How to integrate Mailtrap MCP with LlamaIndex

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Introduction

This guide walks you through connecting Mailtrap to LlamaIndex using the Composio tool router. By the end, you'll have a working Mailtrap agent that can send a test email to marketing team, list all emails sent from mailtrap today, create a new inbox for transactional testing through natural language commands.

This guide will help you understand how to give your LlamaIndex agent real control over a Mailtrap account through Composio's Mailtrap MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

TL;DR

Here's what you'll learn:
  • Set your OpenAI and Composio API keys
  • Install LlamaIndex and Composio packages
  • Create a Composio Tool Router session for Mailtrap
  • Connect LlamaIndex to the Mailtrap MCP server
  • Build a Mailtrap-powered agent using LlamaIndex
  • Interact with Mailtrap through natural language

What is LlamaIndex?

LlamaIndex is a data framework for building LLM applications. It provides tools for connecting LLMs to external data sources and services through agents and tools.

Key features include:

  • ReAct Agent: Reasoning and acting pattern for tool-using agents
  • MCP Tools: Native support for Model Context Protocol
  • Context Management: Maintain conversation context across interactions
  • Async Support: Built for async/await patterns

What is the Mailtrap MCP server, and what's possible with it?

The Mailtrap MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Mailtrap account. It provides structured and secure access so your agent can perform Mailtrap operations on your behalf.

Supported Tools & Triggers

Tools
Clean InboxTool to clean an inbox in Mailtrap by deleting all messages.
Create ContactTool to create a new contact in Mailtrap.
Create Contact EventTool to create a contact event in Mailtrap.
Create Contact ExportTool to create a contact export job for a Mailtrap account.
Create Contact FieldTool to create a custom contact field in Mailtrap.
Create Contact ListTool to create a new contact list in Mailtrap.
Create Email TemplateTool to create a new email template in Mailtrap account.
Create Sending DomainTool to create a new sending domain in Mailtrap.
Delete ContactTool to delete a contact from a Mailtrap account.
Delete Contact FieldTool to delete a contact field by its ID.
Delete Contact ListTool to delete a contact list by its ID.
Delete Email TemplateTool to delete an email template from a Mailtrap account.
Delete ProjectTool to delete a project from Mailtrap.
Delete Sending DomainTool to delete a sending domain from a Mailtrap account.
Get Billing UsageTool to retrieve current billing cycle usage for an account.
Get ContactTool to retrieve a contact by UUID or email address from Mailtrap.
Get Contact ExportTool to retrieve the status of a contact export.
Get Contact FieldTool to retrieve contact field details by field ID.
Get Contact Import StatusTool to retrieve the status of a contact import operation.
Get Contact ListTool to retrieve a specific contact list by its ID.
Get Email TemplateTool to retrieve details of a specific email template by ID.
Get Inbox AttributesTool to retrieve inbox attributes from Mailtrap.
Get Message HTML BodyTool to retrieve the HTML body of a message from Mailtrap.
Get Permission ResourcesTool to retrieve all resources in account for permission management.
Get Project by IDTool to retrieve project details from Mailtrap by project ID.
Get Sending DomainTool to retrieve sending domain details from Mailtrap.
Get Sending StatsTool to retrieve email sending statistics from Mailtrap for a specific account.
Get Sending Stats by CategoriesTool to retrieve email sending statistics grouped by categories.
Get Sending Stats by DateTool to retrieve email sending statistics aggregated by date.
Get Sending Stats by DomainsTool to retrieve sending statistics grouped by domains for a Mailtrap account.
Get Sending Stats by ESPTool to retrieve email sending statistics grouped by email service providers (ESPs) for a specified date range.
Import ContactsTool to import contacts in bulk to Mailtrap.
List AccountsTool to list all Mailtrap accounts you have access to.
List Contact FieldsTool to get all contact fields for a Mailtrap account.
List Contact ListsTool to retrieve all contact lists for a Mailtrap account.
List Email TemplatesTool to retrieve all email templates for a Mailtrap account.
List InboxesTool to get a list of inboxes for a Mailtrap account.
List Messages in InboxTool to get messages from a Mailtrap inbox.
List ProjectsTool to get a list of projects for a Mailtrap account.
List Sending DomainsTool to list all sending domains for a Mailtrap account.
List Email SuppressionsTool to list suppressed email addresses for a Mailtrap account.
Mark Inbox as ReadTool to mark all messages in a Mailtrap inbox as read.
Reset Inbox CredentialsTool to reset SMTP credentials for a Mailtrap inbox.
Update contactTool to update an existing contact in Mailtrap.
Update Contact FieldTool to update a contact field in Mailtrap.
Update Contact ListTool to update a contact list's name in Mailtrap.
Update Email TemplateTool to update an existing email template in Mailtrap account.
Update inboxTool to update an inbox's settings in Mailtrap.
Update projectTool to update a project's name in Mailtrap.

