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No-Code

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For Developer

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

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Prerequisites

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1. Create a Firecrawl Deep Research MCP Server

Use the following endpoint to create a new remote Firecrawl Deep Research 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 Deep Research MCP server instance
firecrawl_deep_research_server = klavis_client.mcp_server.create_server_instance(
    server_name=McpServerName.FIRECRAWL_DEEP_RESEARCH,
    user_id="<YOUR_USER_ID>",
    platform_name="<YOUR_PLATFORM_NAME>",
)

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Response

{
  "serverUrl": "https://firecrawl-deepresearch-mcp-server.klavis.ai/sse?instance_id=<instance-id>",
  "instanceId": "<instance-id>"
}
serverUrl specifies the endpoint of the Firecrawl Deep Research MCP server, which you can connect and use this MCP Server to perform comprehensive research on any topic.
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-token \
  --header 'Authorization: Bearer <YOUR_KLAVIS_API_KEY>' \
  --header 'Content-Type: application/json' \
  --data '{
  "instanceId": "<YOUR_INSTANCE_ID>",
  "authToken": "<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.