Braze MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Braze as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Braze. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Braze?"
)
print(response)
asyncio.run(main())
* 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.
LlamaIndex agents combine Braze tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
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 LlamaIndex 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 LlamaIndex via MCP
Follow these steps to integrate the Braze MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
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
Why Use LlamaIndex with the Braze MCP Server
LlamaIndex provides unique advantages when paired with Braze through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Braze tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Braze tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Braze, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Braze tools were called, what data was returned, and how it influenced the final answer
Braze + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Braze MCP Server delivers measurable value.
Hybrid search: combine Braze real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Braze to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Braze for fresh data
Analytical workflows: chain Braze queries with LlamaIndex's data connectors to build multi-source analytical reports
Braze MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Braze to LlamaIndex 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 LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting Braze to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpBraze + LlamaIndex FAQ
Common questions about integrating Braze MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Braze with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Braze to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.