ai_modelsList AI Studio image and video models with inputs, settings, credit prices, templates, spendable credits, and consent status before generating.
The AI Studio: every image and video model with its inputs, settings and price in credits, the ready templates, the credits AI can spend, and whether the one-time consent is done. Call this before ai_generate.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly/idempotent/non-destructive/openWorld, so safety is covered; the description adds real behavioral content beyond them by disclosing that the payload includes credit balance, model pricing, templates and consent state. It does not mention rate limits, auth requirements or output shape, but for a zero-parameter read tool the added content is substantive.
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?
One dense sentence plus a short imperative, with the 'call before ai_generate' instruction front-adjacent rather than buried. The opening label 'The AI Studio:' is decorative rather than informative, a small waste in an otherwise tight definition.
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 carries the burden of explaining what comes back, and it does so by enumerating models, templates, credits and consent. Combined with a zero-parameter schema and full annotation coverage, an agent has enough to invoke it correctly, though nothing is said about response size or structure.
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 tool takes no parameters, so there is no argument semantics to document and the baseline is 4. The description correctly implies a no-argument, whole-catalog fetch rather than a filtered query.
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 enumerates exactly what the call returns: every image/video model with its inputs, settings and credit price, ready templates, spendable credits, and one-time consent status. It is clearly a read-only discovery endpoint and is distinguishable from siblings like ai_price, ai_list and ai_generate, though it opens with a noun phrase ('The AI Studio') rather than a verb+resource.
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?
It gives explicit ordering guidance with 'Call this before ai_generate,' which tells the agent the sequencing relative to its most important sibling. It does not mention the other nearby alternatives (ai_price, ai_list) or state any when-not-to-use conditions, so the coverage is clear but not exhaustive.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.