What dependencies does UniProt MCP Server require?
Python 3.10 or higher, the MCP Python SDK, httpx for async HTTP requests, and an OrderedDict-based cache.
@MCP-Mirror
基本信息
Mirror of
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"TakumiY235_uniprot-mcp-server": {
"command": "uv",
"args": [
"pip",
"install",
"-r",
"requirements.txt"
]
}
}
}工具列表暂未提供
当前收录暂时没有可展示的工具列表,不代表服务没有工具。请查阅项目文档了解支持的能力。
工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。
A Model Context Protocol (MCP) server that provides access to UniProt protein information, allowing AI assistants to fetch protein function and sequence data directly from UniProt using accession numbers.
Install Python 3.10+, clone the repository, install dependencies with uv pip install -r requirements.txt (or pip), then add the server configuration to your Claude Desktop config file (Windows: %APPDATA%\Claude\claude_desktop_config.json, macOS: ~/Library/Application Support/Claude/claude_desktop_config.json, Linux: ~/.config/Claude/claude_desktop_config.json). Use the get_protein_info tool for single proteins and get_batch_protein_info for multiple proteins.
Python 3.10 or higher, the MCP Python SDK, httpx for async HTTP requests, and an OrderedDict-based cache.
It handles invalid accession numbers (404), network errors, rate limiting (429), malformed responses, and cache management with TTL and size limits.
Data is fetched live from the UniProt API; the server does not store data beyond the 24-hour cache.
The server uses the Model Context Protocol standard transport; no authentication is required as UniProt’s API is public.
The server relies on UniProt API availability and rate limiting; batch requests are limited to the server’s retry logic and cache size.

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Model Context Protocol Servers

Model Context Protocol Servers
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