Together AI Image Server
Together AI 이미지 서버
영어 | 간체 중국어
Together AI API를 사용하여 이미지를 생성하기 위한 TypeScript 기반 MCP(Model Context Protocol) 서버입니다.
개요
이 서버는 MCP 프로토콜을 통해 Together AI의 이미지 생성 모델을 사용하여 이미지를 생성하는 간단한 인터페이스를 제공합니다. Claude를 비롯한 MCP 호환 어시스턴트들이 텍스트 프롬프트를 기반으로 이미지를 생성할 수 있도록 지원합니다.
Related MCP server: gemini-nano-banana-mcp
특징
도구
generate_image- 텍스트 프롬프트에서 이미지 생성필수 매개변수로 텍스트 프롬프트를 사용합니다.
생성 단계 및 이미지 수를 제어하기 위한 선택적 매개변수
생성된 이미지에 대한 URL 및 로컬 경로를 반환합니다.
필수 조건
Node.js(v14 이상 권장)
Together AI API 키
설치
지엑스피1
구성
Together AI API 키를 환경 변수로 설정하세요.
# On Linux/macOS
export TOGETHER_API_KEY="your-api-key-here"
# On Windows (Command Prompt)
set TOGETHER_API_KEY=your-api-key-here
# On Windows (PowerShell)
$env:TOGETHER_API_KEY="your-api-key-here"또는 프로젝트 루트에 .env 파일을 만들 수 있습니다.
TOGETHER_API_KEY=your-api-key-here개발
서버를 빌드하세요:
npm run build자동 재빌드를 사용한 개발의 경우:
npm run watchClaude Desktop과 함께 사용
Claude Desktop과 함께 사용하려면 서버 구성을 추가하세요.
macOS의 경우: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows의 경우: %APPDATA%/Claude/claude_desktop_config.json
{
"mcpServers": {
"Together AI Image Server": {
"command": "/path/to/together-ai-image-server/build/index.js"
}
}
}/path/to/together-ai-image-server 실제 설치 경로로 바꾸세요.
디버깅
MCP 서버는 stdio를 통해 통신하므로 디버깅이 어려울 수 있습니다. 패키지 스크립트로 제공되는 MCP Inspector를 사용하는 것이 좋습니다.
npm run inspector검사기는 브라우저에서 디버깅 도구에 액세스할 수 있는 URL을 제공합니다.
API 참조
생성_이미지
Together AI의 이미지 생성 API를 사용하여 텍스트 프롬프트를 기반으로 이미지를 생성합니다.
매개변수:
prompt(문자열, 필수): 이미지 생성을 위한 텍스트 프롬프트steps(숫자, 선택 사항, 기본값: 4): 확산 단계 수(1-4)n(숫자, 선택, 기본값: 1): 생성할 이미지 수(1-4)
보고:
JSON 객체에는 다음이 포함됩니다.
image_urls: 생성된 이미지에 대한 URL 배열local_paths: 로컬 캐시된 이미지에 대한 경로 배열
특허
MIT
기여하다
기여를 환영합니다! 풀 리퀘스트를 제출해 주세요.
Available Tools
1 toolgenerate_imageC
Generate image from text prompt using Together AI API
| Name | Required | Description | Default |
|---|---|---|---|
| n | No | Number of images to generate (default: 1, max: 4) | |
| steps | No | Number of diffusion steps (default: 4) | |
| prompt | Yes | Text prompt for image generation |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It only mentions the external API but does not disclose any behavioral traits such as rate limits, authentication needs, what happens under the hood, or potential side effects like image generation limits or API costs.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that is concise and front-loaded with the core action. However, it is too short to cover necessary details, but for what it states, it is efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the absence of an output schema and annotations, the description is incomplete. It provides no information about what the tool returns (e.g., image URLs or base64), any limitations, or error conditions. The user would need to guess or rely on external knowledge.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
All three parameters have descriptions in the input schema (100% coverage). The description adds no extra meaning beyond the schema, which already explains 'prompt', 'n', and 'steps'. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: generating an image from a text prompt using the Together AI API. The verb 'generate' and resource 'image' are specific, and mentioning the API adds context. No siblings exist, so differentiation is not needed.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use or avoid this tool. There is no mention of prerequisites, alternatives, or when not to use it. The description simply states what it does without contextual usage advice.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
TDQS
With only one tool, there is no possibility of confusion between tools. The tool's purpose is clearly defined.
The single tool 'generate_image' follows a clear verb_noun pattern, which is consistent by default.
The server has only one tool, which is too few for a typical image generation service. Users would likely expect additional tools for model selection, image variants, or status retrieval.
The tool surface is severely incomplete; a comprehensive image generation server would typically include tools for listing models, configuring generation parameters, and possibly managing generated images.
Maintenance
Resources
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