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cainmaila

gemini-cli-mcp

by cainmaila

Execute Image Task

executeImageTask

Generate images from text prompts through the nanobanana extension, yielding saved file paths and execution metadata for automated workflows.

Instructions

Uses the nanobanana extension to generate images and returns saved image paths plus execution metadata.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cwdNoOptional working directory for generated image output
countNoOptional number of images to generate
promptYesImage generation prompt for the nanobanana extension
stylesNoOptional style names passed to nanobanana
timeoutMsNoOptional timeout in milliseconds
variationsNoOptional variation names passed to nanobanana
approvalModeNoOptional Gemini CLI approval mode. Defaults to yolo for nanobanana.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
okYes
argsYes
signalYes
stderrYes
stdoutYes
abortedYes
commandYes
exitCodeYes
timedOutYes
elapsedMsYes
imagePathsYes
errorMessageNo
responseTextYes
primaryImagePathNo
workingDirectoryNo
effectiveApprovalModeNo

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv1.0.1

TDQS

A3.7/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It does reveal that images are saved and that metadata is returned, which is useful, but it omits caveats about external dependencies, failure behavior, or approval modes. Transparency is partial but not absent.

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 a single front-loaded sentence with no wasted words. It efficiently communicates the mechanism, the operation, and the output type.

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?

The description is adequate for identifying the tool's core purpose, and the output schema covers return details. However, without annotations or usage guidance, and with several sibling tools, the description leaves gaps around when to choose this tool and what side effects to expect beyond saving images.

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 schema already documents all seven parameters. The description adds no additional parameter-level semantics beyond what the schema provides, which is the baseline case.

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 states a specific action ('generate images') and a specific resource ('nanobanana extension'), and it also states what is returned ('saved image paths plus execution metadata'). This clearly differentiates it from the generic sibling tools executeTask and executePrompt.

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 description implies use for image-generation tasks through nanobanana, but it gives no explicit when-to-use or when-not-to-use guidance. It does not mention alternatives or exclusions, so the agent must infer the appropriate context.

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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