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runapi-ai

Nano Banana MCP Server

by runapi-ai

get_task

Poll the status and retrieve the latest result payload for an asynchronous Nano Banana image editing or text-to-image task using its task ID and action.

Instructions

Fetch the current status and latest result payload for a nano-banana task.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
actionYesAsynchronous endpoint the task was created on.
task_idYesTask id returned when the task was created.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.1.12
    • changedInput schema / properties / action / description
      Previous value: -"Endpoint the task was created on."New value: +"Asynchronous endpoint the task was created on."
  2. First observedv0.1.0

TDQS

B3.3/5.0
Behavior2/5

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

With no annotations, the description carries the full behavioral burden. It discloses that it returns the 'current status and latest result payload' but says nothing about authentication requirements (notable given a 'login' sibling), polling behavior, terminal task states, or error handling for invalid task ids.

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?

A single front-loaded sentence states the action, the resource, and what is returned, with no filler. Every clause earns its place.

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?

For a two-parameter read tool with 100% schema coverage and no output schema, the description is minimally sufficient, but with no annotations it should do more to explain return semantics (e.g., pending vs. completed shapes) and auth expectations. The gaps are modest rather than severe.

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%: task_id and action are both documented in the schema, including the enum values for action. The description adds no syntax, format, or origin detail beyond what the schema already states, so the baseline of 3 applies.

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?

States a specific verb ('Fetch') and resource ('current status and latest result payload for a nano-banana task'), which is precise enough for an agent to know what it retrieves. It does not explicitly differentiate itself from siblings like edit_image or text_to_image, though the 'task' framing implies post-creation polling.

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?

Usage is only implied: the reference to a 'task' and the schema's async-endpoint action enum suggest this is a polling tool used after creating a task. There is no explicit guidance on when to call it, how often, or what to do instead if the task does not exist yet.

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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