Generate high-quality images from text prompts using advanced AI models. Create custom visuals by specifying size, model, and output format.
Generate high-quality images from text prompts using advanced AI models (~12 credits)
| Name | Required | Description | Default |
|---|---|---|---|
| seed | No | ||
| size | No | ||
| model | No | ||
| prompt | Yes | ||
| num_images | No | ||
| output_format | No | ||
| guidance_scale | No | ||
| num_inference_steps | No | ||
| enable_safety_checker | No |
Changes observed during successful MCP inspections.
v1.0.2Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry the full behavioral burden. It discloses the cost (~12 credits) and that it invokes 'advanced AI models', but says nothing about model selection behavior, whether the call is synchronous, safety-checker behavior, output format/location, or failure modes.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single front-loaded sentence with no filler, which is efficient for the amount of information it conveys. It is not verbose, though it is arguably too terse for a 9-parameter tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a generation tool with nine parameters, no annotations, and no output schema, the description omits nearly everything an agent would need: parameter meaning, model options, output format, and how it differs from the numerous sibling generators.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Nine parameters exist with 0% schema description coverage, and the description only alludes to 'text prompts'. Seed, size, model, num_images, output_format, guidance_scale, num_inference_steps, and enable_safety_checker are completely undocumented in both the schema and the description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description names a specific verb and resource ('Generate high-quality images from text prompts'), so the core purpose is unambiguous. However, it does not distinguish this generic generator from the many image-producing siblings (nano_banana, ghibli_generator, headshot_generator, product_photo_studio), leaving the agent to guess which one to pick.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus the sibling image generators, no prerequisites, and no exclusions. The only extra context given is a cost hint (~12 credits), which is not usage guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.