Skip to main content
Glama
enesjakozh
sarthakkimtani

Image Generation MCP Server

by sarthakkimtani
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

이미지 생성 MCP 서버

Together AI를 통해 고품질 이미지를 원활하게 생성할 수 있도록 하는 모델 컨텍스트 프로토콜(MCP) 서버입니다. 이 서버는 이미지 생성 매개변수를 지정하기 위한 표준화된 인터페이스를 제공합니다.

특징

  • Flux.1 Schnell 모델을 기반으로 하는 고품질 이미지 생성

  • 사용자 정의 가능한 크기(폭 및 높이) 지원

  • 신속한 검증 및 API 문제에 대한 명확한 오류 처리

  • MCP 호환 클라이언트와의 쉬운 통합

Related MCP server: MCP Replicate FLUX

설치

클로드 데스크탑

  • MacOS의 경우: ~/Library/Application\ Support/Claude/claude_desktop_config.json

  • Windows의 경우: %APPDATA%/Claude/claude_desktop_config.json

지엑스피1

사용 가능한 도구

서버는 하나의 도구를 구현합니다.

생성_이미지

주어진 텍스트 프롬프트와 선택적 크기에 따라 이미지를 생성합니다.

입력 스키마:

{
  "prompt": {
    "type": "string",
    "description": "A descriptive prompt for generating the image (e.g., 'a futuristic cityscape at sunset')"
  },
  "width": {
    "type": "integer",
    "description": "Width of the generated image in pixels (optional)"
  },
  "height": {
    "type": "integer",
    "description": "Height of the generated image in pixels (optional)"
  },
  "model": {
    "type": "string",
    "description": "The exact model name as it appears in Together AI. If incorrect, it will fallback to the default model (black-forest-labs/FLUX.1-schnell)."
  }
}

필수 조건

  • Python 3.12 이상

  • httpx

  • 엠씨피

기여하다

참여를 환영합니다! 참여 방법은 다음과 같습니다.

  1. 저장소를 포크하세요

  2. 새로운 브랜치( feature/my-new-feature )를 생성합니다.

  3. 변경 사항을 커밋하세요

  4. 가지를 포크로 밀어 넣으세요

  5. 풀 리퀘스트 열기

중요한 변경 사항이 있는 경우, 제안된 변경 사항을 논의하기 위해 먼저 이슈를 열어주세요.

특허

이 프로젝트는 MIT 라이선스에 따라 라이선스가 부여됩니다. 자세한 내용은 라이선스 파일을 참조하세요.

Available Tools

1 tool
generate_imageC

Generate an image based on the text prompt, model, and optional dimensions

ParametersJSON Schema
NameRequiredDescriptionDefault
heightNoOptional height for the image
modelYesThe exact model name as it appears in Together AI. If incorrect, it will fallback to the default model (black-forest-labs/FLUX.1-schnell).
promptYesThe text prompt for image generation
widthNoOptional width for the image

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 action ('Generate an image') but lacks details on permissions, rate limits, output format, or error handling. For a generative tool with zero annotation coverage, this is insufficient.

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 front-loads the core purpose without unnecessary details. Every word contributes to understanding the tool's function, making it appropriately concise and well-structured.

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 complexity of image generation, lack of annotations, and no output schema, the description is incomplete. It doesn't address behavioral aspects like response format, errors, or constraints, leaving significant gaps for the agent to infer usage.

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 schema fully documents all parameters. The description adds minimal value by listing inputs ('text prompt, model, and optional dimensions') but doesn't provide additional context beyond what the schema already specifies, aligning with the baseline for high 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 based on the text prompt, model, and optional dimensions.' It specifies the verb ('Generate') and resource ('image'), and mentions key inputs. However, with no sibling tools, it doesn't need to differentiate from alternatives, so a 5 is not warranted.

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 simply lists inputs without context about appropriate scenarios or limitations, leaving the agent with minimal usage direction.

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 confusion or overlap between tools. The tool's purpose is clearly defined and distinct by default.

Naming Consistency5/5

A single tool inherently has perfect naming consistency, as there are no other tools to compare it against. The name 'generate_image' follows a clear verb_noun pattern.

Tool Count2/5

A single tool for an image generation server feels thin and limited in scope. While it covers the core functionality, it lacks related operations like listing models, retrieving generated images, or managing parameters, making it borderline too few for the domain.

Completeness2/5

The server is severely incomplete for image generation. It only offers generation without any supporting tools for model selection, image retrieval, history management, or parameter tuning, which are common in such domains, leading to potential dead ends for agents.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

Related MCP Connectors

Related MCP Servers

Appeared in Searches