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Clipia

by io.github.clipia-ai
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get_model

Read-onlyIdempotent

Get model details and pricing. Generation models include input_schema.parameters (name/type/options with human-readable labels, defaults and ranges — use them for the input object: they are the parameters the model accepts, and a generate call with a misspelled name or a parameter the model is known not to apply is rejected; some of them change the cost) and reference pricing (TTS per 1000 characters, plus text_limits for the allowed text length); text models include context limits and per-1K input/output credit rates.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "model": "<model>"
    },
    "name": "get_model"
  }
}

Input Schema

NameRequiredDescriptionDefault
modelYesModel slug from list_models. Slugs may contain '/' (e.g. 'google/imagen4', 'midjourney/v7') — pass them verbatim.
localeNoLanguage of human-readable labels/hints in input_schema (parameter names and values are language-independent).en

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnly/idempotent/non-destructive, yet the description still adds real value: some parameters change cost, invalid parameter names cause rejection, TTS pricing is per 1000 characters, and text_limits bound allowed input length. It stops short of covering behavior on an invalid slug or auth requirements, so not a 5.

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

Conciseness3/5

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

Purpose is front-loaded, but the body is a single sprawling sentence with nested parentheses that is hard to parse at a glance. Every clause carries information, but the packaging undercuts readability.

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?

With no output schema, the description carries the return-value burden and does so: it specifies exactly what input_schema, reference pricing, text_limits, context limits, and per-1K credit rates contain for each model family. An agent knows what it will get back and how to use it.

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?

Schema coverage is 100%, so the baseline is 3. The description goes beyond it by explaining that input_schema.parameters are the accepted parameters for generation, that names must match exactly, and that locale governs human-readable labels — meaningful semantics the schema alone only partially conveys.

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?

States a specific verb and resource ('Get model details and pricing') and enumerates the two content families returned: generation models (input_schema.parameters, reference pricing, text_limits) and text models (context limits, per-1K credit rates). This clearly separates it from list_models, which only enumerates slugs.

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

Usage Guidelines3/5

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

Usage is implied rather than stated: it hints that the returned input_schema is what you use to build a generate call, and that misspelled or unsupported parameter names get rejected. But it never explicitly says when to call this versus list_models, nor that the 'model' slug must originate from list_models (that guidance lives only in the schema).

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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