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Image MCP Server

Image MCP Server

by iplanwebsites
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Remote

generate_ai_image

Create AI-generated images from text prompts using customizable dimensions and quality settings through the Image MCP Server.

Instructions

Generate AI images using the ai-image npm module

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>",
      "output_dir": "<output_dir>"
    },
    "name": "generate_ai_image"
  }
}

Input Schema

NameRequiredDescriptionDefault
promptYesThe text prompt for image generation
sizeNoImage size in format WIDTHxHEIGHT (e.g., 1536x1024)1024x1024
modelNoAI model to use for generation (optional)
outputNoOutput file path (optional)
output_dirYesAbsolute path direcotry where to save the image (use current folder root by default)
qualityNoImage quality (low, medium, high, auto) - OpenAI onlyhigh
uselibincliNoUse library imports instead of CLI (default: false)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.0.13

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the implementation module ('ai-image npm module') but doesn't describe key behavioral traits such as whether this is a read-only or mutation operation, what permissions or authentication might be required, rate limits, error handling, or what the output looks like (e.g., file saved locally, URL returned). For a tool with 7 parameters and no annotations, this is a significant gap.

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, efficient sentence that directly states the tool's purpose and implementation method without any unnecessary words or structural issues. It's appropriately sized and front-loaded, with every part earning its place.

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 complexity (7 parameters, no annotations, no output schema), the description is incomplete. It doesn't address behavioral aspects like mutation effects, authentication needs, or output format, and it lacks usage guidance relative to sibling tools. For a tool with this level of complexity and no structured support, the description should provide more context to be fully helpful.

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?

The schema description coverage is 100%, meaning all parameters are documented in the schema itself. The description adds no additional parameter semantics beyond what's in the schema (e.g., it doesn't explain parameter interactions, default behaviors, or constraints not captured in schema descriptions). According to the rules, with high schema coverage (>80%), the baseline is 3 even with no param info in the description.

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 ('Generate AI images') and specifies the implementation method ('using the ai-image npm module'), which provides a specific verb+resource combination. However, it doesn't distinguish this tool from its sibling tools (landscape_image, portrait_image, square_image), which likely serve similar image generation purposes but with different constraints or outputs.

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?

The description provides no guidance on when to use this tool versus its siblings or alternative approaches. It mentions the implementation module but doesn't explain the context, prerequisites, or exclusions for using this tool, leaving the agent with no usage direction beyond the basic function stated.

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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