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ilhankilic

YaparAI MCP Server

by ilhankilic

list_models

Browse all available AI generation models for image, video, and music, along with their credit costs.

Instructions

List all available AI generation models and their credit costs.

Returns a list of models with their mode identifier, description, credit cost, and type (image/video/music).

Useful for understanding what you can generate and how much it costs. No credits are charged for this operation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The main handler function for the 'list_models' tool. It creates a YaparAIClient and calls client.get_models() to fetch the list of available AI generation models with their credit costs.
    async def list_models() -> dict:
        """
        List all available AI generation models and their credit costs.
    
        Returns a list of models with their mode identifier,
        description, credit cost, and type (image/video/music).
    
        Useful for understanding what you can generate and how much it costs.
        No credits are charged for this operation.
        """
        client = YaparAIClient()
        return await client.get_models()
  • The client method `get_models()` that makes the actual HTTP GET request to /v1/public/models to retrieve model listings.
    async def get_models(self) -> dict:
        """List available models and their credit costs."""
        return await self._request("GET", "/v1/public/models")
  • Import of the list_models function from yaparai.tools.models into the server module.
    from yaparai.tools.models import list_models
  • Registration of list_models as an MCP tool via mcp.tool(list_models), placed under the '# Utility' section.
    mcp.tool(list_models)
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description explicitly states 'No credits are charged for this operation,' which is a key behavioral trait. It also describes the return structure. No annotations are provided, but the description covers important behavioral aspects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is four sentences, each serving a distinct purpose: stating the action, detailing the return information, noting utility, and clarifying no cost. No unnecessary words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has an output schema and no parameters, the description is complete. It explains the purpose, return fields, utility, and cost-free nature.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has zero parameters, so the description does not need to add parameter semantics. The baseline for 0 parameters is 4 per calibration.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it lists all available AI generation models with their credit costs and details (mode identifier, description, credit cost, type). This action is distinct from sibling tools like generate_image or generate_video, which actually generate content.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description indicates the tool is useful for understanding what can be generated and costs, implying it should be used before generation. It lacks explicit when-not-to-use or alternatives, but the context is 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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