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Preview cost

dry_run_cost
Read-onlyIdempotent

Estimate the cost of a task without creating it or charging your account. Pass the model and full input payload to preview pricing before generation.

Instructions

Estimate the cost of a task without creating it or charging the account. Pass the model and the full input payload.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
inputYesThe API 'input' payload to price — the RAW field names the model expects, which differ from the generation tools' parameters. Include prompt and any model-specific fields. For a model's exact field names, read its page via https://docs.unifically.com/llms.txt (they change, so don't go from memory). Translations this server applies on your behalf: music prompt→gpt_description_prompt (+ required mv:'chirp-bluejay', custom:false); TTS voice_id→voice; stt needs audio_url + duration; the generation tools' image_url becomes whichever start-frame field the model wants. A mis-shaped input prices the model's defaults, so keep it faithful to the real call.
modelYesModel id or alias, e.g. 'google/veo-3.1-fast' or 'veo'.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.1.0

TDQS

A3.8/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint, so the safety profile is covered. The description adds the meaningful guarantee of no account charge, but says nothing about rate limits, validation failures, or how the estimate relates to actual billing.

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?

Two tight sentences, zero filler, with the differentiating constraint (no creation, no charge) front-loaded ahead of the parameter instruction.

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?

For a two-parameter read-only tool with a 100% documented schema and full annotation coverage, the definition covers purpose and the no-side-effect guarantee adequately. It stops short of noting what the estimate returns or how closely it tracks final billing, but those are minor given no output schema exists.

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?

Schema description coverage is 100%, so both parameters are already thoroughly documented (the nested 'input' description is exceptionally rich, covering translations and docs lookup). The description's 'Pass the model and the full input payload' adds no meaning beyond that baseline.

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+resource ('Estimate the cost of a task') and immediately scopes it against the generation siblings by clarifying it neither creates the task nor charges the account. An agent can distinguish it from generate_video/image/audio/music without opening the schema.

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?

The phrase 'without creating it or charging the account' implies the use case (preview price before committing to a generation), but no alternative is named and no explicit when/when-not condition is given. Usage must be inferred from the contrast with the generation tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.