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Price a generation

estimate_generation
Read-onlyIdempotent

Price a generation without making it. Free, changes nothing. Takes the same arguments as generate (without expected_cost) and answers the coins it would cost, the coin balance and the settings that price is for (resolution, duration, audio). Every check generate makes runs here too, so a refused request is found before the person is asked anything. Pass the answered coins to generate as expected_cost.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kindYeswhat to make: image | video | speech | music. Each argument below says which kinds take it
linesNospeech: the script, in order — each line is {speaker, text}
modelYesa model id from list_models for this kind
styleNomusic: the style — a genre, a mood, the instruments (custom songs)
titleNomusic: the song's title (custom songs)
imagesNoimage, video: hosted image URLs — references for an image model; for a video model the first frame, or with images_are_references the subject/style references (at most the model's max_images). Import a link with import_media first
lyricsNomusic: the words to be sung. With lyrics, title and style are required
promptNoimage, video: what to make. music: the description of the song (simple mode; for a song with its own lyrics use lyrics)
durationNovideo: seconds, one of the model's durations. music: the song's length in seconds (custom songs only)
speakersNospeech: the voices, in order (list_models has the voices, accents, styles and paces). Each is {name, voice, accent, style, pace, profile}, all optional except that a script needs at least one speaker
last_frameNovideo: a hosted image URL for the LAST frame — only for models with supports_transition; then images holds exactly the first frame
resolutionNoimage, video: one of the model's resolutions — a higher tier costs more
aspect_ratioNoimage, video: the frame, one of the model's aspect_ratios (e.g. 16:9)
instrumentalNomusic: no vocals
generate_audioNovideo (and any image model that has it): make the sound too — only for models with supports_audio, and it costs audio_cost more
reference_audioNovideo: a hosted audio URL to take sound from — only for models with max_reference_audios above 0
reference_videoNovideo: a hosted video URL to take motion or style from — only for models with max_reference_videos above 0
images_are_referencesNovideo: read images as references to draw from, NOT as the first frame — the video does not start on them. Needs at least one image, cannot be combined with last_frame, and only models with supports_reference take it

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
vNo
kindYes
coinsYes
modelYes
enoughNo
balanceNo
settingsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already cover read-only/idempotent/non-destructive, but the description adds real value: free, changes nothing, runs every validation generate runs, and mirrors generate's argument contract. It partly restates return content that the output schema already documents, which caps it below 5.

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?

Front-loaded with purpose and safety, and each sentence carries information (contract, validation parity, workflow). Slightly long, and the enumerated return fields partly duplicate the output schema, so not perfectly tight.

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 pricing tool with an output schema and full annotation coverage, everything needed is present: it is safe/free, validated identically to generate, returns a cost, and feeds expected_cost into the subsequent generate call.

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, but the description adds a meaningful contract fact beyond the schema: it takes the same arguments as generate without expected_cost. That mapping lets an agent reuse generate's parameter knowledge rather than re-deriving it.

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 ('Price a generation') and immediately distinguishes it from the sibling it could be confused with ('without making it'), i.e. generate. An agent can tell it apart from generate without opening either schema.

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?

Gives a clear when-to-use: run it before generate so refused requests are caught early, then pass the returned coins as expected_cost. No explicit when-not-to-use versus estimate_run/estimate_upscale, but the workflow context is strong.

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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