How to Use the Mailjet MCP in LlamaIndex

Q: How do I configure Mailjet with LlamaIndex using MCP?

Run pip install llama-index-tools-mcp. Set up a BasicMCPClient, wrap it with McpToolSpec, and await totoollistasync() to feed the tools into your FunctionAgent.

Q: Can LlamaIndex trigger Mailjet emails directly?

Yes. Once your agent decides on a response, it can call sendplainemail to deliver the message. It handles both the RAG retrieval and the outbound action.

Q: Can LlamaIndex check my Mailjet sending domains?

Your agent can run listsenderdomains to verify DKIM and SPF status. It indexes that configuration data so you can query your infrastructure health anytime.

Index your Mailjet campaign data directly into LlamaIndex to build searchable email knowledge bases.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Mailjet MCP to LlamaIndex

Create your Vinkius account to connect Mailjet 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.

GDPR Free for Subscribers

Setup Mailjet with LlamaIndex

Ask AI about this MCP

ChatGPT

Claude

Perplexity

RAG for Campaign Analytics via MCP Server

You don't have to guess what subject lines worked last quarter. By connecting this MCP Server, LlamaIndex pulls raw data using `list_sent_campaigns` and `get_campaign_performance`. It embeds those metrics straight into your vector store. Now your knowledge base understands your actual email history. When you ask your RAG application why Q3 engagement dropped, it queries the indexed analytics alongside your marketing docs. The answers are grounded in real API responses, not guesses.

Semantic Search for Templates

Finding the right HTML template across dozens of iterations gets messy. Your LlamaIndex agent can run `list_email_templates` and pull the actual markup using `get_template_details`. It indexes the content of every single template. You just ask your agent to find the template with the blue call-to-action button. It searches the semantic index, retrieves the exact template ID, and can even trigger `send_template_email` with the right variables attached.

Unified Audience Context

Customer support interactions require context. LlamaIndex can pull a user's status via `list_all_contacts` and see which segments they belong to using `list_contact_groups`. This data merges with their support ticket history in your index. When your agent drafts a reply, it knows exactly what marketing emails the user already received. If they need to be added to a new onboarding sequence, the agent executes `subscribe_to_list` right from the chat interface.

Setup guide

Set up Mailjet 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 Mailjet 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 Mailjet tools.",
)
response = await agent.run("List recent Mailjet data")

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Mailjet. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Mailjet now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Mailjet MCP in LlamaIndex

Run `pip install llama-index-tools-mcp`. Set up a `BasicMCPClient`, wrap it with `McpToolSpec`, and await `to_tool_list_async()` to feed the tools into your `FunctionAgent`.

Yes. Once your agent decides on a response, it can call `send_plain_email` to deliver the message. It handles both the RAG retrieval and the outbound action.

You control the indexing schedule. Run an async job that calls `get_account_analytics` daily to keep your vector store updated with fresh engagement numbers.

Your agent can run `list_sender_domains` to verify DKIM and SPF status. It indexes that configuration data so you can query your infrastructure health anytime.

The integration only reads the specific contact records and group IDs you request. The MCP protocol ensures your API keys stay out of the LLM context window, passing only the necessary execution tokens over an ephemeral connection.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript