How to Use the WhatsApp Chat Export Parser MCP in Pydantic AI

Q: How do I use WhatsApp Chat Export Parser with Pydantic AI?

You pass the toolset to your Agent. The agent uses parsewhatsappchat, and because it's using Pydantic validation, you get an immediate error if the resulting JSON doesn't match your required schema.

Type-Safe Chat Analysis with Pydantic AI

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Connect WhatsApp Chat Export Parser MCP to Pydantic AI

Create your Vinkius account to connect WhatsApp Chat Export Parser 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 WhatsApp Chat Export Parser with Pydantic AI

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The `parse_whatsapp_chat` tool converts messy `.txt` exports into JSON that strictly adheres to your defined Pydantic schema. If the API output is malformed, the agent fails loudly—you know instantly when data integrity is compromised. No silent corruption. This type safety means you trust the underlying chat data completely.

Validating Communication Details

You can define types for every piece of extracted data: a sender must be a string, a timestamp must be an ISO date format. The server enforces this when it runs `parse_whatsapp_chat`. This is critical if you're building mission-critical agents that depend on clean, predictable fields.

Handling Chat History Reliability

The agent doesn't just assume the chat data is right; it validates the output. When you process years of conversations, knowing the structure of every message and timestamp means your analysis is trustworthy. This reliability makes it ideal for high-stakes applications where correctness matters more than raw speed.

Setup guide

Set up WhatsApp Chat Export Parser 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": {
        "whatsapp-chat-export-parser-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent WhatsApp Chat Export Parser transactions")
print(result.output)

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Common questions about WhatsApp Chat Export Parser MCP in Pydantic AI

You pass the toolset to your Agent. The agent uses `parse_whatsapp_chat`, and because it's using Pydantic validation, you get an immediate error if the resulting JSON doesn't match your required schema.

Yes. Since every piece of data—messages, dates, senders—is validated against a strict type model, the parser maintains high integrity even when handling massive volumes of historical chats.

It provides messages, senders, timestamps, and participant statistics. These discrete elements are ideal because they map perfectly to strongly typed fields in a Pydantic model.

Yes. The toolset is model-agnostic. It handles the parsing and output structuring, allowing your agent to run that validated data against OpenAI, Gemini, or any other local model.

This server touches personal communication data, including messages, senders, timestamps, and participant statistics found within the chat export file.

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