MCP server

Tavily

io.github.pipeworx-io/tavily

Checked 26 Oct 2026, 08:29 UTC · Connected to it and listed its tools

Tavily MCP — wraps the Tavily API (tavily.com)

Website Source

At a glance

Type

MCP server

Publisher

pipeworx.io (verified by the MCP Registry)

Status

Working at last check

Tools

45

Token cost

About 27,273 tokens per request

Sets up in

Claude Code, Claude Desktop, Codex, Gemini CLI, Cursor, VS Code, Antigravity, ChatGPT, OpenAI API, Grok

Last checked

26 Oct 2026, 08:29 UTC

Connect it to your agent

Add to CursorAdd to VS CodeVS Code Insiders

Opens the app, which shows the server and asks you to confirm. For other apps, copy the setup below.

Claude Code

claude mcp add --transport http tavily https://gateway.pipeworx.io/tavily/mcp

Claude Desktop

Settings → Connectors → Add custom connector, and paste the URL.

Codex (config.toml)

[mcp_servers.tavily]
url = "https://gateway.pipeworx.io/tavily/mcp"

Gemini CLI

{"mcpServers":{"tavily":{"httpUrl":"https://gateway.pipeworx.io/tavily/mcp"}}}

Cursor

{"mcpServers":{"tavily":{"url":"https://gateway.pipeworx.io/tavily/mcp"}}}

VS Code

{"servers":{"tavily":{"type":"http","url":"https://gateway.pipeworx.io/tavily/mcp"}}}

Antigravity

agy mcp add tavily https://gateway.pipeworx.io/tavily/mcp

ChatGPT

Settings → Apps & Connectors → Advanced settings: turn on Developer mode, then Create a connector and paste the URL. Which plans can do this varies.

OpenAI API (Responses tools)

{"type":"mcp","server_label":"tavily","server_url":"https://gateway.pipeworx.io/tavily/mcp","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"tavily","server_url":"https://gateway.pipeworx.io/tavily/mcp"}

Tools

• extract ~219 tokens
  Extract clean article text from one or more URLs via Tavily: strips boilerplate/navigation and returns up to 20,000 chars of readable content per page. Accepts a single URL string or an array. Ideal for feeding source pages into an LLM.
• search ~388 tokens
  AI/LLM-optimized web search built for RAG: returns a synthesized natural-language answer plus a ranked list of sourced results (title, url, content snippet, relevance score). Prefer this over scraping a generic search engine when you need grounded, citable web context. Example: search({ query: "late
• tavily_news ~363 tokens
  Recent NEWS coverage with FULL ARTICLE TEXT — "latest news about <company/person/topic>", "news coverage of X this week", "what are people reporting about X", "recent headlines on X". Returns a synthesized answer plus ranked articles each carrying title, url, publication content, and a relevance sco

Browse

• GitHub tools
• Web scraping
• Web search
• Website monitoring
• GitHub and code hosting

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