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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.

Prerequisites

1. Create a Firecrawl Web Search MCP Server

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

Request

from klavis import Klavis
from klavis.types import McpServerName, ConnectionType

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>",
)

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.

2. Configure Firecrawl API Key

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

Setting up Firecrawl API Key

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

Response

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

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