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Wiro MCP Server

Official
by wiroai

Run a Wiro model

run_model

Run any AI model on Wiro for generation, editing, or analysis across image, video, text, audio, and 3D. Provide the model slug and parameters to execute a task and return its output.

Instructions

Run any AI model on Wiro. Supports image, video, text, audio, 3D, and more. Call get_model_schema first. With wait=true this performs a bounded wait; when the response contains nextAction.tool = "wait_for_task", call that tool with the exact arguments returned. Do not resubmit the original request. A completed tool-call turn instead returns a run_model continuation template; execute its calls, fill its toolOutputs, and invoke it once. When the task completes, present every returned media resource in the user-facing response instead of reporting only task metadata.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
waitNoIf true, poll until completion and return the result. If false, return the task identifiers immediately.
modelYesModel slug in "owner/model" format, e.g. "openai/sora-2", "google/nano-banana-pro"
paramsYesModel-specific parameters as key-value pairs. Use get_model_schema to discover available parameters. Pass json and json-array parameters as structured JSON values in this same object. Continue a completed turn with previousTaskToken: add toolOutputs rows with call_id and output for tool results, or provide exactly one new prompt or non-empty messages value for a stateful next turn. For file parameters (fileinput, multifileinput, combinefileinput), pass URLs directly — no upload needed. For combinefileinput, pass an array of URLs.
timeout_secondsNoMaximum seconds to wait when wait=true. The 45-second default is safe for clients with a 60-second tool timeout.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
taskYes
errorNo
stateYes
outputsYes
responseNo
nextActionNo
Install Server

TDQS

A5/5.0
Behavior5/5

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

Despite annotations already indicating non-readonly and non-idempotent behavior, the description adds crucial operational context: bounded waits, continuation mechanics, file parameter URL handling, and the requirement to render media results to the user. These details go well beyond the structured annotation fields and materially shape how the agent must behave.

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 dense but every sentence carries operational weight. It front-loads the core action, then walks through the async and continuation flows in a logical order without repeating schema content or including filler.

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?

For a tool this complex—with async behavior, continuations, file params, and a rich output schema—the description covers the essential call flow, edge cases, and user-facing obligations. The presence of an output schema and sibling tools further supports a complete context without needing to restate return structures.

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

Parameters5/5

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

Schema coverage is 100% and each parameter already has a description, but the tool description adds high-value semantics: how to pass JSON values, how to continue a completed turn with toolOutputs or messages, and how to handle file input types including combinefileinput arrays. This meaningfully supplements the schema.

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 opens with a crisp verb+resource statement: 'Run any AI model on Wiro' and enumerates supported modalities. It clearly identifies the tool as the execution entry point, and sibling tools like get_model_schema and wait_for_task are contextually distinct without needing to be named.

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

Usage Guidelines5/5

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

The description gives explicit procedural guidance: call get_model_schema first, use wait=true for bounded polling, delegate to wait_for_task when nextAction appears, do not resubmit, and complete continuation templates. It also distinguishes the completed-tool-call flow from the initial invocation, which is essential for correct use.

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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