Can I trigger an API campaign for a specific user?

Yes! Use the `trigger_campaign` tool with the Campaign ID and a JSON array of recipient objects (e.g., `[{"external_user_id":"123"}]`).

How do I update a user's custom attribute?

Simply ask the agent to `track_user` and provide the External ID along with the new attributes in JSON format (e.g., `{"favorite_color":"blue"}`).

Does the integration allow permanently deleting a user for GDPR compliance?

Yes. Use the `delete_user` mutation with the user's External ID. This will permanently remove their profile and data from your Braze dashboard.

Braze MCP Server for Pydantic AI 10 tools — connect in under 2 minutes

Name: Braze
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 10 Tools SDK

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

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

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 Braze "
            "(10 tools)."
        ),
    )

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

asyncio.run(main())

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Braze MCP Server

Connect your Braze customer engagement platform to any AI agent and orchestrate your marketing automation and user tracking workflows through natural conversation.

Pydantic AI validates every Braze tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through the 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

User Orchestration — Track new user attributes and events, identify anonymous users, or permanently delete user profiles for compliance.
Campaign Management — List all your marketing campaigns, retrieve detailed metadata, and instantly trigger API-based campaign sends to specific users.
Canvas (Journey) Control — List and inspect multi-step Canvases, and trigger users to enter specific Canvas workflows.
Data Export — Programmatically export user profile data by their external IDs.

The Braze MCP Server exposes 10 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.

How to Connect Braze to Pydantic AI via MCP

Follow these steps to integrate the Braze MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

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

Explore tools

The agent discovers 10 tools from Braze with type-safe schemas

Why Use Pydantic AI with the Braze MCP Server

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

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

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

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

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

Braze + Pydantic AI Use Cases

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

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

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

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

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

Braze MCP Tools for Pydantic AI (10)

These 10 tools become available when you connect Braze to Pydantic AI via MCP:

delete_user

Delete a user by external ID

export_user_ids

Export profile data for specific users

get_campaign_details

Get details of a specific campaign

get_canvas_details

Get details of a specific Canvas

identify_user

Identify a user (merge alias to external ID)

list_campaigns

List all campaigns

list_canvases

List all Canvases

track_user

Track user attributes or events

trigger_campaign

Trigger an API-triggered campaign

trigger_canvas

Trigger a Canvas journey

Example Prompts for Braze in Pydantic AI

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

"List all active campaigns in Braze."

"Track user 'usr_992' with attribute {'loyalty_tier':'Gold'}."

"List all Canvases configured in the workspace."

Troubleshooting Braze MCP Server with Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Braze + Pydantic AI FAQ

Common questions about integrating Braze MCP Server with Pydantic AI.

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.

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.

Can I switch LLM providers without changing MCP code?

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