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wiroai

Wiro MCP Server

Official
by wiroai

Get a Wiro model schema

get_model_schema
Read-onlyIdempotent

Retrieve a model's typed parameters, defaults, required fields, pricing, and catalog fields to correctly configure API calls and chain stateful next actions.

Instructions

Get one model’s typed parameters, options, defaults, required fields, and pricing. Catalog fields such as previousTaskToken must be discovered here; use it with toolOutputs for tool results or with one new prompt/messages value for a stateful next turn. Fill required parameters and follow the returned run_model next action exactly once.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelYesModel slug in "owner/model" format, e.g. "openai/sora-2", "black-forest-labs/flux-2-pro".

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelYes
nextActionYes
Install Server

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnly, openWorld, idempotent, non-destructive, so the description does not need to restate those. It adds behavioral value by revealing that the response contains a run_model action to follow exactly once and demonstrating how previousTaskToken should be used. No contradiction with annotations.

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

Conciseness4/5

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

The primary action is front-loaded in the first sentence, and the following sentences earn their place by describing the workflow. The second sentence is dense and slightly ambiguous ('use it with toolOutputs'), but overall the text is appropriately sized with no filler.

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

Completeness4/5

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

With an output schema present and strong annotations, the description covers what the returned schema includes and how to proceed (fill required parameters, follow run_model once). It does not spell out error handling or non-existent model slugs, but nothing critical for correct invocation appears missing.

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

Parameters3/5

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

The only parameter, `model`, has 100% schema description coverage with format and examples, so the baseline is 3. The description does not add new input-parameter semantics beyond the schema; its extra detail targets the returned schema content rather than the `model` argument.

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 opening sentence names the verb 'Get', a specific resource ('one model’s typed parameters, options, defaults, required fields, and pricing'), and clearly distinguishes this from siblings like run_model/search_models by framing it as schema discovery. The mention of catalog fields and the returned `run_model` action leaves no ambiguity about the tool’s purpose.

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?

It gives clear context: this is the prerequisite to run a model ('Follow the returned run_model next action exactly once') and states that catalog fields must be discovered here. It lacks explicit exclusions or comparisons with sibling alternatives such as explore or search_models, but it does convey when it should be used in the workflow.

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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