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Ichigo3766

Image Generation MCP Server

by Ichigo3766

get_sd_models

Retrieve a list of available Stable Diffusion models for text-to-image generation and image upscaling via the Image Generation MCP Server. Simplify model selection for API-driven workflows.

Instructions

Get list of available Stable Diffusion models

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • Handler for the 'get_sd_models' tool. Fetches the list of available Stable Diffusion models from the SD WebUI API endpoint '/sdapi/v1/sd-models' and returns an array of model titles as JSON string.
    case 'get_sd_models': {
      const response = await this.axiosInstance.get('/sdapi/v1/sd-models');
      const models = response.data as ModelInfo[];
      return { content: [{ type: 'text', text: JSON.stringify(models.map(m => m.title)) }] };
    }
  • Schema and registration of the 'get_sd_models' tool in the ListTools response. Defines no input parameters.
    {
      name: 'get_sd_models',
      description: 'Get list of available Stable Diffusion models',
      inputSchema: {
        type: 'object',
        properties: {},
        required: []
      }
    },
  • Type interface for Stable Diffusion model information, used to type the API response in the handler.
    interface ModelInfo {
      title: string;
      model_name: string;
      hash: string;
      sha256: string;
      filename: string;
      config: string;
    }
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 states the tool retrieves a list but doesn't describe the return format (e.g., JSON array, model names/IDs), pagination, error conditions, or performance aspects (e.g., cached vs. live data). For a read operation with zero annotation coverage, this leaves significant gaps in understanding how the tool behaves.

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 conveys the tool's purpose with zero wasted words. It is appropriately sized for a simple list-retrieval tool and front-loaded with the essential information, making it easy to parse quickly.

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?

Given the tool's low complexity (0 parameters, no output schema, no annotations), the description is minimally adequate but incomplete. It lacks details on the return format, which is critical for a list-fetching tool, and doesn't integrate with sibling tools (e.g., how the output relates to 'set_sd_model'). For a tool with no structured metadata, the description should do more to compensate.

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 has 0 parameters, and schema description coverage is 100% (since there are no parameters to describe). The description doesn't need to compensate for any parameter gaps, so it meets the baseline expectation for a parameterless tool by clearly stating what it does without unnecessary parameter details.

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 verb ('Get') and resource ('list of available Stable Diffusion models'), making the purpose immediately understandable. It distinguishes from siblings like 'generate_image' (creation) and 'set_sd_model' (configuration), though it doesn't explicitly differentiate from 'get_sd_upscalers' (which fetches a different resource type).

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. It doesn't mention prerequisites (e.g., whether models need to be loaded first), relate to 'set_sd_model' for model selection, or specify scenarios where this list is needed (e.g., before generating images). The description is purely functional without contextual usage advice.

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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