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Generate an image on emaki

generate_image

Create an image from a text prompt and get its URL back, using your emaki credits. Use the result as a first frame for video generation.

Instructions

Generate an image from a text prompt on emaki.ai and return its URL. Defaults to Nano Banana 2 Lite, the lowest-cost model; a good way to make a first frame for generate_video. Runs on your emaki credits.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
waitNoBlock until the image is ready. Default true.
modelNoDefault nano_banana_2_lite.
promptYesWhat the image should show.
qualityNo'basic', 'high' or 'ultra' (seedream_5_lite).
resolutionNo'1K', '2K' or '4K' (nano_banana_2 and gpt_image_2).
aspect_ratioNoe.g. '1:1', '16:9', '9:16', '4:3', '3:4', '2:3', '3:2'.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.9/5.0
Behavior3/5

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

With no annotations, the description carries the full burden and does disclose some behavior: it returns a URL and consumes emaki credits. It omits key operational traits such as what happens when wait=false (likely a job needing check_status), latency expectations, or failure/auth behavior.

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?

Three short sentences, no filler, with the core action and return value front-loaded. Every sentence contributes (action, default/cost, downstream use).

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

Completeness3/5

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

It covers what is returned and the credit cost, but with no annotations and no output schema, the description should explain the async contract implied by the wait parameter and check_status sibling. Six parameters and a cost-bearing operation warrant more disclosure than is present.

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 the baseline is 3 and the schema already documents prompt, model, wait, quality, resolution, and aspect_ratio. The description only adds cost rationale ('lowest-cost model') for the default model, offering no guidance on when to pick other enum values.

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 ('Generate an image from a text prompt on emaki.ai'), names the platform, and declares the return value (a URL). It also differentiates itself from the sibling generate_video by positioning itself as the source for a first frame.

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 clear usage context: it is the lowest-cost option and a good way to make a first frame for generate_video, which routes the agent between siblings. It does not, however, state when not to use it or how it compares to other image-producing paths.

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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