toni-mcp-server

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generate-image

Create images from English text prompts using HuggingFace FLUX.1-schnell; adjust inference steps to trade quality for speed.

Instructions

텍스트 프롬프트를 입력하면 HuggingFace FLUX.1-schnell 모델로 이미지를 생성해 반환합니다.

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>"
    },
    "name": "generate-image"
  }
}

Input Schema

Name	Required	Description	Default
`prompt`	Yes	이미지 생성 프롬프트 (영어 권장)
`num_inference_steps`	No	추론 스텝 수 (기본값: 4, 범위: 1~10). 높을수록 품질이 좋아지지만 느려집니다.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Sep 13, 2026

TDQS

B3.1/5.0

Behavior2/5

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

No annotations are provided, so the description carries the full behavioral burden. It valuably discloses the backing model (FLUX.1-schnell), but says nothing about return format (URL, base64, file path), generation latency, cost, determinism/seed behavior, or failure modes for rejected prompts.

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?

A single tight sentence with no filler, front-loading the input (text prompt) and the output (generated image). Every clause earns its place.

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?

With no output schema and no annotations, the description should at minimum say how the image comes back, and it doesn't. The model name and input/output framing make it minimally viable for a two-parameter tool, but the return contract remains unspecified.

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 description coverage is 100%, with both prompt and num_inference_steps documented in the schema including defaults and ranges, so the baseline is 3. The description only alludes to the prompt ('텍스트 프롬프트를 입력하면') and adds no meaning beyond the schema—it doesn't even mention the step-count parameter.

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 states a specific verb (생성해 반환합니다) and resource (이미지) plus the underlying model (HuggingFace FLUX.1-schnell), so an agent immediately knows what it does. It does not explicitly contrast with any sibling, but the sibling set (greet, calc, now_time, geocode, get-weather, search_articles) contains no other image tool, so ambiguity is low.

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 only restates the input→output contract ('텍스트 프롬프트를 입력하면 ... 생성해 반환합니다') and gives no when-to-use guidance, no prerequisites, and no alternatives or exclusions. For a single-purpose generator this is tolerable but still leaves usage entirely implicit.

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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