Cline 용 고급 모델 컨텍스트 프로토콜(MCP) 서버로, EverArt의 AI 모델과 통합되어 벡터 및 래스터 이미지를 생성합니다. 이 서버는 유연한 저장 옵션과 형식 변환 기능을 갖춘 강력한 이미지 생성 기능을 제공합니다.
벡터 그래픽 생성
Recraft-Vector 모델을 사용하여 SVG 벡터 그래픽을 만듭니다.
자동 SVG 최적화
로고, 아이콘 및 확장 가능한 그래픽에 적합합니다.
래스터 이미지 생성
PNG, JPEG 및 WebP 형식 지원
다양한 스타일을 위한 여러 AI 모델
고품질 이미지 처리
유연한 스토리지
사용자 정의 출력 경로 및 파일 이름
자동 디렉토리 생성
형식 검증 및 확장 처리
웹 프로젝트 통합
Related MCP server: Gemini Image MCP
5000:FLUX1.1 : 표준 품질, 범용 이미지 생성
9000:FLUX1.1-ultra : 디테일한 이미지를 위한 초고화질
6000:SD3.5 : 다양한 스타일을 위한 안정적인 디퓨전 3.5
7000:Recraft-Real : 포토리얼리스틱 스타일
8000:Recraft-Vector : 벡터 아트 스타일(SVG 출력)
저장소를 복제합니다.
지엑스피1
종속성 설치:
npm install프로젝트를 빌드하세요:
npm run buildEverArt API 키를 받으세요:
EverArt 에 가입하세요
계정 설정으로 이동하세요
API 키를 생성하거나 복사하세요
Cline MCP 설정 파일에 서버를 추가합니다.
VS Code 확장 프로그램의 경우 :~/Library/Application Support/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json 편집합니다.
{
"mcpServers": {
"everart-forge": {
"command": "node",
"args": ["/absolute/path/to/everart-forge-mcp/build/index.js"],
"env": {
"EVERART_API_KEY": "your_api_key_here"
},
"disabled": false,
"autoApprove": []
}
}
}Claude 데스크톱 앱의 경우 :~/Library/Application Support/Claude/claude_desktop_config.json (macOS) 또는 OS에 맞는 적절한 위치를 편집하세요.
새로운 MCP 서버를 로드하려면 Cline을 다시 시작하세요.
구성이 완료되면 Cline을 사용하여 다음과 같은 프롬프트로 이미지를 생성할 수 있습니다.
"Recraft-Vector 모델을 사용하여 SVG 형식의 미니멀리스트 기술 로고를 생성합니다."
"FLUX1.1-ultra 모델을 사용하여 사실적인 풍경 이미지를 만들어 보세요"
"인공지능을 표현하는 내 프로젝트의 벡터 아이콘을 만들어주세요"
"전문적인 회사 로고를 SVG 파일로 생성하여 내 데스크톱에 저장합니다."
서버는 다음과 같은 도구를 제공합니다.
다양한 사용자 정의 옵션으로 이미지를 생성합니다.
Parameters:
- prompt (required): Text description of desired image
- model: Model ID (5000:FLUX1.1, 9000:FLUX1.1-ultra, 6000:SD3.5, 7000:Recraft-Real, 8000:Recraft-Vector)
- format: Output format (svg, png, jpg, webp)
- output_path: Custom output path for the image
- web_project_path: Path to web project root for proper asset organization
- project_type: Web project type (react, vue, html, next, etc.)
- asset_path: Subdirectory within the web project assets
- image_count: Number of images to generate (1-10)참고사항:
SVG 형식은 Recraft-Vector(8000) 모델에서만 사용 가능합니다.
모델 8000의 경우 기본 형식은 "svg"이고 다른 모델에서는 "png"입니다.
결합된 모델 ID를 지정할 수 있습니다(예: "8000:Recraft-Vector")
서버에 저장된 이전에 생성된 모든 이미지를 나열합니다.
기본 이미지 뷰어에서 특정 이미지를 엽니다.
Parameters:
- filename: Name of the image file to view오류: 잘못된 모델 ID : 지원되는 모델 ID(5000, 6000, 7000, 8000, 9000) 중 하나를 사용하고 있는지 확인하세요.
모델과 호환되지 않는 형식 : SVG 형식은 Recraft-Vector(8000) 모델에서만 사용 가능합니다.
이미지를 찾을 수 없습니다 . list_images 도구를 사용하여 사용 가능한 이미지를 확인하세요.
API 인증에 실패했습니다 . EverArt API 키를 확인하세요.
이미지가 나타나지 않음 : 파일 권한 및 경로를 확인하세요
MIT 라이센스 - 자세한 내용은 라이센스 파일을 참조하세요.
Generate images using EverArt Models, optimized for web development. Supports web project paths, responsive formats, and inline preview. Available models:
5000:FLUX1.1: Standard quality
9000:FLUX1.1-ultra: Ultra high quality
6000:SD3.5: Stable Diffusion 3.5
7000:Recraft-Real: Photorealistic style
8000:Recraft-Vector: Vector art style (SVG format)
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | Text description of desired image | |
| model | No | Model ID (5000:FLUX1.1, 9000:FLUX1.1-ultra, 6000:SD3.5, 7000:Recraft-Real, 8000:Recraft-Vector) | 5000 |
| format | No | Output format (svg, png, jpg, webp). Note: Vector format (svg) is only available with Recraft-Vector (8000) model. | svg |
| output_path | No | Optional: Custom output path for the generated image. If not provided, image will be saved in the default storage directory. | |
| web_project_path | No | Path to web project root folder for storing images in appropriate asset directories. | |
| project_type | No | Web project type to determine appropriate asset directory structure (e.g., 'react', 'vue', 'html', 'next'). | |
| asset_path | No | Optional subdirectory within the web project's asset structure for storing generated images. | |
| image_count | No | Number of images to generate |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It lists available models and hints at web project integration but fails to describe critical behaviors like authentication needs, rate limits, error handling, or what happens when images are generated (e.g., saved to disk, returned as data). For a complex 8-parameter tool with no annotation coverage, this is a significant gap.
