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my-mcp-server

my-mcp-server

by Gwyn-Ahchoo
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

generate-image

Generate images from text prompts using the FLUX.1-schnell model.

Instructions

HuggingFace Inference API(FLUX.1-schnell)로 텍스트 프롬프트로부터 이미지를 생성합니다.

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

NameRequiredDescriptionDefault
promptYes이미지 생성 프롬프트
num_inference_stepsNo추론 스텝 수 (1~10, 기본값: 4)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

B3.3/5.0
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 burden. It mentions the specific model and API, but omits important behavioral traits such as rate limits, costs, output format, failure modes, or any constraints beyond the parameter schema.

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 a single efficient sentence with no redundancy. It could be improved with more structure, but it remains concise.

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

Completeness2/5

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

No output schema exists, and the description does not mention the return value (e.g., image format, URL). Given the tool generates images, this is a significant gap. The description also lacks context about the API's behavior.

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%, so the description does not need to add meaning. However, it adds no extra context beyond what is already in the schema, so it meets the baseline.

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 action (generate image), the resource (text prompt), and the method (HuggingFace Inference API with FLUX.1-schnell). It is distinct from sibling tools which are unrelated tasks.

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 usage for generating images from prompts, but provides no explicit guidance on when to use or not use this tool, nor any alternatives. Given sibling tools are distinct, confusion is minimal, but guidance is lacking.

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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