How to Use the BuiltWith Tech Lookup MCP in Pydantic AI

Use BuiltWith Tech Lookup with Pydantic AI to validate every piece of web technology data against strict type models.

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Connect BuiltWith Tech Lookup MCP to Pydantic AI

Create your Vinkius account to connect BuiltWith Tech Lookup 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 BuiltWith Tech Lookup with Pydantic AI

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Typed domain lookups for Pydantic AI

Run `lookup_domain_tech` to pull structured data into your agent. Pydantic AI validates the response against your models, catching any schema mismatches immediately. You simply initialize the MCPToolset and add it to your agent. It forces the agent to handle data exactly as your code expects.

Extract keyword profiles

Call `get_domain_keywords` to analyze site content patterns. The framework treats the server response as a validated object, preventing silent errors during execution. This approach works with any model. Whether you use local LLMs or cloud providers, the type safety remains consistent across all calls.

Access domain lists by tech

Use `get_tech_domain_lists` to aggregate domains using specific software. Pydantic AI ensures the returned list structure is correct before the agent processes it. The server runs externally while your agent maintains strict control. It is the safest way to pull large sets of web intelligence.

Setup guide

Set up BuiltWith Tech Lookup 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": {
        "builtwith-tech-lookup-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent BuiltWith Tech Lookup transactions")
print(result.output)

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Common questions about BuiltWith Tech Lookup MCP in Pydantic AI

Pydantic AI raises a validation error if the server returns data that doesn't match your models. This stops the agent from proceeding with corrupted inputs.

Yes, the toolset is model-agnostic. Pydantic AI handles the validation of the server response regardless of which LLM you use to generate the queries.

You just need to import the MCPToolset and pass it to your agent instance. It simplifies the connection while keeping your code type-safe.

The server runs in an ephemeral, isolated environment. Your agent session is short-lived and does not share state with other users or services.

We use scoped endpoint tokens to manage access. Your agent only receives the technology records requested, and all traffic is encrypted to prevent unauthorized interception.

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