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 efficiently structured with a clear opening sentence and a bulleted list of models. However, the model list could be more concise (e.g., by grouping similar models), and some sentences like 'Supports web project paths, responsive formats, and inline preview' are vague and could be tightened.
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 (8 parameters, no annotations, no output schema), the description is incomplete. It lacks details on return values (e.g., image URLs, file paths), error conditions, web development integration specifics, and behavioral constraints. For a generative tool with multiple parameters, this leaves significant gaps for an AI agent.
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?
Schema description coverage is 100%, so the schema already documents all 8 parameters thoroughly. The description adds some value by listing model options with quality/style notes, but it doesn't provide additional semantic context beyond what's in the schema (e.g., explaining 'web_project_path' integration or 'project_type' implications). Baseline 3 is appropriate when the schema does the heavy lifting.
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 generates images using EverArt Models and specifies it's optimized for web development, which provides a specific verb+resource. However, it doesn't explicitly distinguish this from sibling tools like 'list_images' or 'view_image' beyond the core generation function, 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.
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 like 'list_images' or 'view_image'. It mentions web development optimization but doesn't specify scenarios where this is preferred over other image-related tools or when not to use it, leaving usage context implied at best.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all stored images
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the action ('List all stored images') but doesn't describe what 'List' entails—such as whether it returns metadata, thumbnails, or full images; if there's pagination or filtering; or any performance or rate-limiting considerations. This leaves significant gaps in understanding the tool's behavior.
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 'List all stored images' is extremely concise—a single, clear sentence with no wasted words. It's front-loaded and efficiently communicates the core action without any fluff, making it easy for an agent to parse quickly.
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 simplicity (0 parameters, no output schema, no annotations), the description is minimal but insufficient. It doesn't explain what 'List' returns (e.g., a list of image IDs, metadata, or URLs), which is critical for a tool with no output schema. For a basic list operation, more context on the return format is needed to be complete.
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 input schema has 0 parameters with 100% coverage, so the schema fully documents the lack of inputs. The description doesn't add parameter details beyond this, but since there are no parameters to explain, it doesn't need to compensate. A baseline of 4 is appropriate as the description doesn't contradict or add unnecessary information.
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 'List all stored images' clearly states the verb ('List') and resource ('stored images'), making the tool's purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'view_image' (which might show a single image) or 'generate_image' (which creates new images), so it doesn't reach the highest score.
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 provides no guidance on when to use this tool versus alternatives like 'view_image' or 'generate_image'. It doesn't mention any prerequisites, exclusions, or contextual factors that would help an agent choose between these tools, leaving the usage unclear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Open a stored image in the default image viewer
| Name | Required | Description | Default |
|---|---|---|---|
| filename | Yes | Name of the image file to view |
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 states the tool opens an image in a viewer, implying a read-only operation, but lacks details on behavioral traits such as whether it requires specific permissions, if it launches an external application, or any error handling (e.g., if the file doesn't exist). This leaves gaps for safe agent invocation.
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, efficient sentence that directly states the tool's purpose without unnecessary words. It is appropriately sized and front-loaded, making it easy for an agent to parse quickly.
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 simplicity (one parameter, no output schema), the description is minimal but incomplete. It lacks context on usage guidelines, behavioral transparency (e.g., what happens if the file is missing), and doesn't leverage the absence of annotations to compensate, making it inadequate for safe agent operation.
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 description adds no parameter semantics beyond the input schema, which has 100% coverage and fully documents the single 'filename' parameter. This meets the baseline of 3, as the schema handles the heavy lifting, but the description doesn't provide additional context like file format support or path specifications.
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 action ('Open') and resource ('stored image'), specifying it opens in the default image viewer. However, it doesn't explicitly differentiate from sibling tools like 'generate_image' (creates new images) or 'list_images' (lists existing images), which is a minor gap.
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 provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., the image must already exist), exclusions, or comparisons to sibling tools like 'generate_image' for creating images or 'list_images' for browsing available images.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.0.0generate_imagelist_imagesview_imageScored across 3 tools
The three tools have clearly distinct purposes: generate_image creates new images, list_images enumerates stored images, and view_image opens a specific stored image. There is no overlap or ambiguity between these functions, making it easy for an agent to select the correct tool for each task.
All tool names follow a consistent verb_noun pattern with snake_case: generate_image, list_images, and view_image. This predictable naming scheme enhances readability and usability, with no deviations or mixed conventions.
With only three tools, the server feels thin for an image generation and management domain. While the tools cover basic operations (create, list, view), more advanced functionality like editing, deleting, or organizing images is missing, which could limit agent workflows in practice.
The tool surface is significantly incomplete for an image management server. There are notable gaps: no ability to delete, update, or organize images (e.g., tagging, moving), and no tools for managing the generation process (e.g., canceling jobs, checking status). This will likely cause agent failures when trying to perform common image-related tasks.
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