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원활한 Unsplash 이미지 통합 및 검색 기능을 위한 간단한 MCP 서버입니다.
Unsplash MCP 서버는 풍부하고 고품질인 이미지를 검색하는 데 사용됩니다. Unsplash 기능을 자신의 애플리케이션에 통합하려는 개발자에게 이상적입니다.
Related MCP server: Unsplash MCP Server
고급 이미지 검색: 다음 필터를 사용하여 Unsplash의 방대한 사진 라이브러리를 검색합니다:
키워드 관련성
색상 구성
방향 옵션
사용자 지정 정렬 및 페이지 매기기
이 서버를 설치하기 전에 Unsplash API 액세스 키를 획득해야 합니다:
Unsplash에서 개발자 계정을 만듭니다.
새 애플리케이션을 등록합니다.
애플리케이션 세부 정보 페이지에서 액세스 키를 가져옵니다.
아래 구성 단계에서 이 키를 사용합니다.
자세한 내용은 공식 Unsplash API 문서를 참조하세요.
Smithery를 통해 Claude Desktop용 Unsplash 이미지 통합 서버를 자동으로 설치하려면:
Cursor IDE
npx -y @smithery/cli@latest install @hellokaton/unsplash-mcp-server --client cursor --key 7558c683-****-****Windsurf
npx -y @smithery/cli@latest install @hellokaton/unsplash-mcp-server --client windsurf --key 7558c683-****-****Cline
npx -y @smithery/cli@latest install @hellokaton/unsplash-mcp-server --client cline --key 7558c683-****-****# Clone the repository
git clone https://github.com/hellokaton/unsplash-mcp-server.git
# Navigate to project directory
cd unsplash-mcp-server
# Create virtual environment
uv venv
# Install dependencies
uv pip install .Cursor 에디터 통합
Cursor 에디터의 settings.json에 다음 구성을 추가하세요:
⚠️ 참고: 실제 설치 환경에 따라 다음 구성을 조정하세요:
uv가 시스템 PATH에 없는 경우 절대 경로(예: /path/to/uv)를 사용하세요.
./server.py는 서버 스크립트의 실제 위치로 수정해야 합니다(절대 경로 또는 작업 공간 기준 상대 경로 사용 가능).
{
"mcpServers": {
"unsplash": {
"command": "uv",
"args": ["run", "--with", "fastmcp", "fastmcp", "run", "./server.py"],
"env": {
"UNSPLASH_ACCESS_KEY": "${YOUR_ACCESS_KEY}"
}
}
}
}{
"tool": "search_photos",
"query": "mountain",
"per_page": 5,
"orientation": "landscape"
}Golang: unsplash-mcp-server
Java: unsplash-mcp-server
Search for Unsplash photos
Args:
query: Search keyword
page: Page number (1-based)
per_page: Results per page (1-30)
order_by: Sort method (relevant or latest)
color: Color filter (black_and_white, black, white, yellow, orange, red, purple, magenta, green, teal, blue)
orientation: Orientation filter (landscape, portrait, squarish)
Returns:
List[UnsplashPhoto]: List of search results containing photo objects with the following properties:
- id: Unique identifier for the photo
- description: Optional text description of the photo
- urls: Dictionary of available image URLs in different sizes
- width: Original image width in pixels
- height: Original image height in pixels
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| page | No | ||
| per_page | No | ||
| order_by | No | relevant | |
| color | No | ||
| orientation | No |
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 mentions the tool searches photos but doesn't cover critical aspects like rate limits, authentication requirements, pagination behavior beyond parameters, or error conditions. The return format is described, but operational context is missing.
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 well-structured and efficiently organized with clear sections for Args and Returns. Every sentence adds essential information without redundancy, making it easy to parse while maintaining completeness for the tool's functionality.
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 moderate complexity (6 parameters, 1 required) and lack of annotations/output schema, the description does well by detailing parameters and return format. However, it misses some behavioral context like rate limits or authentication needs, preventing a perfect score.
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 significant value beyond the input schema, which has 0% schema description coverage. It clearly explains each parameter's purpose, constraints (e.g., '1-30' for per_page), and valid values for enums like order_by, color, and orientation. This fully compensates for the schema's lack of descriptions.
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: 'Search for Unsplash photos'. It specifies the verb ('Search') and resource ('Unsplash photos'), making the function immediately understandable. However, without sibling tools to differentiate from, it cannot achieve the highest score of 5 which requires distinguishing from alternatives.
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, prerequisites, or constraints. It simply lists parameters and returns without context about appropriate use cases, making it minimally helpful for decision-making.
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.
search_photosScored across 1 tool
With only one tool, there is no possibility of confusion or overlap between tools. The single tool 'search_photos' has a clearly defined and distinct purpose.
The single tool follows a clear verb_noun pattern ('search_photos'), and with only one tool, there is perfect consistency by definition.
A single tool for an Unsplash server feels thin and incomplete for the domain. While search is a core function, typical photo services would include additional operations like getting photo details, downloading, or user-related actions.
The tool surface is severely incomplete for an Unsplash server. There are significant gaps: no ability to get photo details by ID, download photos, access user profiles, collections, or statistics. The server only supports search, which limits agent workflows.
Official ImageKit media library server: search, upload, organize, tag, share and manage assets.
Official ImageKit developer server: search the docs and build transformation URLs. No sign-in.
Search over 1.85 million captioned images using text, a reference image, or both. Upload a JPEG, PNG, or WebP image as base64, or provide a public HTTPS image URL. Find similar images from a Lightdrift asset ID and retrieve hosted file URLs, source licenses, and attribution. Connect with OAuth or an API key. Searches cost $0.005 ($5 per 1,000); image details are free. Setup: https://docs.lightdrift.ai/guides/images-mcp
The OpenRouter MCP server plugs OpenRouter into the AI tools you already use. Once connected, your assistant can pull live OpenRouter data (models, prices, your credits, rankings, and docs) and send quick test messages, all without leaving your editor.
