How to Use the ZoomInfo MCP in Pydantic AI

Q: Does the Pydantic AI handle complex lookups like news articles?

Yes, you can use getcompanynews. Because the response must conform to a Python model, you're guaranteed that the dates are date objects and the summaries are strings. It’s predictable.

Guarantee data integrity with Pydantic AI. Trust ZoomInfo results using type-safe Python SDKs.

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Connect ZoomInfo MCP to Pydantic AI

Create your Vinkius account to connect ZoomInfo 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 ZoomInfo with Pydantic AI

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Search Leads With ZoomInfo MCP Server

The `search_companies` tool fetches company data, but the real value is that Pydantic validates every field returned. You don't get unexpected JSON structure; you get a guaranteed Python object. Similarly, when searching for contacts with `search_contacts`, the framework ensures the output adheres strictly to your defined schema.

Enrich Data Using ZoomInfo MCP Server

Need to fill out missing data? Run `enrich_company` or `enrich_contact`. If the underlying API returns a field that doesn't match your expected type (say, an integer where you need a string), the agent fails loudly. That's how safe this is. It prevents silent corruption and ensures that everything fed into your downstream models is perfectly structured.

Get Market Intelligence With ZoomInfo MCP Server

Using `search_intent` lets you gather market signals, but Pydantic makes sure those signals are correctly typed before your agent acts on them. No guesswork necessary. This focus on correctness means you can build complex, multi-step processes where data integrity is non-negotiable.

Setup guide

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

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

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

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Common questions about ZoomInfo MCP in Pydantic AI

You pass the MCP Toolset to your agent. The moment the tool runs—whether it's `enrich_company` or anything else—Pydantic intercepts the response, validating every piece of data before your code sees it.

Your agent won't break quietly. Pydantic catches validation errors immediately and throws a specific exception telling you exactly which field failed the type check. It's impossible to hallucinate fields.

You are working with Company records, Contact records, and Intent Signals. The strength of this setup is that it treats each of these distinct data sources as strongly typed objects.

Yes, you can use `get_company_news`. Because the response must conform to a Python model, you're guaranteed that the dates are date objects and the summaries are strings. It’s predictable.

The server touches Company records and Contact records. These types are critical for any B2B intelligence gathering process, and Pydantic ensures they're always clean.

Use it with your favorite AI tools

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