Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@deepseek-search-mcp search for the latest DeepSeek search API docs and fetch the top result"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
本地 MCP Server，把 DeepSeek Search & Fetch API 封装成两个 MCP 工具，让任何支持 MCP 的 AI Agent（Kimi Code、Claude Code、Cursor、Claude Desktop 等）都能使用 DeepSeek 自研搜索引擎进行网页搜索和抓取。
A local MCP server that wraps the DeepSeek Search & Fetch APIs, exposing deepseek_search and deepseek_fetch tools to any MCP-compatible AI agent.
工具 | 说明 |
| 网页搜索，返回标题、URL、摘要、发布时间、相关性评分（0–3，≥1.5 为优质）和权威性评分（0–4，≥3 为极权威）。支持 |
| 抓取单个网页，返回 Markdown 正文 |
工程特性：
纯本地 stdio 模式，无需部署服务器
API Key 只从环境变量 DEEPSEEK_API_KEY 读取
408 / 429 / 5xx 自动指数退避重试（最多 3 次），其他 4xx 不重试
60 秒请求超时
Related MCP server: DeepSeek Search MCP Server
Node.js ≥ 18（使用了全局 fetch）
一个 DeepSeek API Key（https://platform.deepseek.com/）
git clone https://github.com/Wildare-98/deepseek-search-mcp.git
cd deepseek-search-mcp
npm install在 MCP 客户端的配置文件中加入（以 Kimi Code / Claude Desktop / Cursor 的 JSON 格式为例）：
{
"mcpServers": {
"deepseek-search": {
"command": "node",
"args": ["/绝对路径/deepseek-search-mcp/index.js"],
"env": {
"DEEPSEEK_API_KEY": "sk-你的key"
}
}
}
}各客户端配置文件位置：
Kimi Code：~/.kimi-code/mcp.json（或 $KIMI_CODE_HOME/mcp.json）
Claude Desktop：%APPDATA%\Claude\claude_desktop_config.json（Windows）/ ~/Library/Application Support/Claude/claude_desktop_config.json（macOS）
Cursor：设置 → MCP 面板
也可以把 DEEPSEEK_API_KEY 设为系统环境变量，然后从配置中删掉 env 段，子进程会自动继承。
改完配置后重启客户端（Kimi Code 用 /new 开新会话），用 /mcp 或对应面板确认连接状态。
先 deepseek_search（need_content=false）拿到概览；
按 rerank_score、authority、date 挑出 1–3 个最佳结果；
只对选中的 URL 调用 deepseek_fetch 读全文。
这样最省上下文，也最稳定。
Search / Fetch 均为每 1,000 次成功请求 5 元，失败、无结果、超时不计费
Search 限速 100 QPS，Fetch 限速 25 QPS
详见 DeepSeek 官方文档。
MIT
This server cannot be deployed
Scrape, crawl and search the web for AI agents via MCP.
Fetch pages as markdown, search web and news, extract structured data. For AI agents.
Your agent needs the open web — searched by more than one engine, and read as clean markdown rather than raw HTML. **What you can ask for** • "Search this question with two providers and tell me where they disagree." • "Scrape these 40 URLs into markdown, in one batch." • "Crawl this documentation site and give me every page." • "Do deep research on this topic and cite the sources." • "Find the academic papers behind this claim." **How to use it** Point any MCP client at https://mcp.aisa.one/search/mcp and sign in with OAuth — there is no key to create or paste. 30 tools across several independent providers: Tavily and Exa search, answers, contents and agent runs; Firecrawl scrape, batch scrape, crawl, map and search; Perplexity Sonar, Sonar Pro, reasoning and deep research; Oxylabs AI search and LLM jobs; OpenAI and Anthropic web search; and scholarly search. **Why this rather than the source** Several independent indexes behind one account, because one engine's blind spot is not visible from inside it. **It is also a door to the rest** The same login reaches 26 sources and 580+ operations. Find the page here, then ask the same agent who links to it or how much traffic it gets — without adding a second server. **What it costs** Finding and inspecting an operation is free. Running one is billed per call at API prices, with no seat and no monthly minimum, and every call takes max_price_usd so an agent cannot overspend by accident. **Where else it reaches** https://mcp.aisa.one/seo-serp/mcp for the Google results page itself, https://mcp.aisa.one/seo-serp-other-engines/mcp for Bing, Baidu and Naver.
Free web search for AI agents. No API key required. Hosted MCP in active development.
