> ## Documentation Index
> Fetch the complete documentation index at: https://docs.firecrawl.dev/llms.txt
> Use this file to discover all available pages before exploring further.
# Agent Development Kit (ADK)
> Integrate Firecrawl with Google's ADK using MCP for advanced agent workflows
Integrate Firecrawl with Google's Agent Development Kit (ADK) to build powerful AI agents with web scraping capabilities through the Model Context Protocol (MCP).
## Overview
Firecrawl provides an MCP server that seamlessly integrates with Google's ADK, enabling your agents to efficiently scrape, crawl, and extract structured data from any website. The integration supports both cloud-based and self-hosted Firecrawl instances with streamable HTTP for optimal performance.
## Features
* Efficient web scraping, crawling, and content discovery from any website
* Advanced search capabilities and intelligent content extraction
* Deep research
* Flexible deployment (cloud-based or self-hosted)
* Optimized for modern web environments with streamable HTTP support
## Prerequisites
* Obtain an API key for Firecrawl from [firecrawl.dev](https://firecrawl.dev)
* Install Google ADK
## Setup
```python Remote MCP Server theme={null}
from google.adk.agents.llm_agent import Agent
from google.adk.tools.mcp_tool.mcp_session_manager import StreamableHTTPServerParams
from google.adk.tools.mcp_tool.mcp_toolset import MCPToolset
FIRECRAWL_API_KEY = "YOUR-API-KEY"
root_agent = Agent(
model="gemini-2.5-pro",
name="firecrawl_agent",
description='A helpful assistant for scraping websites with Firecrawl',
instruction='Help the user search for website content',
tools=[
MCPToolset(
connection_params=StreamableHTTPServerParams(
url=f"https://mcp.firecrawl.dev/{FIRECRAWL_API_KEY}/v2/mcp",
),
)
],
)
```
```python Local MCP Server theme={null}
from google.adk.agents.llm_agent import Agent
from google.adk.tools.mcp_tool.mcp_session_manager import StdioConnectionParams
from google.adk.tools.mcp_tool.mcp_toolset import MCPToolset
from mcp import StdioServerParameters
root_agent = Agent(
model='gemini-2.5-pro',
name='firecrawl_agent',
description='A helpful assistant for scraping websites with Firecrawl',
instruction='Help the user search for website content',
tools=[
MCPToolset(
connection_params=StdioConnectionParams(
server_params = StdioServerParameters(
command='npx',
args=[
"-y",
"firecrawl-mcp",
],
env={
"FIRECRAWL_API_KEY": "YOUR-API-KEY",
}
),
timeout=30,
),
)
],
)
```
## Available Tools
| Tool | Name | Description |
| ------------------ | ------------------------------ | -------------------------------------------------------------------- |
| Scrape Tool | `firecrawl_scrape` | Scrape content from a single URL with advanced options |
| Map Tool | `firecrawl_map` | Map a website to discover all indexed URLs on the site |
| Search Tool | `firecrawl_search` | Search the web and optionally extract content from search results |
| Crawl Tool | `firecrawl_crawl` | Start an asynchronous crawl with advanced options |
| Check Crawl Status | `firecrawl_check_crawl_status` | Check the status of a crawl job |
| Extract Tool | `firecrawl_extract` | Extract structured information from web pages using LLM capabilities |
## Configuration
### Required Configuration
**FIRECRAWL\_API\_KEY**: Your Firecrawl API key
* Required when using cloud API (default)
* Optional when using self-hosted instance with FIRECRAWL\_API\_URL
### Optional Configuration
**Firecrawl API URL (for self-hosted instances)**:
* `FIRECRAWL_API_URL`: Custom API endpoint
* Example: `https://firecrawl.your-domain.com`
* If not provided, the cloud API will be used
## Example: Web Research Agent
```python theme={null}
from google.adk.agents.llm_agent import Agent
from google.adk.tools.mcp_tool.mcp_session_manager import StreamableHTTPServerParams
from google.adk.tools.mcp_tool.mcp_toolset import MCPToolset
FIRECRAWL_API_KEY = "YOUR-API-KEY"
# Create a research agent
research_agent = Agent(
model="gemini-2.5-pro",
name="research_agent",
description='An AI agent that researches topics by scraping and analyzing web content',
instruction='''You are a research assistant. When given a topic or question:
1. Use the search tool to find relevant websites
2. Scrape the most relevant pages for detailed information
3. Extract structured data when needed
4. Provide comprehensive, well-sourced answers''',
tools=[
MCPToolset(
connection_params=StreamableHTTPServerParams(
url=f"https://mcp.firecrawl.dev/{FIRECRAWL_API_KEY}/v2/mcp",
),
)
],
)
# Use the agent
response = research_agent.run("What are the latest features in Python 3.13?")
print(response)
```
## Best Practices
1. **Use the right tool for the job**:
* `firecrawl_search` when you need to find relevant pages first
* `firecrawl_scrape` for single pages
* `firecrawl_crawl` for discovering and scraping entire sites
* repeated `firecrawl_scrape` calls when you already have a short list of known URLs
2. **Monitor your usage**: Use your Firecrawl dashboard and API responses to track credit usage.
3. **Handle errors gracefully**: Surface MCP/API errors to the user and retry only when your agent workflow can do so safely.
4. **Optimize performance**: Use `firecrawl_map` before scraping when the agent needs to discover relevant URLs.
***
## Related Resources
Learn how to build powerful multi-agent AI systems using Google's ADK framework with Firecrawl for web scraping capabilities.
Learn more about Firecrawl's Model Context Protocol (MCP) server integration and capabilities.
Explore the official Google Agent Development Kit documentation for comprehensive guides and API references.