)
Generate images using OpenAI's image generation API. For transparency conversion, generate with solid white background (or black for dark objects/snowy scenes). The transparency converter uses color tolerance, so backgrounds close to pure white/black (within tolerance range) will be made transparent.
Descriptions are verbose and lack LLM-optimized guidance. Many descriptions exceed 300 characters and restate transparency/background behavior repeatedly. The rubric baseline for A+ tools is 50-200 chars. Descriptions should concisely state WHAT, WHEN, and WHY, not implementation details.
Optional parameters lack clear dependency documentation. E.g., 'inputImagePath' in openai_generate_image and 'referenceImagePaths' in generate_character_variation are optional, but there is no description of when to use them or what they enable. Undocumented optional params cause LLM confusion.
No security or permission documentation. These tools write files to disk and call paid external APIs (OpenAI, Gemini, FAL.ai). There is no mention of: required API keys (credential injection), file path validation (path traversal risk), rate limits, or permission scopes. Production tools must declare security requirements.
Missing parameter constraints in descriptions. E.g., 'outputPath' appears in all 11 tools but has no guidance on relative vs absolute paths, required directories, file format expectations, or path traversal protections. 'model' and 'variant' parameters lack documentation of available options beyond enum values.