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
Basic information
Mirror of
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"TakumiY235_uniprot-mcp-server": {
"command": "uv",
"args": [
"pip",
"install",
"-r",
"requirements.txt"
]
}
}
}Tool list not available yet
This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.
We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
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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