What are the prerequisites for installation?
Node.js v16 or higher and npm or yarn are required.
@Augmented-Nature
基本信息
A comprehensive Model Context Protocol (MCP) server providing advanced access to the UniProt protein database.
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"augmented-nature-uniprot-mcp-server-augmented-nature": {
"command": "docker",
"args": [
"build",
"-t",
"uniprot-mcp-server",
"."
]
}
}
}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。
An MCP server that provides 26 specialized bioinformatics tools for accessing the UniProt protein database via its REST API, enabling AI assistants and MCP clients to perform protein research, comparative genomics, structural biology, and systems biology analyses.
Install Node.js v16+, clone the repository, run npm install and npm build, then start with npm start or add the server to an MCP client (e.g., Claude Desktop) using the JSON configuration provided. Docker deployment is also supported: build the image with docker build -t uniprot-mcp-server and run with docker run -i uniprot-mcp-server.
Node.js v16 or higher and npm or yarn are required.
Add the following to your MCP client config: `{"mcpServers": {"uniprot": {"command": "node", "args": ["/path/to/build/index.js"], "env": {}}}}`.
It integrates with the UniProt REST API (`https://rest.uniprot.org`), setting a User-Agent header (`UniProt-MCP-Server/1.0.0`) and a 30‑second timeout.
Results can be returned in JSON, TSV, FASTA, or XML format, depending on the tool.
Yes. You can build a Docker image and run it with `docker run -i uniprot-mcp-server`, or use Docker Compose for easier management.

A free remote MCP server for The Living Bread. Bible passages read word for word from a stored public domain text with an evidence label, keyword search and cross references, real churches, gatherings and prayer groups w
This open-source curriculum introduces the fundamentals of Model Context Protocol (MCP) through real-world, cross-language examples in .NET, Java, TypeScript, JavaScript, Rust and Python. Designed for developers, it focuses on practical techniques for building modular, scalable,

DeepMark helps teachers deliver rapid, consistent marking with meaningful feedback for every student — in a fraction of the time. What once took a week, now takes one free period.
A curated list of references for MLOps

Model Context Protocol Servers
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