Tavily MCP connector

1. Install the SDK

   Node.js

   npm install @scalekit-sdk/node

   Python

   pip install scalekit

   Full SDK reference: Node.js | Python

2. Set your credentials

   Add your Scalekit credentials to your .env file. Find values in app.scalekit.com > Developers > API Credentials.

   .env

   SCALEKIT_ENVIRONMENT_URL=<your-environment-url>
   SCALEKIT_CLIENT_ID=<your-client-id>
   SCALEKIT_CLIENT_SECRET=<your-client-secret>

3. Authorize and make your first call

   Node.js

   quickstart.ts

   import { ScalekitClient } from '@scalekit-sdk/node'
   import 'dotenv/config'
   
   const scalekit = new ScalekitClient(
     process.env.SCALEKIT_ENV_URL,
     process.env.SCALEKIT_CLIENT_ID,
     process.env.SCALEKIT_CLIENT_SECRET,
   )
   const actions = scalekit.actions
   
   const connector = 'tavilymcp'
   const identifier = 'user_123'
   
   // Generate an authorization link for the user
   const { link } = await actions.getAuthorizationLink({ connectionName: connector, identifier })
   console.log('Authorize Tavily MCP:', link)
   process.stdout.write('Press Enter after authorizing...')
   await new Promise(r => process.stdin.once('data', r))
   
   // Make your first call
   const result = await actions.executeTool({
     connector,
     identifier,
     toolName: 'tavilymcp_tavily_search',
     toolInput: { query: 'YOUR_QUERY' },
   })
   console.log(result)

   Python

   quickstart.py

   import os
   from scalekit.client import ScalekitClient
   from dotenv import load_dotenv
   load_dotenv()
   
   scalekit_client = ScalekitClient(
       env_url=os.getenv("SCALEKIT_ENV_URL"),
       client_id=os.getenv("SCALEKIT_CLIENT_ID"),
       client_secret=os.getenv("SCALEKIT_CLIENT_SECRET"),
   )
   actions = scalekit_client.actions
   
   connection_name = "tavilymcp"
   identifier = "user_123"
   
   # Generate an authorization link for the user
   link_response = actions.get_authorization_link(
       connection_name=connection_name,
       identifier=identifier,
   )
   print("Authorize Tavily MCP:", link_response.link)
   input("Press Enter after authorizing...")
   
   # Make your first call
   result = actions.execute_tool(
       tool_input={"query":"YOUR_QUERY"},
       tool_name="tavilymcp_tavily_search",
       connection_name=connection_name,
       identifier=identifier,
   )
   print(result)

What you can do

Connect this agent connector to let your agent:

• Search tavily — Search the web for current information and return snippets with source URLs
• Research tavily — Run comprehensive multi-source research on a topic or question
• Map tavily — Map a website’s URL structure starting from a base URL
• Extract tavily — Extract raw content from one or more URLs in markdown or plain text format
• Crawl tavily — Crawl a website from a starting URL and extract page content with configurable depth and breadth

Tool list

Use the exact tool names from the Tool list below when you call execute_tool. If you’re not sure which name to use, list the tools available for the current user first.

tavilymcp_tavily_crawl#Crawl a website from a starting URL and extract page content with configurable depth and breadth.11 params▾

Crawl a website from a starting URL and extract page content with configurable depth and breadth.

NameTypeRequiredDescription

urlstringrequiredThe root URL to begin the crawl

allow_externalbooleanoptionalWhether to return external links in the final response

extract_depthstringoptionalAdvanced extraction retrieves more data, including tables and embedded content, with higher success but may increase latency

formatstringoptionalThe format of the extracted web page content. markdown returns content in markdown format. text returns plain text and may increase latency.

include_faviconbooleanoptionalWhether to include the favicon URL for each result

instructionsstringoptionalNatural language instructions for the crawler. Instructions specify which types of pages the crawler should return.

limitintegeroptionalTotal number of links the crawler will process before stopping

max_breadthintegeroptionalMax number of links to follow per level of the tree (i.e., per page)

max_depthintegeroptionalMax depth of the crawl. Defines how far from the base URL the crawler can explore.

select_domainsarrayoptionalRegex patterns to restrict crawling to specific domains or subdomains (e.g., ^docs\.example\.com$)

select_pathsarrayoptionalRegex patterns to select only URLs with specific path patterns (e.g., /docs/.*, /api/v1.*)

tavilymcp_tavily_extract#Extract raw content from one or more URLs in markdown or plain text format.6 params▾

Extract raw content from one or more URLs in markdown or plain text format.

