Unsplash MCP Server
Seamless Unsplash image integration via the Model Context Protocol.
Key Features
Use Cases
README
Unsplash MCP Server
English | 简体中文
A simple MCP server for seamless Unsplash image integration and search capabilities.
📋 Overview
Unsplash MCP Server is used for searching rich, high-quality images. It's ideal for developers who want to integrate Unsplash functionality into their own applications.
✨ Features
- Advanced Image Search: Search Unsplash's extensive photo library with filters for:
- Keyword relevance
- Color schemes
- Orientation options
- Custom sorting and pagination
🔑 Obtaining Unsplash Access Key
Before installing this server, you'll need to obtain an Unsplash API Access Key:
- Create a developer account at Unsplash
- Register a new application
- Get your Access Key from the application details page
- Use this key in the configuration steps below
For more details, refer to the official Unsplash API documentation.
🚀 Installation
To install Unsplash Image Integration Server for Claude Desktop automatically via Smithery:
IDE Setup
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-****-****
Manual Installation
# 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 Editor Integration
Add the following configuration to your Cursor editor's settings.json:
⚠️ Note: Please adjust the following configuration according to your actual installation:
- If
uvis not in your system PATH, use an absolute path (e.g.,/path/to/uv) ./server.pyshould be modified to the actual location of your server script (can use absolute path or path relative to workspace)
{
"mcpServers": {
"unsplash": {
"command": "uv",
"args": ["run", "--with", "fastmcp", "fastmcp", "run", "./server.py"],
"env": {
"UNSPLASH_ACCESS_KEY": "${YOUR_ACCESS_KEY}"
}
}
}
}
Using in Cursor
🛠️ Available Tools
Search Photos
{
"tool": "search_photos",
"query": "mountain",
"per_page": 5,
"orientation": "landscape"
}
🔄 Other Implementations
- Golang: unsplash-mcp-server
- Java: unsplash-mcp-server
📄 License
📬 Contact
Star History
Repository Owner
User
Repository Details
Programming Languages
Tags
Topics
Join Our Newsletter
Stay updated with the latest AI tools, news, and offers by subscribing to our weekly newsletter.
Related MCPs
Discover similar Model Context Protocol servers
Stocky
Find beautiful royalty-free stock images across multiple providers in one search.
Stocky enables fast, asynchronous search and retrieval of royalty-free stock images from Pexels and Unsplash through a unified MCP-compatible interface. It supports rich metadata, provider flexibility, pagination, and robust error handling. Designed to operate as an MCP (Model Context Protocol) server, it integrates with MCP clients using standard client configuration and environment variables for API keys. Stocky provides Python async functions for searching, retrieving details, and downloading images with flexible output options.
- ⭐ 14
- MCP
- joelio/stocky
tavily-search MCP server
A search server that integrates Tavily API with Model Context Protocol tools.
tavily-search MCP server provides an MCP-compliant server to perform search queries using the Tavily API. It returns search results in text format, including AI responses, URLs, and result titles. The server is designed for easy integration with clients like Claude Desktop or Cursor and supports both local and Docker-based deployment. It facilitates AI workflows by offering search functionality as part of a standardized protocol interface.
- ⭐ 44
- MCP
- Tomatio13/mcp-server-tavily
Brave Search MCP Server
MCP integration for web, image, news, video, and local search via Brave Search API.
Implements a Model Context Protocol server that connects with the Brave Search API, enabling AI systems to perform comprehensive web, image, news, video, and local points of interest searches. Provides standardized MCP tools for various search types, each supporting advanced filtering parameters. Designed for easy integration in context-aware model interfaces such as Claude Code.
- ⭐ 86
- MCP
- mikechao/brave-search-mcp
Bing Search MCP Server
MCP server enabling Bing-powered web, news, and image search for AI assistants.
Bing Search MCP Server provides a Model Context Protocol (MCP) compliant interface for integrating Microsoft Bing Search API capabilities with AI assistants. The server allows AI clients to perform web, news, and image searches programmatically, with features like rate limiting and comprehensive error handling. Designed for easy deployment, it supports integration with clients such as Claude Desktop and Cursor for enhanced search access. Secure configuration via environment variables enables safe use of API keys.
- ⭐ 65
- MCP
- leehanchung/bing-search-mcp
Semgrep MCP Server
A Model Context Protocol server powered by Semgrep for seamless code analysis integration.
Semgrep MCP Server implements the Model Context Protocol (MCP) to enable efficient and standardized communication for code analysis tasks. It facilitates integration with platforms like LM Studio, Cursor, and Visual Studio Code, providing both Docker and Python (PyPI) deployment options. The tool is now maintained in the main Semgrep repository with continued updates, enhancing compatibility and support across developer tools.
- ⭐ 611
- MCP
- semgrep/mcp
Vectorize MCP Server
MCP server for advanced vector retrieval and text extraction with Vectorize integration.
Vectorize MCP Server is an implementation of the Model Context Protocol (MCP) that integrates with the Vectorize platform to enable advanced vector retrieval and text extraction. It supports seamless installation and integration within development environments such as VS Code. The server is configurable through environment variables or JSON configuration files and is suitable for use in collaborative and individual workflows requiring vector-based context management for models.
- ⭐ 97
- MCP
- vectorize-io/vectorize-mcp-server
Didn't find tool you were looking for?