How to Use the Trengo MCP in Pydantic AI

Q: What data types does Trengo touch when using Pydantic AI?

It handles message content (via listmessages) and team membership lists (listteammembers). The strong typing makes sure these complex structures are always parsed correctly.

Q: Can I list all support tickets using Trengo and Pydantic AI?

Absolutely. You use the listtickets tool, and because of Pydantic validation, you can trust that every ticket record returned has the required fields (like ID and status).

Q: What if I need to send a message using Trengo and Pydantic AI?

You call sendmessage. The framework validates all parameters before execution, ensuring you don't try to send a message with an invalid recipient ID or format.

Q: How do I verify webhook details using Trengo and Pydantic AI?

You use listwebhooks to retrieve the configured webhooks. Pydantic validation confirms that every resulting object—including the URL schema and event types—is correctly structured.

Ensure flawless data handling with Pydantic AI on the Trengo MCP Server.

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Connect Trengo MCP to Pydantic AI

Create your Vinkius account to connect Trengo 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 Trengo with Pydantic AI

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Structured ticket and conversation management

The agent can retrieve specific support threads using `get_ticket` or fetch a full list of tickets via `list_tickets`. Since every response is validated, you'll know the data structure for status updates (`update_ticket`) is always correct. This prevents silent failures; if the API returns bad ticket data, your agent fails loudly, protecting your process integrity.

Reliable communication actions via MCP Server

You can send messages using `send_message`, and the framework guarantees that any required inputs (like recipient IDs) conform to expected types. Furthermore, listing contacts with `list_contacts` yields predictable JSON objects. If you need to track external changes, calling `create_webhook` ensures the resulting webhook object is strictly validated.

Account visibility and resource listing

The agent can get basic account info using `get_account_profile` or list available channels with `list_channels`. These tools return predictable data, which Pydantic validates at runtime. It also lets you manage system resources by calling `list_webhooks` and retrieving the full team roster via `list_team_members`, guaranteeing a consistent structure.

Setup guide

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

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

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

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Common questions about Trengo MCP in Pydantic AI

The agent calls tools on the Trengo MCP Server, but every single response is validated against your defined Pydantic models. This means you get guaranteed correct data types back.

It handles message content (via `list_messages`) and team membership lists (`list_team_members`). The strong typing makes sure these complex structures are always parsed correctly.

Absolutely. You use the `list_tickets` tool, and because of Pydantic validation, you can trust that every ticket record returned has the required fields (like ID and status).

You call `send_message`. The framework validates all parameters before execution, ensuring you don't try to send a message with an invalid recipient ID or format.

You use `list_webhooks` to retrieve the configured webhooks. Pydantic validation confirms that every resulting object—including the URL schema and event types—is correctly structured.

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