How to Use the Wati MCP in Pydantic AI

Q: How do I verify Wati connectivity with Pydantic AI?

You call checkwatistatus. The agent confirms that the connection is live and ready to handle messaging. This step provides a clean, binary check before any complex workflow runs.

Q: What tools are available for Wati with Pydantic AI?

The MCP Server exposes contact management (addcontact, getcontact), message sending (three types), and listing tools. They give your Pydantic AI agent full operational control.

Q: Does the MCP Server handle bulk messaging for Wati with Pydantic AI?

Yes, you can use listbroadcasts to prepare a large set of recipients. The subsequent message sends are then validated by the agent's type-safety layer.

Q: What happens if I try to send bad data? Wati with Pydantic AI

The agent fails loud and clear. If sendsessionmessage receives malformed input, the validation catches it immediately, preventing any unexpected or corrupted API calls.

Q: What specific data does the MCP Server touch regarding privacy? Wati with Pydantic AI

The server manages contact attributes. When you use updatecontact, you are directly modifying user profile information, which is treated as sensitive data.

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Connect Wati MCP to Pydantic AI

Create your Vinkius account to connect Wati 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 Wati with Pydantic AI

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Maintain Data Integrity using MCP Server

The agent relies on the `list_contacts` tool to gather all current user records. It then uses `get_contact` and `update_contact` to ensure any changes are structured correctly, preventing data corruption. Because of Pydantic AI’s validation layer, if the API returns a contact record that doesn't match expected fields, the agent fails immediately with an error—no silent bugs.

Reliable Messaging for Pydantic AI

To send a message, the agent must first use `get_template` to validate the structure. It then calls `send_template_message`, guaranteeing that all required fields are present before execution. This approach means you don't just *hope* the API works; Pydantic AI validates the entire communication payload against strict models.

Track and List Everything with MCP Server

The agent can list all active message templates using `list_templates` before calling `send_template_message`. It also tracks every interaction by listing historical records via `list_messages`. This disciplined approach lets you build a verifiable, correct messaging pipeline without guessing what the underlying API returns.

Setup guide

Set up Wati 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": {
        "wati-alternative-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Wati MCP in Pydantic AI

You call `check_wati_status`. The agent confirms that the connection is live and ready to handle messaging. This step provides a clean, binary check before any complex workflow runs.

The MCP Server exposes contact management (`add_contact`, `get_contact`), message sending (three types), and listing tools. They give your Pydantic AI agent full operational control.

Yes, you can use `list_broadcasts` to prepare a large set of recipients. The subsequent message sends are then validated by the agent's type-safety layer.

The agent fails loud and clear. If `send_session_message` receives malformed input, the validation catches it immediately, preventing any unexpected or corrupted API calls.

The server manages contact attributes. When you use `update_contact`, you are directly modifying user profile information, which is treated as sensitive data.

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