What is the Composio tool router, and how does it fit here?

What is Tool Router?

Composio's Tool Router helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Tool Router

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Tool Router works

The Tool Router follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Prerequisites

Before you begin, make sure you have:
  • Python 3.8/Node 16 or higher installed
  • A Composio account with the API key
  • An OpenAI API key
  • A Mailtrap account and project
  • Basic familiarity with async Python/Typescript

Getting API Keys for OpenAI, Composio, and Mailtrap

OpenAI API key (OPENAI_API_KEY)
  • Go to the OpenAI dashboard
  • Create an API key if you don't have one
  • Assign it to OPENAI_API_KEY in .env
Composio API key and user ID
  • Log into the Composio dashboard
  • Copy your API key from Settings
    • Use this as COMPOSIO_API_KEY
  • Pick a stable user identifier (email or ID)
    • Use this as COMPOSIO_USER_ID

Installing dependencies

pip install composio-llamaindex llama-index llama-index-llms-openai llama-index-tools-mcp python-dotenv

Create a new Python project and install the necessary dependencies:

  • composio-llamaindex: Composio's LlamaIndex integration
  • llama-index: Core LlamaIndex framework
  • llama-index-llms-openai: OpenAI LLM integration
  • llama-index-tools-mcp: MCP client for LlamaIndex
  • python-dotenv: Environment variable management

Set environment variables

bash
OPENAI_API_KEY=your-openai-api-key
COMPOSIO_API_KEY=your-composio-api-key
COMPOSIO_USER_ID=your-user-id

Create a .env file in your project root:

These credentials will be used to:

  • Authenticate with OpenAI's GPT-5 model
  • Connect to Composio's Tool Router
  • Identify your Composio user session for Mailtrap access

Import modules

import asyncio
import os
import dotenv

from composio import Composio
from composio_llamaindex import LlamaIndexProvider
from llama_index.core.agent.workflow import ReActAgent
from llama_index.core.workflow import Context
from llama_index.llms.openai import OpenAI
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec

dotenv.load_dotenv()

Create a new file called mailtrap_llamaindex_agent.py and import the required modules:

Key imports:

  • asyncio: For async/await support
  • Composio: Main client for Composio services
  • LlamaIndexProvider: Adapts Composio tools for LlamaIndex
  • ReActAgent: LlamaIndex's reasoning and action agent
  • BasicMCPClient: Connects to MCP endpoints
  • McpToolSpec: Converts MCP tools to LlamaIndex format

Load environment variables and initialize Composio

OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not OPENAI_API_KEY:
    raise ValueError("OPENAI_API_KEY is not set in the environment")
if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set in the environment")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set in the environment")

What's happening:

This ensures missing credentials cause early, clear errors before the agent attempts to initialise.

