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runapi-ai

Nano Banana MCP Server

by runapi-ai

edit_image

Create an image editing task with Nano Banana AI. Specify aspect ratio and output format, then receive task ID, status, and image URLs.

Instructions

Create a Nano Banana task on RunAPI (edit image). Returns a task id, status, and output URLs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
aspect_ratioNo
output_formatNo
waitNoPoll until the task reaches a terminal status.
timeout_msNo
poll_interval_msNo
modelNoRunAPI model slug for this model line.
Behavior2/5

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

The description mentions returning a task id and status, implying async behavior, but does not explain the task lifecycle, how to provide an input image (missing from schema), or the role of parameters like wait and timeout_ms. No annotations exist to supplement this.

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 sentence that efficiently communicates the primary action and return value. No extraneous information is present.

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

Completeness2/5

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

Given the complexity of an async task-based tool with 6 parameters, the description is too minimal. It omits crucial details like how to provide the image, how to use polling/wait mechanisms, and what the output URLs represent.

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

Parameters2/5

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

Schema coverage is only 33% (2 of 6 parameters have descriptions). The description adds no parameter-level information, so it fails to compensate for the low coverage. The lack of an image input parameter in the schema is puzzling and not addressed.

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 clearly states the tool creates a Nano Banana task for editing images, distinguishing it from sibling tools like text_to_image which generates new images. The verb 'Create' and resource 'edit image' are specific and unambiguous.

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

Usage Guidelines2/5

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

No explicit guidance is provided on when to use this tool versus alternatives like text_to_image or get_task. The description only states what it does, not the context or prerequisites for using it.

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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