How to Use the WSLA (WhatsApp) MCP in Pydantic AI

Q: How do I manage WhatsApp templates with WSLA (WhatsApp) and the Pydantic AI framework?

You use listwhatsapptemplates to check what's available. Then, you pass the necessary fields into sendwhatsapptemplate. Since everything is type-validated, your agent knows exactly what data structure WhatsApp expects.

Q: Can WSLA (WhatsApp) send text messages using the Pydantic AI framework?

Yes. The sendwhatsapptext tool handles this directly. Your agent sends a plain string, and Pydantic ensures that the final payload structure is correct before sending it out.

Q: What if I want my agent to react to a message using WSLA (WhatsApp) and the Pydantic AI framework?

You call sendwhatsappreaction. The value here is that even minor actions are subjected to runtime validation. It stops incorrect data from ever reaching the WhatsApp API.

Q: Does this MCP Server support sending media details with WSLA (WhatsApp) and the Pydantic AI framework?

It does. getwhatsappmediadetails provides the metadata, and because of your schema definition, you get back clean, predictable data structures every single time.

Guarantee correct WhatsApp messaging with the Pydantic AI framework.

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Connect WSLA (WhatsApp) MCP to Pydantic AI

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Setup WSLA (WhatsApp) with Pydantic AI

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Sending templates using MCP Server validation.

Your agent validates template usage by first calling `list_whatsapp_templates` to confirm availability. Then, it executes `send_whatsapp_template`. Because of Pydantic's checks, you never risk sending a malformed or non-existent message.

Handling media and text with type safety.

Need details on an image? Use `get_whatsapp_media_details`, knowing the output will match your defined schema. For simple messages, `send_whatsapp_text` provides a straightforward way to send pure text, validated at runtime.

Sending reactions with guaranteed structure.

Use `send_whatsapp_reaction` when you need the agent to perform an action. The key here is that Pydantic validates the parameters for this tool call, preventing silent failures and ensuring data correctness.

Setup guide

Set up WSLA (WhatsApp) 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": {
        "wsla-whatsapp-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about WSLA (WhatsApp) MCP in Pydantic AI

You use `list_whatsapp_templates` to check what's available. Then, you pass the necessary fields into `send_whatsapp_template`. Since everything is type-validated, your agent knows exactly what data structure WhatsApp expects.

Yes. The `send_whatsapp_text` tool handles this directly. Your agent sends a plain string, and Pydantic ensures that the final payload structure is correct before sending it out.

You call `send_whatsapp_reaction`. The value here is that even minor actions are subjected to runtime validation. It stops incorrect data from ever reaching the WhatsApp API.

It does. `get_whatsapp_media_details` provides the metadata, and because of your schema definition, you get back clean, predictable data structures every single time.

This server accesses WhatsApp message content. The specific data types are the media identifiers and text strings used in templates or direct messages; Pydantic ensures these fields are handled correctly.

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