Create a Tool Router session and build the agent function

async def build_agent() -> ReActAgent:
    composio_client = Composio(
        api_key=COMPOSIO_API_KEY,
        provider=LlamaIndexProvider(),
    )

    session = composio_client.create(
        user_id=COMPOSIO_USER_ID,
        toolkits=["mailtrap"],
    )

    mcp_url = session.mcp.url
    print(f"Composio MCP URL: {mcp_url}")

    mcp_client = BasicMCPClient(mcp_url, headers={"x-api-key": COMPOSIO_API_KEY})
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    llm = OpenAI(model="gpt-5")

    description = "An agent that uses Composio Tool Router MCP tools to perform Mailtrap actions."
    system_prompt = """
    You are a helpful assistant connected to Composio Tool Router.
    Use the available tools to answer user queries and perform Mailtrap actions.
    """
    return ReActAgent(tools=tools, llm=llm, description=description, system_prompt=system_prompt, verbose=True)

What's happening here:

  • We create a Composio client using your API key and configure it with the LlamaIndex provider
  • We then create a tool router MCP session for your user, specifying the toolkits we want to use (in this case, mailtrap)
  • The session returns an MCP HTTP endpoint URL that acts as a gateway to all your configured tools
  • LlamaIndex will connect to this endpoint to dynamically discover and use the available Mailtrap tools.
  • The MCP tools are mapped to LlamaIndex-compatible tools and plug them into the Agent.

Create an interactive chat loop

async def chat_loop(agent: ReActAgent) -> None:
    ctx = Context(agent)
    print("Type 'quit', 'exit', or Ctrl+C to stop.")

    while True:
        try:
            user_input = input("\nYou: ").strip()
        except (KeyboardInterrupt, EOFError):
            print("\nBye!")
            break

        if not user_input or user_input.lower() in {"quit", "exit"}:
            print("Bye!")
            break

        try:
            print("Agent: ", end="", flush=True)
            handler = agent.run(user_input, ctx=ctx)

            async for event in handler.stream_events():
                # Stream token-by-token from LLM responses
                if hasattr(event, "delta") and event.delta:
                    print(event.delta, end="", flush=True)
                # Show tool calls as they happen
                elif hasattr(event, "tool_name"):
                    print(f"\n[Using tool: {event.tool_name}]", flush=True)

            # Get final response
            response = await handler
            print()  # Newline after streaming
        except KeyboardInterrupt:
            print("\n[Interrupted]")
            continue
        except Exception as e:
            print(f"\nError: {e}")

What's happening here:

  • We're creating a direct terminal interface to chat with your Mailtrap database
  • The LLM's responses are streamed to the CLI for faster interaction.
  • The agent uses context to maintain conversation history
  • You can type 'quit' or 'exit' to stop the chat loop gracefully
  • Agent responses and any errors are displayed in a clear, readable format

Define the main entry point

async def main() -> None:
    agent = await build_agent()
    await chat_loop(agent)

if __name__ == "__main__":
    # Handle Ctrl+C gracefully
    signal.signal(signal.SIGINT, lambda s, f: (print("\nBye!"), exit(0)))
    try:
        asyncio.run(main())
    except KeyboardInterrupt:
        print("\nBye!")

What's happening here:

  • We're orchestrating the entire application flow
  • The agent gets built with proper error handling
  • Then we kick off the interactive chat loop so you can start talking to Mailtrap

Run the agent

npx ts-node llamaindex-agent.ts

When prompted, authenticate and authorise your agent with Mailtrap, then start asking questions.

Complete Code

Here's the complete code to get you started with Mailtrap and LlamaIndex:

import asyncio
import os
import signal
import dotenv

from composio import Composio
from composio_llamaindex import LlamaIndexProvider
from llama_index.core.agent.workflow import ReActAgent
from llama_index.core.workflow import Context
from llama_index.llms.openai import OpenAI
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec

dotenv.load_dotenv()

OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not OPENAI_API_KEY:
    raise ValueError("OPENAI_API_KEY is not set")
if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set")

async def build_agent() -> ReActAgent:
    composio_client = Composio(
        api_key=COMPOSIO_API_KEY,
        provider=LlamaIndexProvider(),
    )

    session = composio_client.create(
        user_id=COMPOSIO_USER_ID,
        toolkits=["mailtrap"],
    )

    mcp_url = session.mcp.url
    print(f"Composio MCP URL: {mcp_url}")

    mcp_client = BasicMCPClient(mcp_url, headers={"x-api-key": COMPOSIO_API_KEY})
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    llm = OpenAI(model="gpt-5")
    description = "An agent that uses Composio Tool Router MCP tools to perform Mailtrap actions."
    system_prompt = """
    You are a helpful assistant connected to Composio Tool Router.
    Use the available tools to answer user queries and perform Mailtrap actions.
    """
    return ReActAgent(
        tools=tools,
        llm=llm,
        description=description,
        system_prompt=system_prompt,
        verbose=True,
    );

async def chat_loop(agent: ReActAgent) -> None:
    ctx = Context(agent)
    print("Type 'quit', 'exit', or Ctrl+C to stop.")

    while True:
        try:
            user_input = input("\nYou: ").strip()
        except (KeyboardInterrupt, EOFError):
            print("\nBye!")
            break

        if not user_input or user_input.lower() in {"quit", "exit"}:
            print("Bye!")
            break

        try:
            print("Agent: ", end="", flush=True)
            handler = agent.run(user_input, ctx=ctx)

            async for event in handler.stream_events():
                # Stream token-by-token from LLM responses
                if hasattr(event, "delta") and event.delta:
                    print(event.delta, end="", flush=True)
                # Show tool calls as they happen
                elif hasattr(event, "tool_name"):
                    print(f"\n[Using tool: {event.tool_name}]", flush=True)

            # Get final response
            response = await handler
            print()  # Newline after streaming
        except KeyboardInterrupt:
            print("\n[Interrupted]")
            continue
        except Exception as e:
            print(f"\nError: {e}")

async def main() -> None:
    agent = await build_agent()
    await chat_loop(agent)

if __name__ == "__main__":
    # Handle Ctrl+C gracefully
    signal.signal(signal.SIGINT, lambda s, f: (print("\nBye!"), exit(0)))
    try:
        asyncio.run(main())
    except KeyboardInterrupt:
        print("\nBye!")

Conclusion

You've successfully connected Mailtrap to LlamaIndex through Composio's Tool Router MCP layer. Key takeaways:
  • Tool Router dynamically exposes Mailtrap tools through an MCP endpoint
  • LlamaIndex's ReActAgent handles reasoning and orchestration; Composio handles integrations
  • The agent becomes more capable without increasing prompt size
  • Async Python provides clean, efficient execution of agent workflows
You can easily extend this to other toolkits like Gmail, Notion, Stripe, GitHub, and more by adding them to the toolkits parameter.

How to build Mailtrap MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Mailtrap MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Mailtrap MCP with Claude Agent SDK

Google ADK

Google ADK

Use Mailtrap MCP with Google ADK

LangChain

LangChain

Use Mailtrap MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Mailtrap MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Mailtrap MCP with Mastra AI

CrewAI

CrewAI

Use Mailtrap MCP with CrewAI

Claude Code

Claude Code

Use Mailtrap MCP with Claude Code

Codex

Codex

Use Mailtrap MCP with Codex

FAQ

What are the differences in Tool Router MCP and Mailtrap MCP?

With a standalone Mailtrap MCP server, the agents and LLMs can only access a fixed set of Mailtrap tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Mailtrap and many other apps based on the task at hand, all through a single MCP endpoint.

Can I use Tool Router MCP with LlamaIndex?

Yes, you can. LlamaIndex fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Mailtrap tools.

Can I manage the permissions and scopes for Mailtrap while using Tool Router?

Yes, absolutely. You can configure which Mailtrap scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

How safe is my data with Composio Tool Router?

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Mailtrap data and credentials are handled as safely as possible.

Used by agents from

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