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nukIeer

Higgsfield Unlimited MCP

by nukIeer

generate_raw

Execute any model with custom parameters for face-swap, upscale, or inpainting. Pass a params dict directly, and upload input files as needed.

Instructions

Escape hatch: call any model with a custom params dict, across the account pool.

Use for face-swap, character-swap, upscale, inpaint, or any model whose exact schema you already know (see docs/MODEL_SCHEMAS.md). params is passed through verbatim (with use_unlim added). api_version selects the endpoint: v1 (/jobs/{model}) or v2 (/jobs/v2/{model}, newer models incl. most video). Local input_files are uploaded and merged into params['input_images'].

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kindNoraw
waitNo
modelYes
paramsYes
timeoutNo
downloadNo
api_versionNov1
input_filesNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations, the description carries the burden. It explains key behaviors: params passed verbatim with use_unlim added, api_version endpoint selection, and input_files merging, providing sufficient transparency for the agent.

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 concise (~90 words), front-loaded with purpose, and structured to provide quick understanding without superfluous content.

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?

Given 8 parameters, nested objects, and an output schema, the description covers core functionality and key parameters, though it omits details on wait, timeout, and download. The output schema existence reduces need to describe returns.

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 coverage is 0%, so the description must add meaning. It explains params, api_version, and input_files, but does not cover kind, wait, timeout, or download, leaving gaps despite baseline of 4.

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 identifies the tool as an 'escape hatch' for calling any model with a custom params dict, listing specific use cases (face-swap, character-swap, etc.) and distinguishing it from siblings by targeting advanced/raw access.

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

Usage Guidelines4/5

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

It explicitly states when to use the tool (for models with known schemas, specific tasks) and references external documentation, but does not explicitly exclude cases where sibling tools like generate_image would be more appropriate.

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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