How to Use the Brevo MCP in LlamaIndex

Q: Can LlamaIndex index live contact data from Brevo?

Yes. You can ingest contact segments by calling listcontacts and parsing the output directly into Document objects for your vector store.

Q: How does LlamaIndex avoid outdated context when querying Brevo lists?

The agent runs getcontactlist to fetch current active members before performing vector searches, ensuring your query engine uses fresh data.

Q: Can I use LlamaIndex to analyze previous Brevo campaign performance?

Yes. Your query engine can fetch historical logs via listemailcampaigns and index them to help write better copy based on past successes.

Index your Brevo campaign data and contact lists directly into LlamaIndex vector stores for ground-truth RAG.

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Connect Brevo MCP to LlamaIndex

Create your Vinkius account to connect Brevo to LlamaIndex 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 Brevo with LlamaIndex

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Build RAG indexes from Brevo campaign histories

Your LlamaIndex agent pulls historical data using `list_email_campaigns` and indexes the raw text into your vector database. It queries this index to ground future generation tasks in actual past performance data. Connecting this MCP Server means your LlamaIndex RAG pipeline can reference real metrics from `get_smtp_statistics` to answer queries about past open rates.

Semantic search over contact lists in LlamaIndex

Index your subscriber database by calling `list_contacts` and storing the profile attributes in a queryable LlamaIndex document store. Your agent uses these documents to find patterns in user engagement without manual filtering. The pipeline reads raw contact data through `get_contact` to update the index in real time. This keeps your semantic search queries grounded in active audience segments.

Context-aware email drafting with LlamaIndex

Draft highly targeted transactional messages by retrieving user context from your LlamaIndex index and passing it to `send_email`. The agent checks `get_account` to verify account limits before generating the draft. This MCP Server setup enables a closed-loop system where your LlamaIndex index determines the exact messaging context and triggers the dispatch in one session.

Setup guide

Set up Brevo MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Brevo MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Brevo tools.",
)
response = await agent.run("List recent Brevo data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Brevo. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Brevo MCP in LlamaIndex

Yes. You can ingest contact segments by calling `list_contacts` and parsing the output directly into Document objects for your vector store.

The agent runs `get_contact_list` to fetch current active members before performing vector searches, ensuring your query engine uses fresh data.

Yes. Your query engine can fetch historical logs via `list_email_campaigns` and index them to help write better copy based on past successes.

Install the MCP tool adapter, initialize the client with your endpoint, and register the tools to your LlamaIndex FunctionAgent.

Your campaign logs and contact details are streamed directly to your LlamaIndex application. Vinkius executes the MCP Server inside ephemeral, zero-trust V8 isolates, meaning we never store or inspect your payloads.

Use it with your favorite AI tools

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