How to Use the Brevo MCP in LlamaIndex

Q: How do I index Brevo contact lists into LlamaIndex?

Use the McpToolSpec from llama-index-tools-mcp to connect to the Vinkius server. Use listcontactfolders to fetch the Brevo data, then parse it into documents to build your LlamaIndex vector index.

Q: Can LlamaIndex update Brevo attributes during a query?

Yes, your LlamaIndex agent can call updatecontactattributes during execution. If a LlamaIndex RAG query reveals new user preferences, the agent writes those updates directly back to the Brevo CRM.

Q: How does LlamaIndex handle Brevo SMTP failures?

The tool sendtransactionalemail returns structured errors if a Brevo domain check fails. Your LlamaIndex agent catches these exceptions and can search its vector index for alternative contact methods.

Q: Can I filter which Brevo tools this MCP Server exposes to LlamaIndex?

Yes, you can use the allowedtools filter in the LlamaIndex MCP client. This lets you restrict the LlamaIndex agent to read-only Brevo tools like getcontactattributes if you want to prevent accidental modifications.

Feed live Brevo CRM data and campaign metrics directly into your LlamaIndex vector indexes for smarter 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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Index Brevo contact data for LlamaIndex RAG

The `list_crm_contacts` tool pulls your entire Brevo contact list and turns it into a searchable index using this MCP Server. Your LlamaIndex agent queries this data to find matching profiles without scanning the entire Brevo database manually. By mapping these records with `get_contact_attributes`, you build a complete vector space of your Brevo customer base. This lets your LlamaIndex RAG pipeline answer complex questions about user segments using real-time CRM data.

Query Brevo account status via LlamaIndex

The `get_smtp_account_details` tool provides raw Brevo account and quota data that your LlamaIndex agent can index. This keeps your LlamaIndex system aware of current limits and active plans during automated runs. When integrated with `list_contact_folders`, the LlamaIndex agent understands how your Brevo campaigns are structured. It uses this context to ground its answers, ensuring it never suggests sending campaigns when Brevo limits are reached.

Trigger Brevo SMS from LlamaIndex insights

The `send_transactional_sms` tool allows your LlamaIndex pipeline to dispatch Brevo text alerts based on RAG query results. When the LlamaIndex index flags a high-priority customer issue, the agent triggers a Brevo SMS to their hardware. It formats the number correctly to bypass Brevo web client blocks. This ensures your urgent LlamaIndex alerts reach the recipient's phone instantly without manual intervention.

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

Use the `McpToolSpec` from `llama-index-tools-mcp` to connect to the Vinkius server. Use `list_contact_folders` to fetch the Brevo data, then parse it into documents to build your LlamaIndex vector index.

Yes, your LlamaIndex agent can call `update_contact_attributes` during execution. If a LlamaIndex RAG query reveals new user preferences, the agent writes those updates directly back to the Brevo CRM.

The tool `send_transactional_email` returns structured errors if a Brevo domain check fails. Your LlamaIndex agent catches these exceptions and can search its vector index for alternative contact methods.

Yes, you can use the `allowed_tools` filter in the LlamaIndex MCP client. This lets you restrict the LlamaIndex agent to read-only Brevo tools like `get_contact_attributes` if you want to prevent accidental modifications.

Your Brevo contact records are processed in memory and never stored on the Vinkius platform. The MCP Server acts as a secure bridge, passing Brevo emails and phone numbers directly to your local LlamaIndex vector store.

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