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Wati MCP Server for Pydantic AIGive Pydantic AI instant access to 13 tools to Add Contact, Check Wati Status, Get Contact, and more

Built by Vinkius GDPR 13 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Wati through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Ask AI about this App Connector for Pydantic AI

The Wati app connector for Pydantic AI is a standout in the Communication Messaging category — giving your AI agent 13 tools to work with, ready to go from day one.

Vinkius delivers Streamable HTTP and SSE to any MCP client

python
import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Wati "
            "(13 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Wati?"
    )
    print(result.data)

asyncio.run(main())
Wati
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About Wati MCP Server

Connect your Wati WhatsApp Business account to any AI agent and simplify how you engage with your customers through natural conversation and automated messaging workflows.

Pydantic AI validates every Wati tool response against typed schemas, catching data inconsistencies at build time. Connect 13 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

  • Direct Messaging — Send instant WhatsApp session messages to active contacts for real-time support and engagement.
  • Template Automation — Send pre-approved WhatsApp templates to start conversations or send notifications with dynamic parameters.
  • Contact Management — List and inspect your WhatsApp subscribers and contacts to keep your directory up-to-date.
  • Chat History — Retrieve the complete message history for any specific contact to understand the conversation context.
  • Template Catalog — List all available message templates to identify the best options for your communication strategy.

The Wati MCP Server exposes 13 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

All 13 Wati tools available for Pydantic AI

When Pydantic AI connects to Wati through Vinkius, your AI agent gets direct access to every tool listed below — spanning whatsapp-api, chatbot, customer-engagement, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.

add_contact

Add a contact

check_wati_status

Verify connectivity

get_contact

Get contact details

get_template

Get template details

list_broadcasts

List broadcasts

list_contacts

List contacts

list_messages

List messages

list_tags

List tags

list_templates

List message templates

send_media_message

Send media message

send_session_message

Send a session message

send_template_message

Send a template message

update_contact

Update contact attributes

Connect Wati to Pydantic AI via MCP

Follow these steps to wire Wati into Pydantic AI. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.

01

Install Pydantic AI

Run pip install pydantic-ai
02

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token
03

Run the agent

Save to agent.py and run: python agent.py
04

Explore tools

The agent discovers 13 tools from Wati with type-safe schemas

Why Use Pydantic AI with the Wati MCP Server

Pydantic AI provides unique advantages when paired with Wati through the Model Context Protocol.

01

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

02

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Wati integration code

03

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

04

Dependency injection system cleanly separates your Wati connection logic from agent behavior for testable, maintainable code

Wati + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Wati MCP Server delivers measurable value.

01

Type-safe data pipelines: query Wati with guaranteed response schemas, feeding validated data into downstream processing

02

API orchestration: chain multiple Wati tool calls with Pydantic validation at each step to ensure data integrity end-to-end

03

Production monitoring: build validated alert agents that query Wati and output structured, schema-compliant notifications

04

Testing and QA: use Pydantic AI's dependency injection to mock Wati responses and write comprehensive agent tests

Example Prompts for Wati in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Wati immediately.

01

"List all my WhatsApp templates."

02

"Send the 'order_ready' template to 5511999999999."

03

"Show me the chat history for the number 5511888888888."

Troubleshooting Wati MCP Server with Pydantic AI

Common issues when connecting Wati to Pydantic AI through the Vinkius, and how to resolve them.

01

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Wati + Pydantic AI FAQ

Common questions about integrating Wati MCP Server with Pydantic AI.

01

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.
02

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.
03

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Wati MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.