mcp-image-generator
MCP 이미지 생성기
Together AI의 이미지 생성 모델을 사용하여 이미지를 생성하는 모델 컨텍스트 프로토콜(MCP) 서버입니다. 이 MCP 서버는 로컬에서 실행하거나 SSE 엔드포인트를 사용하여 실행할 수 있습니다. MCP 이미지 생성기에는 공급자가 필요하며, 현재 "Replicate"와 "Together"만 지원됩니다. TOGETHER_API_KEY 또는 REPLICATE_API_TOKEN 환경 변수를 설정하고, PROVIDER 환경 변수를 "replicate" 또는 "together"로 설정해야 합니다.
SSE 엔드포인트(Docker 환경)
저장소를 복제합니다
지엑스피1
Docker 컨테이너 빌드 및 실행
docker build -f Dockerfile.server -t mcp-imagegen .
docker run -p 3000:3000 mcp-imagegenMCP 클라이언트로 구성
{
"mcpServers": {
"imagegenerator": {
"url": "http://localhost:3000/sse",
"env": {
"PROVIDER": "replicate",
"REPLICATE_API_TOKEN": "your-replicate-api-token"
}
}
}
}사용하려는 MCP 서버의 종단점으로 url 조정합니다. provider "복제" 또는 "함께"가 될 수 있습니다.
Related MCP server: pixel-surgeon-mcp
stdio를 사용하여 로컬로 실행
필수 조건
노드.js
Together AI API 키 또는 Replicate API 토큰
설치
저장소를 복제합니다.
git clone https://github.com/gmkr/mcp-imagegen.git cd mcp-imagegen종속성 설치:
pnpm install
구성
MCP 클라이언트용 구성 파일을 만드세요. 구성 예시는 다음과 같습니다.
{
"mcpServers": {
"imagegenerator": {
"command": "pnpx",
"args": [
"-y",
"tsx",
"/path/to/mcp-imagegen/src/index.ts"
],
"env": {
"PROVIDER": "replicate",
"REPLICATE_API_TOKEN": "your-replicate-api-token"
}
}
}
}/path/to/mcp-imagegen 복제된 저장소의 절대 경로로 바꾸고 your-replicate-api-token 실제 Replicate API 토큰으로 바꾸세요.
용법
MCP 이미지 생성기는 텍스트 프롬프트를 기반으로 이미지를 생성하는 데 사용할 수 있는 generate_image 라는 도구를 제공합니다.
도구: generate_image
제공된 프롬프트에 따라 이미지를 생성합니다.
매개변수:
prompt(문자열): 이미지를 생성하기 위한 텍스트 프롬프트width(숫자, 선택 사항): 생성할 이미지의 너비(기본값: 512)height(숫자, 선택 사항): 생성할 이미지의 높이(기본값: 512)numberOfImages(숫자, 선택 사항): 생성할 이미지 수(기본값: 1)
환경 변수
PROVIDER: 이미지 생성에 사용할 공급자(기본값: "replicate")REPLICATE_API_TOKEN: 복제 API 토큰TOGETHER_API_KEY: Together AI API 키MODEL_NAME: 이미지 생성에 사용할 모델(기본값: "black-forest-labs/flux-schnell")
특허
MIT
Available Tools
1 toolgenerate_imageA
Generates and returns an image based on the provided promptUse this tool when you need to generate an image based on a promptThe image will be returned as a base64 encoded string
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | The prompt to generate an image for | |
| width | No | The width of the image to generate | |
| height | No | The height of the image to generate | |
| numberOfImages | No | The number of images to generate |
TDQS
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 discloses that the image is 'returned as a base64 encoded string,' which adds useful behavioral context beyond the input schema. However, it lacks details on potential limitations (e.g., rate limits, quality constraints, or error conditions), leaving gaps for a mutation tool.
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 front-loaded with the purpose and usage guidelines in two sentences, with no wasted words. However, the lack of punctuation between sentences ('promptUse this tool') slightly reduces readability, preventing a perfect score of 5.
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 tool's complexity (image generation with 4 parameters) and no annotations or output schema, the description is moderately complete. It covers the basic operation and output format but lacks details on behavioral traits (e.g., performance, errors) and does not explain return values beyond the base64 string, leaving room for improvement.
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?
The schema description coverage is 100%, meaning all parameters are documented in the schema itself. The description does not add any parameter-specific details beyond what the schema provides (e.g., format or constraints for 'prompt' or 'width'). Thus, it meets the baseline of 3 but does not enhance parameter understanding.
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: 'Generates and returns an image based on the provided prompt.' It specifies both the action (generate and return) and the resource (image). However, with no sibling tools provided, it cannot demonstrate differentiation from alternatives, which prevents a score of 5.
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?
The description includes explicit guidance: 'Use this tool when you need to generate an image based on a prompt.' This clearly indicates the primary use case. However, it lacks exclusions or alternatives (e.g., when not to use it or other tools for similar tasks), which prevents a score of 5.
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. Dates show when Glama detected each change.
1 tool update
- First observed
generate_image
TDQS
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 this set.
The single tool name 'generate_image' follows a clear verb_noun pattern. Since there is only one tool, naming consistency is inherently perfect with no deviations or mixed conventions to evaluate.
A single tool is too few for a server named 'mcp-image-generator', which suggests a broader scope for image generation tasks. While the tool covers basic generation, the count feels thin and lacks operations like editing, upscaling, or managing generated images that might be expected.
The tool set is severely incomplete for an image generation domain. It only provides generation, with no coverage for common operations like editing images, adjusting parameters, retrieving generation history, or handling different formats, which will limit agent capabilities and cause workarounds.
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