How to Use the Zenvia Conversion MCP in Pydantic AI

Q: Can I reliably send messages using Zenvia Conversion and Pydantic AI?

Yes. The sendmessage tool is fully supported, and because of the type-safe framework, you'll know the message payload structure is correct every time the agent runs it.

Q: Is listing prospects reliable with Zenvia Conversion and Pydantic AI?

It's extremely reliable. When you call listprospects, the resulting list is immediately validated against your defined schema, so you get structured data every single time.

Use Zenvia Conversion with Pydantic AI for type-safe, validated lead management.

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Connect Zenvia Conversion MCP to Pydantic AI

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Setup Zenvia Conversion with Pydantic AI

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Creating and Validating Leads

The `create_lead` tool lets you build new leads. Because this MCP Server is used with Pydantic AI, the agent will validate that all contact details and initial messages conform to your specified schema before calling the API. You can verify existing records using `get_prospect`. If the underlying Zenvia Conversion data structure changes unexpectedly, the agent fails loudly, telling you exactly where it went wrong.

Sending Messages and Tracking Records

To communicate, use the `send_message` tool. The type-safe nature of Pydantic AI means that when your agent sends a message or uses `send_template`, it's guaranteed to pass correctly structured data. The system also tracks history via `list_interactions`. You can complement this by calling `get_account` for overall context, all validated against Python models.

Listing and Organizing Data

The agent needs to know what it's working with. Use `list_prospects` to get a list of contacts, or use `list_labels` to see available categories. Every tool call, from listing groups (`list_groups`) to getting account info (`get_account`), results in Pydantic-validated output. This focus on correctness means you don't have to worry about silent data corruption.

Setup guide

Set up Zenvia Conversion 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": {
        "zenvia-conversion-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Zenvia Conversion MCP in Pydantic AI

Every single response from this MCP Server is validated against your Pydantic models at runtime. This guarantees that if the API returns unexpected data, your agent stops and throws a clear validation error.

Yes. The `send_message` tool is fully supported, and because of the type-safe framework, you'll know the message payload structure is correct every time the agent runs it.

This server handles contact details and prospect information. All of these core records are validated by your Pydantic models before the agent processes them.

It's extremely reliable. When you call `list_prospects`, the resulting list is immediately validated against your defined schema, so you get structured data every single time.

The framework handles it by wrapping all tool calls. Even when calling several functions—like `create_lead` followed by `list_interactions`—the output of each step is checked for data integrity.

Use it with your favorite AI tools

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