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For Developer

Follow the instructions below to integrate Firecrawl Web Search MCP server to your AI application using our API or SDK.

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Prerequisites

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1. Create a Firecrawl Web Search MCP Server

Use the following endpoint to create a new remote Firecrawl Web Search MCP server instance:

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Request

from klavis import Klavis
from klavis.types import McpServerName

klavis_client = Klavis(api_key="<YOUR_API_KEY>")

# Create a Firecrawl Web Search MCP server instance
firecrawl_web_search_server = klavis_client.mcp_server.create_server_instance(
    server_name=McpServerName.FIRECRAWL_WEB_SEARCH,
    user_id="<YOUR_USER_ID>",
    platform_name="<YOUR_PLATFORM_NAME>",
)

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Response

{
  "serverUrl": "https://firecrawl-websearch-mcp-server.klavis.ai/sse?instance_id=<instance-id>",
  "instanceId": "<instance-id>"
}
serverUrl specifies the endpoint of the Firecrawl Web Search MCP server, which allows you to perform real-time web searches.
instanceId is used for authentication and identification of your server instance.

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2. Configure Firecrawl API Key

To use the Firecrawl Deep Research MCP Server, you need to configure it with your Firecrawl API key.

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Setting up Firecrawl API Key

curl --request POST \
  --url https://api.klavis.ai/mcp-server/instance/set-auth \
  --header 'Authorization: Bearer <YOUR_KLAVIS_API_KEY>' \
  --header 'Content-Type: application/json' \
  --data '{
  "instanceId": "<YOUR_INSTANCE_ID>",
  "authData": {
    "token": "<YOUR_FIRECRAWL_API_KEY>"
  }
}'

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Response

{
  "success": true,
  "message": "<string>"
}

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Explore MCP Server Tools

For more details about tool input schema, use the list_tool API.