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Tavily MCP Server

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Tavily Crawl

GitHub Repo stars npm

🎉 Introducing tavily-crawl + tavily-map in v0.2.1! 🎉

MCP demo

The Model Context Protocol (MCP) is an open standard that enables AI systems to interact seamlessly with various data sources and tools, facilitating secure, two-way connections.

Developed by Anthropic, the Model Context Protocol (MCP) enables AI assistants like Claude to seamlessly integrate with Tavily's advanced search and data extraction capabilities. This integration provides AI models with real-time access to web information, complete with sophisticated filtering options and domain-specific search features.

The Tavily MCP server provides:

  • search, extract, map, crawl tools

  • Real-time web search capabilities through the tavily-search tool

  • Intelligent data extraction from web pages via the tavily-extract tool

  • Powerful web mapping tool that creates a structured map of website

  • Web crawler that systematically explores websites

📚 Helpful Resources

  • Tutorial on combining Tavily MCP with Neo4j MCP server

  • Tutorial on integrating Tavily MCP with Cline in VS Code

Related MCP server: Tavily MCP Server

Prerequisites 🔧

Before you begin, ensure you have:

  • Tavily API key

    • If you don't have a Tavily API key, you can sign up for a free account here

  • Claude Desktop or Cursor

  • Node.js (v20 or higher)

    • You can verify your Node.js installation by running:

      • node --version

  • Git installed (only needed if using Git installation method)

    • On macOS: brew install git

    • On Linux:

      • Debian/Ubuntu: sudo apt install git

      • RedHat/CentOS: sudo yum install git

    • On Windows: Download Git for Windows

Tavily MCP server installation ⚡

Running with NPX

npx -y tavily-mcp@0.2.1

Installing via Smithery

To install Tavily MCP Server for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install @tavily-ai/tavily-mcp --client claude

Although you can launch a server on its own, it's not particularly helpful in isolation. Instead, you should integrate it into an MCP client. Below is an example of how to configure the Claude Desktop app to work with the tavily-mcp server.

Configuring MCP Clients ⚙️

This repository will explain how to configure VS Code, Cursor and Claude Desktop to work with the tavily-mcp server.

Configuring VS Code 💻

For one-click installation, click one of the install buttons below:

Install with NPX in VS Code Install with NPX in VS Code Insiders

Manual Installation

First check if there are install buttons at the top of this section that match your needs. If you prefer manual installation, follow these steps:

Add the following JSON block to your User Settings (JSON) file in VS Code. You can do this by pressing Ctrl + Shift + P (or Cmd + Shift + P on macOS) and typing Preferences: Open User Settings (JSON).

{
  "mcp": {
    "inputs": [
      {
        "type": "promptString",
        "id": "tavily_api_key",
        "description": "Tavily API Key",
        "password": true
      }
    ],
    "servers": {
      "tavily": {
        "command": "npx",
        "args": ["-y", "tavily-mcp@0.2.1"],
        "env": {
          "TAVILY_API_KEY": "${input:tavily_api_key}"
        }
      }
    }
  }
}

Optionally, you can add it to a file called .vscode/mcp.json in your workspace:

{
  "inputs": [
    {
      "type": "promptString",
      "id": "tavily_api_key",
      "description": "Tavily API Key",
      "password": true
    }
  ],
  "servers": {
    "tavily": {
      "command": "npx",
      "args": ["-y", "tavily-mcp@0.2.1"],
      "env": {
        "TAVILY_API_KEY": "${input:tavily_api_key}"
      }
    }
  }
}

Configuring Cline 🤖

The easiest way to set up the Tavily MCP server in Cline is through the marketplace with a single click:

  1. Open Cline in VS Code

  2. Click on the Cline icon in the sidebar

  3. Navigate to the "MCP Servers" tab ( 4 squares )

  4. Search "Tavily" and click "install"

  5. When prompted, enter your Tavily API key

Alternatively, you can manually set up the Tavily MCP server in Cline:

  1. Open the Cline MCP settings file:

    For macOS:

    # Using Visual Studio Code
code ~/Library/Application\ Support/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json

# Or using TextEdit
open -e ~/Library/Application\ Support/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json

    For Windows:

    code %APPDATA%\Code\User\globalStorage\saoudrizwan.claude-dev\settings\cline_mcp_settings.json

  2. Add the Tavily server configuration to the file:

    Replace your-api-key-here with your actual Tavily API key.

