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gemini-image-mcp

gemini-image-mcp

by someshwarpatil
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

list_models

Find all available image generation models, their Vertex AI IDs, and usage notes.

Instructions

List the friendly model aliases, their real Vertex ids, and usage notes.

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

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.3/5.0
Behavior3/5

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

No annotations are provided, so the description must disclose behavioral traits. It states the tool lists information but does not mention whether it is read-only, cached, or subject to any limitations. The output schema fills some gaps, but additional details on side effects or constraints would be beneficial.

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, well-structured sentence that immediately conveys the tool's purpose. No unnecessary words; every part is relevant.

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

Completeness5/5

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

Given zero parameters and the existence of an output schema, the description fully covers the tool's purpose and output. The context of sibling tools further clarifies its role. No additional details are needed.

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 input schema has zero parameters, so schema coverage is 100%. The description adds value by specifying what the output contains (friendly aliases, real IDs, usage notes), which is more than the empty schema provides. Baseline is 4 due to full coverage.

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 uses the verb 'List' and specifies the exact resource: friendly model aliases, their real Vertex IDs, and usage notes. This distinguishes it from sibling tools that edit or generate images.

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

Usage Guidelines4/5

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

Although no explicit when/when-not guidance is given, the context of sibling tools (edit_image, generate_image) makes it clear that this tool is for listing model information. A brief note on when to use it instead of other tools would improve clarity.

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

Deploy Server

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