list_modelsLook up the AI models PopNow offers (image, video, audio) or its video effects, with prices and the PopNow tool to use for each. Read-only; it doesn't create anything.
| Name | Required | Description | Default |
|---|---|---|---|
| model | No | Optional model id: show what that model can do, which PopNow tool does it, and the settings each one accepts. | |
| category | No | image, video, audio or effects. Default image. |
Changes observed during successful MCP inspections.
Input schema / properties / model / descriptionPrevious value: -"Optional model id: show everything that model can do and the exact inputs each task takes (use this before run_model)."New value: +"Optional model id: show what that model can do, which PopNow tool does it, and the settings each one accepts."Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so 'read-only; it doesn't create anything' largely restates structured data. It does add value by disclosing the return content (prices and the corresponding PopNow tool per model), which annotations don't cover, but offers no detail on result format beyond that.
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 compact sentences that front-load the purpose and then the return content. The trailing 'it doesn't create anything' is mildly redundant against the readOnlyHint annotation, but it costs little.
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?
With no output schema, the description usefully states what a call returns (models/effects, prices, the PopNow tool to use). For a two-optional-parameter lookup tool this is nearly sufficient; only finer detail on result structure is absent.
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?
Schema coverage is 100% and both parameters are documented in the schema, including the enum and default for category. The description mentions the image/video/audio/effects scope but adds no syntax or behavioral nuance for the model id parameter, so the baseline 3 applies.
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?
Specific verb ('look up') plus a precisely scoped resource (PopNow's AI models and video effects), with what's returned (prices, the PopNow tool to use for each). This clearly separates it from the many creation/manipulation siblings like create_image or apply_effect, which produce artifacts rather than catalog metadata.
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?
Implies the usage context well: an agent consults this to learn which tool and settings to use for a given model, i.e. before invoking a creation tool. It does not explicitly name alternatives or state when not to use it, but the 'which PopNow tool does it' framing makes its role as a discovery step clear.
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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