@ref-tools
基本信息
Helping coding agents never make mistakes working with public or private libraries without wasting the context window.
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"Ref": {
"command": "npx",
"args": [
"ref-tools-mcp@latest"
],
"env": {
"REF_API_KEY": "YOUR_API_KEY"
}
}
}
}Search for documentation on the web or github as well from private resources like repos and pdfs. Use Ref 'ref_read_url' to read the content of a url.
Read the content of a url as markdown. The EXACT url from a 'ref_search_documentation' result should be passed to this tool.
Ref MCP is a Model Context Protocol server that gives your AI coding tool or agent access to documentation for APIs, services, and libraries. It is designed for developers using LLM-based coding agents who need fast, token‑efficient access to up‑to‑date documentation.
Install via npm (npx ref-tools-mcp) or connect via the streamable HTTP server at api.ref.tools/mcp. Use the tools ref_search_documentation (with a query string) and ref_read_url (with a URL) to search and fetch documentation.
It provides two tools: ref_search_documentation searches for relevant documentation based on a natural language query, and ref_read_url fetches the content of a URL, converting it to markdown and returning the most relevant portion (up to 5k tokens).
Sign up at ref.tools to obtain an API key. The key is required for both the HTTP and stdio setup methods.
Two transport options: streamable HTTP (recommended) and local stdio. The HTTP server URL is `https://api.ref.tools/mcp?apiKey=YOUR_API_KEY`; the stdio command is `npx ref-tools-mcp@latest` with the environment variable `REF_API_KEY`.
It deduplicates repeated search results in a session and fetches only the most relevant 5k token portion of a page based on the agent’s search history, avoiding the common problem of pulling 20k+ irrelevant tokens from large documentation pages.
More context can make models less accurate (context rot), and extra tokens cost money—for example, 6k irrelevant tokens in a step using Claude Opus can waste ~$0.09 per step, adding up across multiple steps.
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