How to Use the Firecrawl MCP in Pydantic AI

Q: Can I stop a Firecrawl job using Pydantic AI tools?

Use cancelactivecrawl. It is a direct tool call that halts any ongoing job for the provided ID.

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Connect Firecrawl MCP to Pydantic AI

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Setup Firecrawl with Pydantic AI

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Type-safe scraping for Pydantic AI

Use `scrape_url` to retrieve markdown and validate the structure against your Pydantic models. If the output doesn't match your schema, the agent will throw a validation error immediately. This ensures your agent never processes hallucinated fields or malformed data. You maintain strict control over the inputs your application relies on.

Reliable crawls for Pydantic AI

Call `start_crawl` to initiate large-scale data collection. Because MCP responses are validated, you can trust the job ID format and status updates returned by `get_crawl_status`. If the job stalls, `cancel_active_crawl` provides a clean way to terminate the request. You keep your pipeline clean and avoid silent failures during long-running tasks.

Audit API usage in Pydantic AI

The `get_api_usage` tool returns a structured response that you can map to a Pydantic model. Monitor your credits to prevent unexpected service interruptions during critical agent operations. Use `map_website_structure` to define the bounds of your crawl. By validating the returned URL list, you ensure your agent only targets the specific subdirectories you have authorized.

Setup guide

Set up Firecrawl MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "firecrawl-extended-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Firecrawl tools.",
)

result = await agent.run("List recent Firecrawl transactions")
print(result.output)

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Common questions about Firecrawl MCP in Pydantic AI

Define a Pydantic model for the expected markdown metadata. The MCP toolset handles the validation, ensuring the data matches your model before the agent uses it.

Yes. You can scrape content and immediately parse it into your Pydantic schemas, ensuring no malformed data enters your agent's memory.

Use `cancel_active_crawl`. It is a direct tool call that halts any ongoing job for the provided ID.

Your API keys and scraped data are isolated. We use secure HTTP/SSE transport, and the server is ephemeral, meaning your data isn't stored between requests.

Map the site to a list of URLs. You can then use Pydantic to filter this list based on your specific ingestion rules before triggering a scrape.

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