Can I trigger a data extraction for a specific URL?

Yes! Use the `run_robot` tool with the Robot ID and the target URL (`origin_url`). Your agent will create a new task in Browse AI to extract the data instantly.

How do I retrieve the data once a robot finishes its task?

Simply ask the agent to `get_task` and provide the Task ID. If the status is 'successful', it will return the JSON structure containing all the captured data fields.

Can I list all the robots I have trained in my account?

Yes. Use the `list_robots` tool. It will retrieve all the approved robots currently available in your Browse AI dashboard, including their names and unique IDs.

Browse AI MCP Server for Pydantic AI 10 tools — connect in under 2 minutes

Name: Browse AI
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 10 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Browse AI through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

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 Browse AI "
            "(10 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Browse AI?"
    )
    print(result.data)

asyncio.run(main())

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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 Browse AI MCP Server

Connect your Browse AI account to any AI agent and orchestrate your web scraping, data extraction, and website monitoring workflows through natural conversation.

Pydantic AI validates every Browse AI tool response against typed schemas, catching data inconsistencies at build time. Connect 10 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

Robot Oversight — List all your approved robots and retrieve detailed metadata for each scraper.
Task Execution — Trigger robot runs (tasks) on specific URLs and monitor their progress in real-time.
Data Retrieval — Retrieve structured data captured by your robots directly into your workspace.
Website Monitoring — List and create monitor schedules to track changes on any website automatically.
Bulk Operations — Manage and inspect bulk runs to extract data from multiple sources at once.
System Status — Check the health and queue status of the Browse AI infrastructure.

The Browse AI MCP Server exposes 10 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.

How to Connect Browse AI to Pydantic AI via MCP

Follow these steps to integrate the Browse AI MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 10 tools from Browse AI with type-safe schemas

Why Use Pydantic AI with the Browse AI MCP Server

Pydantic AI provides unique advantages when paired with Browse AI 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 Browse AI integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Browse AI connection logic from agent behavior for testable, maintainable code

Browse AI + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Browse AI MCP Server delivers measurable value.

Type-safe data pipelines: query Browse AI with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Browse AI tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Browse AI and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Browse AI responses and write comprehensive agent tests

Browse AI MCP Tools for Pydantic AI (10)

These 10 tools become available when you connect Browse AI to Pydantic AI via MCP:

create_monitor

Create a new monitor schedule for a robot

get_bulk_run

Get details of a specific bulk run

get_robot

Get details of a specific robot

get_system_status

Check Browse AI system and queue status

get_task

Get status and extracted data for a task

list_bulk_runs

List all bulk runs for a robot

list_monitors

List all monitors for a specific robot

list_robots

List all approved robots

list_tasks

List all tasks for a specific robot

run_robot

Run a robot to extract data (creates a task)

Example Prompts for Browse AI in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Browse AI immediately.

"List all my approved web scraping robots."

"Run robot rob_123 on https://example.com/product."

"Retrieve the data from task task_99283."

Troubleshooting Browse AI MCP Server with Pydantic AI

Common issues when connecting Browse AI to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Browse AI + Pydantic AI FAQ

Common questions about integrating Browse AI MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Browse AI MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.