AIGigLab MCP Server

generate_asset

Generate images from text prompts using AI models, with optional aspect ratio and image-to-image support.

Instructions

Generate an image asset using AI models.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "prompt": "<prompt>"
    },
    "name": "generate_asset"
  }
}

Input Schema

Name	Required	Description	Default
`prompt`	Yes	The text prompt for image generation
`modelId`	No	The ID of the AI model to use. Defaults to 'google/nano-banana-pro'.	google/nano-banana-pro
`image_file`	No	Absolute local path for image-to-image generation
`aspectRatio`	No	Aspect ratio (e.g., '1:1', '16:9'). Default depends on model.
`image_content`	No	Base64 encoded image content for image-to-image

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.3.0Jul 15, 2026

TDQS

C2.8/5.0

Behavior2/5

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

With no annotations, the description must fully disclose behavioral traits. It only states it generates an image, omitting details about permissions, rate limits, potential destruction, or any side effects. This is insufficient for a mutation tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single concise sentence, but it is overly terse and misses important contextual information. It is not front-loaded with the most critical details.

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 5 parameters, no output schema, and no annotations, the description is very incomplete. It does not explain what the tool returns, how to interpret results, or any constraints. An agent would lack sufficient context to use it effectively.

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%, so the baseline is 3. The description adds no additional meaning beyond what the schema already provides for each parameter. It is adequate but not improving.

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 tool generates an image asset using AI models, with a clear verb and resource. However, it does not differentiate from sibling tools like brandos_generate_asset or generate_thumbnail_image, which have similar purposes.

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, nor any conditions or prerequisites. The description lacks any contextual usage advice.

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