Skip to main content
Glama
Sign In
nano-banana

nano-banana

by baiano
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

list_models

List all image generation models available for Nano Banana so users can choose a model before generating images.

Instructions

List all available image generation models for Nano Banana.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "list_models"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

B3.4/5.0
Behavior3/5

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

With no annotations provided, the description carries the full behavioral burden, and 'List all available' does convey an exhaustive, non-mutating read. However, it says nothing about the shape of a model entry, whether authentication or a specific account/workspace is required, or whether results are catalog-stable, leaving real gaps for a no-annotation tool.

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 with no filler; the verb and resource come first and every word earns its place. Nothing could be trimmed without losing meaning.

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 trivial zero-parameter read tool this is nearly adequate, but with no output schema and no annotations the description could usefully say what identifying information a listed model carries (e.g., names/IDs to pass to set_model). As written, an agent knows what happens but not what it will get back or how to act on it.

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

Parameters4/5

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

The tool takes zero parameters, so the baseline of 4 applies; there is no parameter whose semantics need explaining. Nothing in the description contradicts or adds confusion on this dimension.

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 pairs a specific verb ('List') with a specific resource ('all available image generation models'), and the word 'List' readily distinguishes it from siblings generate_image and set_model. It stops short of explicitly naming those siblings, but the verb alone is differentiating.

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?

There is no statement of when to use this tool versus set_model or generate_image, and no prerequisites or ordering guidance. The natural 'discover a model, then set it' workflow is left entirely to inference, so an agent gets no real routing guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server

Other Tools