sd-api-mcp

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batch_merge_models

Merge a base model with multiple target models in one batch operation, choosing merge methods and alpha strength, and wait for completion.

Instructions

Merge a base model with multiple target models in batch. Waits for completion.

Args:
    model_type: Model architecture — "sd15" or "sdxl"
    base_model: Base model checkpoint filename
    target_models: List of target model checkpoint filenames
    method: Merge method — "linear", "slerp", "additive", or "subtract"
    alpha: Merge strength (0.0-1.0)
    output_subdir: Output subdirectory name for merged models
    preserve_metadata: Whether to preserve model metadata

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>",
      "base_model": "<base_model>",
      "target_models": "<target_models>"
    },
    "name": "batch_merge_models"
  }
}

Input Schema

Name	Required	Default
`alpha`	No
`method`	No	linear
`base_model`	Yes
`model_type`	Yes
`output_subdir`	No	batch_merged
`target_models`	Yes
`preserve_metadata`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 23, 2026

TDQS

A3.8/5.0

Behavior2/5

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

With no annotations provided, the description must carry full behavioral transparency. It discloses that the tool 'Waits for completion,' which is useful, but omits any mention of side effects, permissions, idempotency, or potential long-running behavior. Similar to the calibration example, a batch operation of this nature should clarify reversibility or failure handling; the description does not.

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 front-loaded with a one-sentence summary followed by a structured parameter list. Every line provides necessary information, though the docstring format is somewhat mechanical; it is appropriately sized for a 7-parameter tool and avoids fluff.

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

Completeness4/5

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

The presence of an output schema means return values are documented elsewhere, so the description doesn't need to cover them. For the complexity (7 params, no enums, no nesting), the description covers the operation's purpose and all parameter semantics adequately. Minor gaps like validation rules or error behavior exist but aren't critical for invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Since schema description coverage is 0%, the description is the only source of parameter meaning. It explicitly explains each parameter (e.g., 'model_type: Model architecture — "sd15" or "sdxl"') with types, enums, and defaults, going far beyond the schema's bare types and titles.

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 states the verb ('Merge'), the resource ('base model with multiple target models'), and the batch scope ('in batch'). It distinguishes itself from the sibling 'merge_models' by implying the batch variant, and the summary sentence is specific and unambiguous.

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

Usage Guidelines3/5

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

The description implies when to use the tool (for batch merging) via the phrase 'in batch', but it never explicitly contrasts with alternatives like 'merge_models' or states when not to use it. The behavior 'Waits for completion' hints at synchronous execution but does not provide explicit exclusions or prerequisites.

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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