sd-api-mcp

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list_models

Retrieve a list of available models by specifying model architecture (sd15 or sdxl) and resource type (checkpoints, loras, or vaes) to find matching entries.

Instructions

List available models.

Args:
    model_type: Model architecture — "sd15" or "sdxl"
    resource_type: Resource category — "checkpoints", "loras", or "vaes"

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": {
      "model_type": "<model_type>",
      "resource_type": "<resource_type>"
    },
    "name": "list_models"
  }
}

Input Schema

Name	Required	Description	Default
`model_type`	Yes
`resource_type`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 23, 2026

TDQS

B3.3/5.0

Behavior3/5

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

No annotations are present, so the description carries the full disclosure burden. It states the tool lists models and gives filter scope, but it doesn't expose aspects such as ordering, pagination, rate limits, or confirm the read-only nature beyond the word 'List'. This adds some behavioral context but remains minimal.

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

Conciseness4/5

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

The description is short and well-structured, with a leading sentence and two concise parameter lines. Every element contributes. A tiny bit more context on usage could be added without bloating the text, so it's not perfect but highly efficient.

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?

The output schema covers return values, avoiding the need to describe them. All parameters are documented, and the tool is low complexity (2 params). However, given the rich set of sibling tools like get_model_metadata and compare_models, the description lacks contextual guidance on when this checking function is 'the' right choice.

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 schema has no parameter descriptions (0% coverage), but the description compensates well by explaining both parameters with explicit value prompts (e.g., 'sd15' or 'sdxl', 'checkpoints', 'loras', or 'vaes'). This gives essential meaning, though it falls short of describing exact combination rules or default behavior.

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 uses a specific verb 'List' and a clear resource 'models', and the parameter explanations make the scope explicit (list by architecture and resource category). It doesn't mention siblings explicitly, but its function is clearly differentiated from tools like get_model_metadata or compare_models.

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 guidance on when to use this tool versus alternatives, no preconditions, and no context about its role in workflows (e.g., fetching IDs before generation). The parameter descriptions imply a lookup, but an agent gets no explicit when/when-not cues.

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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