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mcp-flux-schnell

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mcp-flux-schnell MCP 서버

Flux Schnell 모델을 사용하여 텍스트-이미지 생성 도구를 구현하는 TypeScript 기반 MCP 서버입니다. 이 서버는 Cloudflare의 Flux Schnell 워커 API와 통합되어 MCP를 통한 이미지 생성 기능을 제공합니다.

특징

도구

  • generate_image - 텍스트 설명에서 이미지 생성

    • 텍스트 프롬프트를 입력으로 받습니다(1~2048자)

    • 생성된 이미지 파일의 경로를 반환합니다.

Related MCP server: Image Generator MCP Server

환경 변수

다음 환경 변수를 구성해야 합니다.

  • FLUX_API_URL - Flux Schnell API 엔드포인트의 URL

  • FLUX_API_TOKEN - Flux Schnell API에 대한 인증 토큰

  • WORKING_DIR (선택 사항) - 생성된 이미지가 저장될 디렉토리(기본값은 현재 작업 디렉토리)

개발

종속성 설치:

지엑스피1

서버를 빌드하세요:

npm run build
# or
pnpm build

설치

커서 구성

Cursor에서 MCP 서버를 구성하는 방법은 두 가지가 있습니다.

프로젝트 구성

특정 프로젝트에만 사용할 수 있는 도구의 경우 프로젝트 디렉토리에 .cursor/mcp.json 파일을 만듭니다.

{
  "mcpServers": {
    "mcp-flux-schnell": {
      "command": "node",
      "args": ["/path/to/mcp-flux-schnell/build/index.js"],
      "env": {
        "FLUX_API_URL": "your flux api url",
        "FLUX_API_TOKEN": "your flux api token",
        "WORKING_DIR": "your working directory"
      }
    }
  }
}

이 구성은 특정 프로젝트 내에서만 사용할 수 있습니다.

글로벌 구성

모든 프로젝트에서 사용하려는 도구의 경우 홈 디렉토리에 동일한 구성으로 ~/.cursor/mcp.json 파일을 만듭니다.

{
  "mcpServers": {
    "mcp-flux-schnell": {
      "command": "node",
      "args": ["/path/to/mcp-flux-schnell/build/index.js"],
      "env": {
        "FLUX_API_URL": "your flux api url",
        "FLUX_API_TOKEN": "your flux api token",
        "WORKING_DIR": "your working directory"
      }
    }
  }
}

이렇게 하면 모든 커서 작업 공간에서 MCP 서버를 사용할 수 있습니다.

Available Tools

1 tool
generate_imageC

Generate an image from a text prompt using Flux Schnell model

ParametersJSON Schema
NameRequiredDescriptionDefault
promptYesA text description of the image you want to generate.

TDQS

C2.9/5.0
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 of behavioral disclosure. It mentions the model ('Flux Schnell') but fails to describe key traits like whether this is a read-only or mutative operation, potential rate limits, authentication needs, output format, or error handling. This leaves significant gaps for an AI agent to understand how to invoke it safely and effectively.

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 a single, efficient sentence that directly states the tool's function without any redundant or extraneous information. It is front-loaded and appropriately sized for a simple tool, making it easy for an AI agent to parse quickly.

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?

Given the lack of annotations and output schema, the description is incomplete for a tool that performs image generation. It does not cover behavioral aspects like mutation risks, rate limits, or output details (e.g., image format, size), which are crucial for an AI agent to use the tool correctly in various contexts.

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?

The input schema has 100% description coverage, with the 'prompt' parameter well-documented in the schema itself. The description adds minimal value beyond the schema by implying the prompt is for image generation, but it does not provide additional context like prompt formatting tips or model-specific constraints. This meets the baseline for high schema coverage.

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 clearly states the tool's purpose: 'Generate an image from a text prompt using Flux Schnell model.' It specifies the verb ('generate'), resource ('image'), and method ('using Flux Schnell model'), which is specific and unambiguous. However, since there are no sibling tools, it cannot demonstrate differentiation from alternatives, preventing a perfect score.

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 provides no guidance on when to use this tool versus alternatives, prerequisites, or constraints. It merely states what the tool does without indicating appropriate contexts or exclusions, such as when other image generation models might be preferred or if there are usage limits.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 1 tool updatev1.0.0
    • First observedgenerate_image
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

B3.1/5.0

Scored across 1 tool

Disambiguation5/5

With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool 'generate_image' has a clear and distinct purpose that cannot be confused with any other tool in the set.

Naming Consistency5/5

The single tool name 'generate_image' follows a clear verb_noun pattern. With only one tool, consistency is inherently perfect as there are no other names to compare against or deviate from.

Tool Count2/5

A single tool is too few for most practical server purposes, as it severely limits functionality and flexibility. For an image generation server, typical expectations might include variations like upscaling, editing, or batch processing, making one tool feel thin and under-scoped.

Completeness2/5

The server's domain appears to be image generation, but with only a basic generation tool, there are significant gaps. Missing operations might include image editing, style variations, resolution adjustments, or batch processing, which could lead to agent failures when more complex tasks are required.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

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