Returns available models and their cost in credits (images, video, audio). Use before generating to pick the right model.
This tool declares no required parameters. Add optional parameters from the input schema as needed.
{
"id": 1,
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"arguments": {},
"name": "fattly_list_models"
}
}| Name | Required | Description | Default |
|---|---|---|---|
| category | No | Optional category filter. |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description usefully adds that the return contains models and credit costs, but it does not describe the response format or any data freshness details.
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?
Two short sentences deliver the core purpose first and then add a practical usage cue. There is no filler or redundant information.
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 simple, read-only listing tool with one optional parameter and no output schema, the description adequately covers what is returned and when to call it. The exact response shape is not described, but the low complexity makes that a minor gap.
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?
The schema already provides 100% coverage for the single optional category parameter, including its enum values. The description only repeats the category names in parentheses without adding additional parameter semantics.
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 clearly states the tool returns available models and their credit costs, covering image, video, and audio categories. This makes it easily distinguishable from the many generation-focused sibling tools.
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?
The description explicitly says to use the tool before generating to pick the right model, which gives clear usage timing. However, it does not mention when not to use it or contrast it with other listing tools like fattly_list_ad_presets.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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