DeepSeek Search MCP Server
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- AlicenseAqualityBmaintenanceEnables AI agents to perform multi-engine web search, fetch web pages, and extract clean Markdown content via MCP, with no API keys required.375 PyPI8MIT
- AlicenseBqualityBmaintenanceEnables AI agents to perform web searches and extract full-text content from web pages via standard MCP tools, with fallback search and semantic reranking.22MIT
- AlicenseAqualityBmaintenanceEnables AI agents to search the web and fetch webpage content as clean text via MCP, with optional headless rendering for dynamic pages.3MIT
- AlicenseBqualityCmaintenanceEnables MCP clients to run web searches via DeepSeek's open platform API, with optional web page fetching that returns Markdown content, and supports query syntax for source, language, and time filters.1MIT
- FlicenseNot gradedqualityCmaintenanceEnables AI agents to perform unified web research through a single MCP server, including search, page fetching, recursive crawling, document parsing, YouTube transcript extraction, and deep multi-query research.3-
- AlicenseAqualityCmaintenanceExposes DeepSeek's Search and Fetch REST APIs as two read-only MCP tools, letting any MCP-capable agent run ranked web searches and retrieve individual pages as truncated Markdown. Works with whichever model the host runs, over stdio or Streamable HTTP.2MIT
TDQS
Scored across 2 tools
deepseek_search and deepseek_fetch have clearly distinct purposes: one queries the search engine, the other retrieves and converts a specific URL to Markdown. An agent can trivially tell which to call based on whether it has a query or a known URL.
Both tools use the same deepseek_ prefix followed by a concise action verb (search, fetch). The pattern is predictable and readable, with no mixing of conventions.
Two tools is on the lean side, but for a web-retrieval server this is exactly the minimal complete pair (find then read). Nothing feels missing or padded, though a multi-URL or batch fetch would be a natural addition.
Search plus fetch covers the full retrieval lifecycle: discovering sources and then reading them in depth. There are no obvious dead ends for the stated purpose of web search and content extraction.