Tavily Cursor MCP Server
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., "@Tavily Cursor MCP Server search for recent advancements in quantum computing"
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.
Tavily Cursor MCP Server
A custom Tavily MCP server with underscore-named tools for Cursor compatibility.
Why This Exists
The official Tavily MCP server uses hyphenated tool names (tavily-search, tavily-extract) which Cursor's CallMcpTool interface doesn't properly recognize. This custom server uses underscore naming (tavily_search, tavily_extract) to work seamlessly with Cursor.
Related MCP server: tavily-mcp
Features
✅ tavily_search - Web search with Tavily API
✅ tavily_extract - Extract clean content from URLs
✅ tavily_search_qna - Direct question answering
✅ tavily_search_context - Generate context for RAG applications
Installation
Option 1: Local Installation (Recommended)
Clone or download this directory to your local machine
Install dependencies:
cd tavily-cursor-mcp npm installMake the script executable (Mac/Linux):
chmod +x index.jsAdd to your Cursor
mcp.json:{ "mcpServers": { "tavily_cursor": { "command": "node", "args": ["/absolute/path/to/tavily-cursor-mcp/index.js"], "env": { "TAVILY_API_KEY": "your-tavily-api-key-here" } } } }Important: Replace
/absolute/path/to/tavily-cursor-mcp/with the actual full path to this directory.
Option 2: NPM Global Installation
Install globally:
cd tavily-cursor-mcp npm install -g .Add to your Cursor
mcp.json:{ "mcpServers": { "tavily_cursor": { "command": "tavily-cursor-mcp", "env": { "TAVILY_API_KEY": "your-tavily-api-key-here" } } } }
Configuration
Cursor MCP Configuration Location
Windows:
%APPDATA%\Cursor\User\globalStorage\mcp.jsonMac:
~/.cursor/mcp.jsonor workspace.cursor/mcp.jsonLinux:
~/.cursor/mcp.jsonor workspace.cursor/mcp.json
Get Your Tavily API Key
Go to https://tavily.com
Sign up or log in
Get your API key from the dashboard
Replace
your-tavily-api-key-herein the config with your actual key
Usage in Cursor
After installation and configuration, restart Cursor completely. Then use in Agent mode:
Use tavily_search to find the latest AI developmentsUse tavily_extract to get the content from https://example.comUse tavily_search_qna to answer: What is the capital of France?Available Tools
tavily_search
Search the web using Tavily API.
Parameters:
query(required): Search querysearch_depth: "basic" or "advanced" (default: "basic")topic: "general" or "news" (default: "general")days: Number of days back for news search (default: 3)max_results: Max results to return (default: 5, max: 20)include_images: Include images (default: false)include_answer: Include AI-generated answer (default: false)include_raw_content: Include raw HTML (default: false)
tavily_extract
Extract clean content from URLs.
Parameters:
urls(required): Array of URLs to extract from
tavily_search_qna
Get direct answers to questions.
Parameters:
query(required): The question to answersearch_depth: "basic" or "advanced" (default: "basic")
tavily_search_context
Generate context for RAG applications.
Parameters:
query(required): Search querysearch_depth: "basic" or "advanced" (default: "basic")max_results: Max results (default: 5)
Troubleshooting
Tools not showing up in Cursor
Make sure you've completely quit and restarted Cursor (not just closed the window)
Verify the path in
mcp.jsonis correct and absoluteCheck that Node.js is installed:
node --version(should be >= 18.0.0)Verify your Tavily API key is correct
"TAVILY_API_KEY environment variable is required" error
Make sure your API key is set in the env section of your mcp.json configuration.
Tools discovered but not usable
This was the original problem! This server fixes it by using underscores instead of hyphens in tool names.
Testing
You can test the server directly:
TAVILY_API_KEY=your-key-here node index.jsThen use the MCP Inspector or send MCP protocol messages via stdin.
License
MIT
Available Tools
4 toolstavily_extractA
Extract clean content from one or more URLs. Returns the main content from web pages, removing ads and navigation.
Inputs: urls (required).
tavily_searchB
Search the web using Tavily API. Returns relevant search results with URLs, content snippets, and metadata.
Inputs: query (required), search_depth, topic, days, +4 more.
tavily_search_contextC
Generate context for RAG applications. Returns search results optimized for context generation.
Inputs: query (required), search_depth, max_results.
tavily_search_qnaB
Get a direct answer to a question using Tavily's Q&A optimized search. Returns a concise answer to specific questions.
Inputs: query (required), search_depth.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 4 tools
The tools are mostly distinct, with clear primary functions: extraction, general search, context generation, and Q&A. However, tavily_search and tavily_search_context could be slightly confusing as both involve search, though their descriptions differentiate them well.
All tools follow a consistent 'tavily_verb' pattern, using snake_case uniformly. The naming is predictable and readable, with no deviations in style or convention.
With 4 tools, this server is well-scoped for its purpose of web search and content extraction. Each tool serves a specific, non-redundant function, making the count appropriate and manageable.
The toolset covers core web search and extraction workflows effectively, including content retrieval, general search, context generation, and direct Q&A. A minor gap might be the lack of advanced filtering or customization options, but agents can work around this for most use cases.
Related MCP Connectors
Web scraping, Google SERP, Markdown and ChatGPT/Gemini answers as typed tools for AI agents.
Web data for agents. Extract web pages, crawl websites, discover URLs, and search the web.
Web search, Google SERP, page extraction to markdown and cited answers for AI agents.
LLM-ready web search + instant answers + URL-to-clean-text fetch for agents and RAG.
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