How do I find my PhantomBuster API Key?

In your PhantomBuster account, click on your name in the top right, go to **Org settings** or **Workspace settings**, and look for the **API Key** section.

Can I provide arguments when launching a Phantom?

Yes! The `launch_phantom` action accepts an optional `argument` field where you can provide a JSON string of parameters to override the default Phantom configuration.

How do I see the data extracted by a Phantom?

Use the `get_phantom_output` tool with the specific agent ID. It will return the results of the last successful execution of that Phantom.

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

Name: PhantomBuster
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 PhantomBuster 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 PhantomBuster "
            "(10 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in PhantomBuster?"
    )
    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 PhantomBuster MCP Server

Connect your PhantomBuster account to any AI agent and take full control of your lead generation and web automation workflows through natural conversation.

Pydantic AI validates every PhantomBuster 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

Agent Oversight — List all your Phantoms and workflows to maintain visibility over your automation suite.
Automation Control — Launch and abort Phantoms directly through the agent, including support for custom arguments.
Result Retrieval — Fetch the latest outputs and data extracted by your Phantoms for immediate analysis.
Configuration Auditing — Review the setup and arguments of any Phantom to verify your automation logic.
Usage Monitoring — Get account settings and usage info to track your available execution time.

The PhantomBuster 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 PhantomBuster to Pydantic AI via MCP

Follow these steps to integrate the PhantomBuster 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 PhantomBuster with type-safe schemas

Why Use Pydantic AI with the PhantomBuster MCP Server

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

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

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

PhantomBuster + Pydantic AI Use Cases

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

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

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

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

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

PhantomBuster MCP Tools for Pydantic AI (10)

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

abort_phantom

Stop a running Phantom

get_phantom

Get details for a specific Phantom

get_phantom_output

Get output/results from a Phantom

get_phantom_setup

Get configuration arguments for a Phantom

get_phantombuster_account

Get account settings and usage info

get_workflow

Get details for a specific workflow

launch_phantom

Start a Phantom execution

list_containers

List all Phantom containers

list_phantoms

List all Phantom agents

list_workflows

List all automation workflows

Example Prompts for PhantomBuster in Pydantic AI

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

"List all Phantoms in my account and show their current status."

"Launch the 'LinkedIn Search Export' Phantom with ID '12345'."

"Show me the extracted data from the last run of Phantom 12345."

Troubleshooting PhantomBuster MCP Server with Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

PhantomBuster + Pydantic AI FAQ

Common questions about integrating PhantomBuster 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 PhantomBuster MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.