How to Use the Chorus.ai (ZoomInfo) MCP in Pydantic AI

Strict type validation for Chorus.ai (ZoomInfo) tools in Pydantic AI.

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Connect Chorus.ai (ZoomInfo) MCP to Pydantic AI

Create your Vinkius account to connect Chorus.ai (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 Chorus.ai (ZoomInfo) with Pydantic AI

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Type-safe meetings for Pydantic AI

Use `list_chorus_meetings` to pull call lists that Pydantic AI validates at runtime. If the API returns junk, your agent catches the error immediately. This keeps your agent's decision-making logic clean and predictable. You define the model, and the server fills it with verified data.

Validate transcripts in Pydantic AI

Invoke `get_meeting_transcript` to pull full text into your agent's workflow. Pydantic AI ensures the returned string matches your expected schema before processing starts. This prevents runtime crashes when dealing with long-form conversation data. You get reliable input every single time.

Query competitors via MCP Server

The `list_tracked_competitors` tool provides a structured list of market rivals. This MCP Server ensures your agent gets a clean list of competitors to analyze. It integrates perfectly with your existing Pydantic models. Your agent logic stays robust because the data format is guaranteed.

Setup guide

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

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

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

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Common questions about Chorus.ai (ZoomInfo) MCP in Pydantic AI

You get runtime validation for every piece of meeting data. If the server response doesn't match your schema, the agent stops before executing logic.

Yes, you map the tool output to your Pydantic models. It forces the server data to conform to your specific requirements.

The framework is model-agnostic. You connect the server, define your models, and use any LLM you prefer.

You provide the Vinkius endpoint token during initialization. The server handles the session validation behind the scenes.

Your call transcripts are processed within your local memory after validation. The server acts as a strictly defined pipe, ensuring no unauthorized data leaks occur.

Use it with your favorite AI tools

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