NameTypeRequiredDescription

urlsarrayrequiredList of URLs to extract content from

extract_depthstringoptionalUse 'advanced' for LinkedIn, protected sites, or tables/embedded content

formatstringoptionalOutput format

include_faviconbooleanoptionalInclude favicon URLs

include_imagesbooleanoptionalInclude images from pages

querystringoptionalQuery to rerank content chunks by relevance

tavilymcp_tavily_map#Map a website's URL structure starting from a base URL.8 params▾

Map a website's URL structure starting from a base URL.

NameTypeRequiredDescription

urlstringrequiredThe root URL to begin the mapping

allow_externalbooleanoptionalWhether to return external links in the final response

instructionsstringoptionalNatural language instructions for the crawler

limitintegeroptionalTotal number of links the crawler will process before stopping

max_breadthintegeroptionalMax number of links to follow per level of the tree (i.e., per page)

max_depthintegeroptionalMax depth of the mapping. Defines how far from the base URL the crawler can explore

select_domainsarrayoptionalRegex patterns to restrict crawling to specific domains or subdomains (e.g., ^docs\.example\.com$)

select_pathsarrayoptionalRegex patterns to select only URLs with specific path patterns (e.g., /docs/.*, /api/v1.*)

tavilymcp_tavily_research#Run comprehensive multi-source research on a topic or question.2 params▾

Run comprehensive multi-source research on a topic or question.

NameTypeRequiredDescription

inputstringrequiredA comprehensive description of the research task

modelstringoptionalDefines the degree of depth of the research. 'mini' is good for narrow tasks with few subtopics. 'pro' is good for broad tasks with many subtopics

tavilymcp_tavily_search#Search the web for current information and return snippets with source URLs.15 params▾

Search the web for current information and return snippets with source URLs.

NameTypeRequiredDescription

querystringrequiredSearch query

countrystringoptionalBoost search results from a specific country. Must be a full country name (e.g., 'United States', 'Japan', 'Germany'). ISO country codes (e.g., 'us', 'jp') are not supported. Available only if topic is general. See https://docs.tavily.com/documentation/api-reference/search for the full list of supported countries.

end_datestringoptionalWill return all results before the specified end date. Required to be written in the format YYYY-MM-DD

exact_matchstringoptionalOnly return results containing the exact phrase(s) in quotes in your query

exclude_domainsarrayoptionalList of domains to specifically exclude, if the user asks to exclude a domain set this to the domain of the site

include_domainsarrayoptionalA list of domains to specifically include in the search results, if the user asks to search on specific sites set this to the domain of the site

include_faviconbooleanoptionalWhether to include the favicon URL for each result

include_image_descriptionsbooleanoptionalInclude a list of query-related images and their descriptions in the response

include_imagesbooleanoptionalInclude a list of query-related images in the response

include_raw_contentbooleanoptionalInclude the cleaned and parsed HTML content of each search result

max_resultsintegeroptionalThe maximum number of search results to return

search_depthstringoptionalThe depth of the search. 'basic' for generic results, 'advanced' for more thorough search, 'fast' for optimized low latency with high relevance, 'ultra-fast' for prioritizing latency above all else

start_datestringoptionalWill return all results after the specified start date. Required to be written in the format YYYY-MM-DD.

time_rangestringoptionalThe time range back from the current date to include in the search results

topicstringoptionalThe category of the search. This will determine which of our agents will be used for the search