How to Use the Wati Alternative MCP in Pydantic AI

Q: How do I handle Wati Alternative messaging using Pydantic AI?

You call sendtemplatemessage. Because the tool's output is validated by Pydantic, you can trust that the template message was constructed with exactly the data types it needed.

Q: What if I need to check available chatbots with Wati Alternative and Pydantic AI?

Use getchatbots to pull a list of existing bots. The validation layer ensures you only work with the data fields that actually exist in your current chatbot setup.

Q: Can I assign an operator after getting contact details using Wati Alternative and Pydantic AI?

Yes. First, run getcontacts to validate the user record. Then, you can reliably pass that clean data into assignoperator to hand off the conversation.

Q: Does the MCP Server support managing contacts with Wati Alternative and Pydantic AI?

Yes. The getcontacts tool pulls contact lists from Wati, providing reliable access to customer data that your agent can validate against defined schemas.

Manage WhatsApp comms with Pydantic AI. Get contacts, assign operators, and send templates directly from your type-safe Python agent workflows.

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Connect Wati Alternative MCP to Pydantic AI

Create your Vinkius account to connect Wati Alternative 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 Alternative with Pydantic AI

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Send Pre-Approved Messages

Need to update a client? Use `send_template_message` to dispatch pre-approved WhatsApp messages. Since the response is validated via Pydantic, you know exactly what data left your agent and where it went. This makes sure that when your agent pushes out information—like reminders or updates—the structure of the message payload is correct every single time.

Route Chats to Experts

When a chat needs specialized attention, call `assign_operator`. This tool routes incoming conversations to an expert agent. The type-safe nature means you can confidently pass the required operator ID. It's critical for structured support workflows. Your agent doesn’t just track the issue; it reliably directs it to the correct resource.

Retrieve Contact Data

Want to know who you're talking to? `get_contacts` pulls a list of contacts from Wati. This allows your agent to validate user data against expected models before making any decision. This prevents bad inputs and ensures that every conversational step starts with verified, clean customer records.

Setup guide

Set up Wati Alternative 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-1-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Wati Alternative MCP in Pydantic AI

You call `send_template_message`. Because the tool's output is validated by Pydantic, you can trust that the template message was constructed with exactly the data types it needed.

Use `get_chatbots` to pull a list of existing bots. The validation layer ensures you only work with the data fields that actually exist in your current chatbot setup.

Yes. First, run `get_contacts` to validate the user record. Then, you can reliably pass that clean data into `assign_operator` to hand off the conversation.

Yes. The `get_contacts` tool pulls contact lists from Wati, providing reliable access to customer data that your agent can validate against defined schemas.

This server handles structured contact information and WhatsApp communication history. The agent processes fields like user IDs, contact names, and message content.

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