Firecrawl MCP Server for Pydantic AIGive Pydantic AI instant access to 6 tools to Cancel Active Crawl, Get Api Usage, Get Crawl Status, and more
Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Firecrawl through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.
Ask AI about this App Connector for Pydantic AI
The Firecrawl app connector for Pydantic AI is a standout in the Friends Mcp category — giving your AI agent 6 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
agent = Agent(
model="openai:gpt-4o",
mcp_servers=[server],
system_prompt=(
"You are an assistant with access to Firecrawl "
"(6 tools)."
),
)
result = await agent.run(
"What tools are available in Firecrawl?"
)
print(result.data)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Firecrawl MCP Server
Connect your Firecrawl account to any AI agent and take full control of your web data acquisition and recursive crawling workflows through natural conversation.
Pydantic AI validates every Firecrawl tool response against typed schemas, catching data inconsistencies at build time. Connect 6 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.
What you can do
- Web Scrape Orchestration — Scrape any single URL programmatically into high-fidelity Markdown, excluding boilerplate content like headers and footers automatically
- Recursive Crawling — Programmatically discover and scrape all subpages starting from a root URL to build comprehensive knowledge bases and RAG pipelines
- Site Mapping — Quickly identify all reachable links on a domain without full content extraction to understand website structures and hierarchies
- Visual Capture — Capture full-page screenshots of any URL directly through your agent to maintain a visual record of web data
- Usage Monitoring — Track your Firecrawl credit usage, remaining limits, and active crawl job statuses in real-time
The Firecrawl MCP Server exposes 6 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
All 6 Firecrawl tools available for Pydantic AI
When Pydantic AI connects to Firecrawl through Vinkius, your AI agent gets direct access to every tool listed below — spanning data-extraction, markdown-conversion, rag-pipelines, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.
Stop an ongoing crawl
Check Firecrawl credit usage
Check the status of a crawl job
Discover all URLs on a site
Turn a single URL into clean Markdown
Returns a job ID. Recursively crawl a website
Connect Firecrawl to Pydantic AI via MCP
Follow these steps to wire Firecrawl into Pydantic AI. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install Pydantic AI
pip install pydantic-aiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use Pydantic AI with the Firecrawl MCP Server
Pydantic AI provides unique advantages when paired with Firecrawl through the Model Context Protocol.
Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application
Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Firecrawl integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your Firecrawl connection logic from agent behavior for testable, maintainable code
Firecrawl + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the Firecrawl MCP Server delivers measurable value.
Type-safe data pipelines: query Firecrawl with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple Firecrawl tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query Firecrawl and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock Firecrawl responses and write comprehensive agent tests
Example Prompts for Firecrawl in Pydantic AI
Ready-to-use prompts you can give your Pydantic AI agent to start working with Firecrawl immediately.
"Turn 'https://stripe.com/docs/api' into clean Markdown."
"Crawl 'https://docs.firecrawl.dev' recursively with a limit of 10 pages."
"Map all internal links for 'https://github.com/vinkius'."
Troubleshooting Firecrawl MCP Server with Pydantic AI
Common issues when connecting Firecrawl to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiFirecrawl + Pydantic AI FAQ
Common questions about integrating Firecrawl MCP Server with Pydantic AI.
How does Pydantic AI discover MCP tools?
MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.