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runapi-ai

Nano Banana MCP Server

by runapi-ai

text_to_image

Generate images from text descriptions by creating RunAPI tasks using Nano Banana models. Parameters include aspect ratio, output format, and resolution.

Instructions

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

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
aspect_ratioNo
output_formatNo
output_resolutionNo
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?

With no annotations, the description must disclose behavioral traits. It states it creates a task and returns outputs, but does not mention that the operation is asynchronous (implied by task id), whether it is destructive, or any side effects like cost. The polling behavior via 'wait' parameter is not explained.

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?

The description is concise with a single sentence containing a parenthetical. It is front-loaded with the primary action. While not verbose, it lacks structure, but for a short description, it is adequate.

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 tool has 7 parameters, no output schema, and complex nested behavior (async task creation with polling), the description is far too sparse. It does not explain how to use the returned task id, what status results look like, or how the polling parameters interact. The description is incomplete for effective agent usage.

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 description coverage is only 29% (2 of 7 parameters described). The tool description adds no additional meaning to any parameter. It fails to compensate for the low coverage, leaving agents uninformed about crucial parameters like aspect_ratio, output_format, and output_resolution.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Create'), the resource ('a Nano Banana task on RunAPI'), and context ('text to image'). It also mentions return values. However, it does not differentiate from the sibling 'edit_image', which could be confused for a similar image creation tool.

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 guidance is provided on when to use this tool versus alternatives like 'edit_image' or 'get_task'. There are no prerequisites, required parameters, or context indicating typical use cases.

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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