    {
  "mcpServers": {
    "tavily-mcp": {
      "command": "npx",
      "args": ["-y", "tavily-mcp@0.2.1"],
      "env": {
        "TAVILY_API_KEY": "your-api-key-here"
      },
      "disabled": false,
      "autoApprove": []
    }
  }
}

  3. Save the file and restart Cline if it's already running.

  4. When using Cline, you'll now have access to the Tavily MCP tools. You can ask Cline to use the tavily-search and tavily-extract tools directly in your conversations.

Configuring Cursor 🖥️

Note: Requires Cursor version 0.45.6 or higher

To set up the Tavily MCP server in Cursor:

  1. Open Cursor Settings

  2. Navigate to Features > MCP Servers

  3. Click on the "+ Add New MCP Server" button

  4. Fill out the following information:

    • Name: Enter a nickname for the server (e.g., "tavily-mcp")

    • Type: Select "command" as the type

    • Command: Enter the command to run the server:

      env TAVILY_API_KEY=your-api-key npx -y tavily-mcp@0.2.1

      Important: Replace your-api-key with your Tavily API key. You can get one at app.tavily.com/home

After adding the server, it should appear in the list of MCP servers. You may need to manually press the refresh button in the top right corner of the MCP server to populate the tool list.

The Composer Agent will automatically use the Tavily MCP tools when relevant to your queries. It is better to explicitly request to use the tools by describing what you want to do (e.g., "User tavily-search to search the web for the latest news on AI"). On mac press command + L to open the chat, select the composer option at the top of the screen, beside the submit button select agent and submit the query when ready.

Cursor Interface Example

Configuring the Claude Desktop app 🖥️

For macOS:

# Create the config file if it doesn't exist
touch "$HOME/Library/Application Support/Claude/claude_desktop_config.json"

# Opens the config file in TextEdit 
open -e "$HOME/Library/Application Support/Claude/claude_desktop_config.json"

# Alternative method using Visual Studio Code (requires VS Code to be installed)
code "$HOME/Library/Application Support/Claude/claude_desktop_config.json"

For Windows:

code %APPDATA%\Claude\claude_desktop_config.json

Add the Tavily server configuration:

Replace your-api-key-here with your actual Tavily API key.

{
  "mcpServers": {
    "tavily-mcp": {
      "command": "npx",
      "args": ["-y", "tavily-mcp@0.2.1"],
      "env": {
        "TAVILY_API_KEY": "your-api-key-here"
      }
    }
  }
}

2. Git Installation

  1. Clone the repository:

git clone https://github.com/tavily-ai/tavily-mcp.git
cd tavily-mcp

  1. Install dependencies:

npm install

  1. Build the project:

npm run build

Configuring the Claude Desktop app ⚙️

Follow the configuration steps outlined in the Configuring the Claude Desktop app section above, using the below JSON configuration.

Replace your-api-key-here with your actual Tavily API key and /path/to/tavily-mcp with the actual path where you cloned the repository on your system.

{
  "mcpServers": {
    "tavily": {
      "command": "npx",
      "args": ["/path/to/tavily-mcp/build/index.js"],
      "env": {
        "TAVILY_API_KEY": "your-api-key-here"
      }
    }
  }
}

Usage in Claude Desktop App 🎯

Once the installation is complete, and the Claude desktop app is configured, you must completely close and re-open the Claude desktop app to see the tavily-mcp server. You should see a hammer icon in the bottom left of the app, indicating available MCP tools, you can click on the hammer icon to see more detial on the tavily-search and tavily-extract tools.

Alt text

Now claude will have complete access to the tavily-mcp server, including the tavily-search and tavily-extract tools. If you insert the below examples into the Claude desktop app, you should see the tavily-mcp server tools in action.

Tavily Search Examples

  1. General Web Search:

Can you search for recent developments in quantum computing?

  1. News Search:

Search for news articles about AI startups from the last 7 days.

  1. Domain-Specific Search:

Search for climate change research on nature.com and sciencedirect.com

Tavily Extract Examples

  1. Extract Article Content:

Extract the main content from this article: https://example.com/article

✨ Combine Search and Extract ✨

You can also combine the tavily-search and tavily-extract tools to perform more complex tasks.

Search for news articles about AI startups from the last 7 days and extract the main content from each article to generate a detailed report.

