How to Use the WhoisXML MCP in Pydantic AI

Q: How does WhoisXML fit with Pydantic AI?

The pydantic-ai framework treats the entire MCP Server as a toolset, validating every single response against your Python models. This guarantees that when your agent gets data from WhoisXML, it's always structured correctly.

Q: What kind of geolocation data can WhoisXML give Pydantic AI?

The getipgeolocation tool provides location data for an IP address. Your agent will receive this output, and Pydantic validates that the resulting coordinates fit your expected geographical schema.

Q: Can I use WhoisXML in my autonomous pipelines with Pydantic AI?

Absolutely. Since pydantic-ai is model-agnostic, you can hook the WhoisXML tools into any LLM—OpenAI, Anthropic, or Gemini—and your pipeline remains type-safe end-to-end.

Q: Which specific tools are exposed by WhoisXML to Pydantic AI?

Your agent can use getwhoisrecord, checkdomainavailability, verifyemail, and getipgeolocation. Every call is type-checked against your schemas.

Get rock-solid domain data with Pydantic AI, validated against your schemas.

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Connect WhoisXML MCP to Pydantic AI

Create your Vinkius account to connect WhoisXML to Pydantic AI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup WhoisXML with Pydantic AI

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Perplexity

Retrieve detailed WHOIS records

The `get_whois_record` tool pulls both raw and parsed WHOIS information. Since you're using `pydantic-ai`, the agent validates this complex data against your defined schema, eliminating unexpected fields. This means if the API changes, your agent fails loudly with a validation error instead of silently corrupting your process.

Determine domain availability

Need to check if `example.com` is available? The `check_domain_availability` tool handles this. With Pydantic, the boolean response will always match the expected type—no guesswork required. It makes your agent's decision-making process predictable and correct.

Verify email structure and existence

The `verify_email` tool checks an address. Crucially, Pydantic validates the output of this check, ensuring that even if the service returns a weird JSON blob, your agent only receives clean data structures. Correctness is everything here; it prevents bad data from poisoning your models.

Setup guide

Set up WhoisXML 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": {
        "whoisxml-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about WhoisXML MCP in Pydantic AI

The `pydantic-ai` framework treats the entire MCP Server as a toolset, validating every single response against your Python models. This guarantees that when your agent gets data from `WhoisXML`, it's always structured correctly.

The `get_ip_geolocation` tool provides location data for an IP address. Your agent will receive this output, and Pydantic validates that the resulting coordinates fit your expected geographical schema.

Absolutely. Since `pydantic-ai` is model-agnostic, you can hook the WhoisXML tools into any LLM—OpenAI, Anthropic, or Gemini—and your pipeline remains type-safe end-to-end.

Your agent can use `get_whois_record`, `check_domain_availability`, `verify_email`, and `get_ip_geolocation`. Every call is type-checked against your schemas.

The server touches domains, IP addresses, emails, and WHOIS records. The unique power of `pydantic-ai` is that it forces every piece of this returned metadata to conform to your specified Python model.

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