Troubleshooting 🛠️

Common Issues

  1. Server Not Found

    • Verify the npm installation by running npm --verison

    • Check Claude Desktop configuration syntax by running code ~/Library/Application\ Support/Claude/claude_desktop_config.json

    • Ensure Node.js is properly installed by running node --version

  2. NPX related issues

  • If you encounter errors related to npx, you may need to use the full path to the npx executable instead.

  • You can find this path by running which npx in your terminal, then replace the "command": "npx" line with "command": "/full/path/to/npx" in your configuration.

  1. API Key Issues

    • Confirm your Tavily API key is valid

    • Check the API key is correctly set in the config

    • Verify no spaces or quotes around the API key

Acknowledgments ✨

Available Tools

4 tools
tavily-crawlA

A powerful web crawler that initiates a structured web crawl starting from a specified base URL. The crawler expands from that point like a tree, following internal links across pages. You can control how deep and wide it goes, and guide it to focus on specific sections of the site.

ParametersJSON Schema
NameRequiredDescriptionDefault
urlYesThe root URL to begin the crawl
max_depthNoMax depth of the crawl. Defines how far from the base URL the crawler can explore.
max_breadthNoMax number of links to follow per level of the tree (i.e., per page)
limitNoTotal number of links the crawler will process before stopping
instructionsNoNatural language instructions for the crawler
select_pathsNoRegex patterns to select only URLs with specific path patterns (e.g., /docs/.*, /api/v1.*)
select_domainsNoRegex patterns to select crawling to specific domains or subdomains (e.g., ^docs\.example\.com$)
allow_externalNoWhether to allow following links that go to external domains
categoriesNoFilter URLs using predefined categories like documentation, blog, api, etc
extract_depthNoAdvanced extraction retrieves more data, including tables and embedded content, with higher success but may increase latencybasic

TDQS

A3.9/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the crawling behavior ('expands like a tree', 'following internal links'), scope control ('how deep and wide'), and guidance capabilities ('focus on specific sections'). However, it doesn't mention important behavioral aspects like rate limits, authentication needs, error handling, or what the output looks like.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is perfectly concise with three sentences that each earn their place: first establishes the core crawling functionality, second explains the expansion behavior, third describes the control mechanisms. No wasted words and front-loaded with the main purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a complex tool with 10 parameters and no annotations or output schema, the description provides good basic context about the crawling behavior but lacks important details about what the tool returns, error conditions, performance characteristics, or how it differs meaningfully from its sibling tools in practical use cases.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all 10 parameters thoroughly. The description adds some context about the crawling approach ('tree expansion', 'internal links', 'specific sections') that helps understand the parameters' purpose, but doesn't provide additional syntax, format, or usage details beyond what the schema provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'initiates a structured web crawl starting from a specified base URL' with specific verbs ('crawl', 'expands', 'following') and resources ('web', 'internal links', 'pages'). It distinguishes from sibling tools by focusing on crawling rather than extraction, mapping, or searching.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when to use this tool ('structured web crawl', 'following internal links', 'control how deep and wide it goes'), but doesn't explicitly mention when not to use it or name alternatives like tavily-extract, tavily-map, or tavily-search for different scenarios.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

tavily-extractC

A powerful web content extraction tool that retrieves and processes raw content from specified URLs, ideal for data collection, content analysis, and research tasks.

ParametersJSON Schema
NameRequiredDescriptionDefault
urlsYesList of URLs to extract content from
extract_depthNoDepth of extraction - 'basic' or 'advanced', if usrls are linkedin use 'advanced' or if explicitly told to use advancedbasic
include_imagesNoInclude a list of images extracted from the urls in the response

TDQS

C2.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool 'retrieves and processes raw content' but lacks details on rate limits, authentication needs, error handling, or what 'processes' entails (e.g., formatting, filtering). For a web content extraction tool with potential complexities, this is insufficient behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and front-loaded with the core purpose in the first clause. It uses two sentences efficiently without redundancy. However, the second sentence ('ideal for data collection...') could be more specific to earn a perfect score, but overall it's well-structured and avoids waste.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 3 parameters with full schema coverage but no annotations or output schema, the description is moderately complete. It covers the basic purpose but lacks behavioral details (e.g., rate limits, error handling) and output information. For a web extraction tool with sibling alternatives, more context on differences and usage would improve completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema fully documents all parameters (urls, extract_depth, include_images). The description adds no parameter-specific information beyond what's in the schema, such as explaining why 'advanced' is needed for LinkedIn or what 'basic' vs. 'advanced' extraction entails. Baseline 3 is appropriate since the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'retrieves and processes raw content from specified URLs' with specific verbs and resources. It distinguishes from siblings by focusing on extraction rather than crawling, mapping, or searching, though it doesn't explicitly contrast with them. The mention of 'data collection, content analysis, and research tasks' adds context but doesn't fully differentiate from sibling tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no explicit guidance on when to use this tool versus its siblings (tavily-crawl, tavily-map, tavily-search). It mentions general use cases like 'data collection, content analysis, and research tasks' but offers no when/when-not rules or alternatives. This leaves the agent without clear direction for tool selection among similar options.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

tavily-mapB

A powerful web mapping tool that creates a structured map of website URLs, allowing you to discover and analyze site structure, content organization, and navigation paths. Perfect for site audits, content discovery, and understanding website architecture.

ParametersJSON Schema
NameRequiredDescriptionDefault
urlYesThe root URL to begin the mapping
max_depthNoMax depth of the mapping. Defines how far from the base URL the crawler can explore
max_breadthNoMax number of links to follow per level of the tree (i.e., per page)
limitNoTotal number of links the crawler will process before stopping
instructionsNoNatural language instructions for the crawler
select_pathsNoRegex patterns to select only URLs with specific path patterns (e.g., /docs/.*, /api/v1.*)
select_domainsNoRegex patterns to select crawling to specific domains or subdomains (e.g., ^docs\.example\.com$)
allow_externalNoWhether to allow following links that go to external domains
categoriesNoFilter URLs using predefined categories like documentation, blog, api, etc

TDQS

B3.1/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the tool 'creates a structured map' and allows 'discover and analyze,' but lacks critical details: it doesn't specify whether this is a read-only operation, potential rate limits, authentication requirements, output format, or error handling. For a complex web crawling tool with 9 parameters, this leaves significant behavioral gaps unaddressed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is efficiently structured in two sentences: the first defines the core functionality, and the second lists key use cases. Every phrase adds value without redundancy. While it could potentially benefit from more behavioral details given the tool's complexity, what's present is well-organized and front-loaded with the primary purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (9 parameters, web crawling functionality), lack of annotations, and absence of an output schema, the description is insufficiently complete. It doesn't address critical aspects like what the structured map output looks like, performance characteristics, error conditions, or how it differs from sibling tools. For a tool with this many configuration options and no structured safety hints, more comprehensive guidance is needed.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, meaning all parameters are documented in the schema itself. The description adds no specific parameter information beyond what's already in the schema descriptions (e.g., it doesn't explain parameter interactions or provide examples). However, it does contextualize the overall purpose ('structured map of website URLs') which helps interpret why parameters like max_depth and categories matter, meeting the baseline expectation when schema coverage is complete.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'creates a structured map of website URLs' for 'discover and analyze site structure, content organization, and navigation paths.' It specifies the verb (creates/maps) and resource (website URLs) with concrete use cases like site audits and content discovery. However, it doesn't explicitly differentiate from sibling tools like tavily-crawl or tavily-extract, which likely have overlapping web analysis functions.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage contexts ('Perfect for site audits, content discovery, and understanding website architecture') but doesn't provide explicit guidance on when to use this tool versus alternatives like tavily-crawl or tavily-extract. There's no mention of prerequisites, exclusions, or comparative scenarios with sibling tools, leaving the agent to infer appropriate usage from the described functionality alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.6/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: crawling (tavily-crawl) explores site structure, extraction (tavily-extract) retrieves content from URLs, mapping (tavily-map) analyzes site architecture, and searching (tavily-search) finds web information. There is no overlap in functionality, making tool selection straightforward for an agent.

Naming Consistency5/5

All tool names follow a consistent 'tavily-verb' pattern (tavily-crawl, tavily-extract, tavily-map, tavily-search). This uniform naming convention enhances readability and predictability, with no deviations in style or structure.

Tool Count5/5

With 4 tools, the server is well-scoped for web-related tasks, covering crawling, extraction, mapping, and searching. Each tool earns its place by addressing a specific aspect of web interaction without redundancy or excessive complexity.

Completeness4/5

The tool set provides comprehensive coverage for web content discovery and analysis, including search, extraction, and structural mapping. A minor gap exists in lacking explicit tools for content manipulation or summarization, but core workflows are fully supported.

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