How to integrate Lettr MCP with LlamaIndex

This guide walks you through connecting Lettr to LlamaIndex using the Composio tool router. By the end, you'll have a working Lettr agent that can create email templates, add your contacts, create audience segments, create your lists, and set up webhooks through natural language commands. This guide will help you understand how to give your LlamaIndex agent real control over a Lettr account through Composio's Lettr MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Lettr is an email and messaging platform for transactional delivery, templates, audiences, campaigns, domains, and webhooks. Use it to send reliable product emails and manage customer messaging from one place.

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Introduction

This guide walks you through connecting Lettr to LlamaIndex using the Composio tool router. By the end, you'll have a working Lettr agent that can create email templates, add your contacts, create audience segments, create your lists, and set up webhooks through natural language commands.

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

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

Also integrate Lettr with

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKLangChainVercel AI SDKMastra AICrewAI

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 Lettr
  • Connect LlamaIndex to the Lettr MCP server
  • Build a Lettr-powered agent using LlamaIndex
  • Interact with Lettr 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 Lettr MCP server, and what's possible with it?

The Lettr MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Lettr account. It provides structured and secure access to your audiences, templates, campaigns, sending domains, webhooks, and email delivery records, so your agent can organize contacts, create templates, configure sending resources, monitor campaigns, and track emails on your behalf.

  • Audience and contact management: Have your agent create, find, and delete contacts, organize them into lists, and manage their list memberships.
  • Segments, properties, and topics: Let the agent define contact properties, build dynamic audience segments, and manage public or private subscription topics.
  • Email template management: Direct your agent to create and publish HTML or editor-based templates, inspect required merge tags, retrieve template content, or remove old templates.
  • Campaign and delivery monitoring: Instruct your agent to review campaigns, delivery and engagement statistics, campaign events, sent email status, and scheduled email activity, or cancel an email before processing begins.
  • Domains and webhook configuration: Have your agent register sending domains, check DNS and verification status, and create or remove event webhooks with supported authentication settings.

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

What is Composio SDK?

Composio's Composio SDK 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 Composio SDK

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

How the Composio SDK works

The Composio SDK 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

Step by step10 STEPS
1

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 Lettr account and project
  • Basic familiarity with async Python/Typescript
2

Getting API Keys for OpenAI, Composio, and Lettr

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
3

Installing dependencies

npm install @composio/llamaindex @llamaindex/openai @llamaindex/tools @llamaindex/workflow dotenv

Create a new Typescript project and install the necessary dependencies:

  • @composio/llamaindex: Composio's LlamaIndex integration
  • @llamaindex/openai: OpenAI LLM integration
  • @llamaindex/tools: MCP client for LlamaIndex
  • @llamaindex/workflow: Workflow framework for LlamaIndex
  • dotenv: Environment variable management
4

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 Lettr access
5

Import modules

import "dotenv/config";
import readline from "node:readline/promises";
import { stdin as input, stdout as output } from "node:process";

import { Composio } from "@composio/core";

import { mcp } from "@llamaindex/tools";
import { agent as createAgent } from "@llamaindex/workflow";
import { openai } from "@llamaindex/openai";

dotenv.config();

Create a new file called lettr_llamaindex_agent.ts and import the required modules:

Key imports:

  • dotenv.config loads .env at runtime
  • readline gives us a simple CLI chat loop
  • Composio is the main Composio SDK client
  • mcp connects to an MCP endpoint
  • createAgent builds a LlamaIndex agent
  • openai configures the LLM backend
6

Load environment variables and initialize Composio

const OPENAI_API_KEY = process.env.OPENAI_API_KEY;
const COMPOSIO_API_KEY = process.env.COMPOSIO_API_KEY;
const COMPOSIO_USER_ID = process.env.COMPOSIO_USER_ID;

if (!OPENAI_API_KEY) throw new Error("OPENAI_API_KEY is not set");
if (!COMPOSIO_API_KEY) throw new Error("COMPOSIO_API_KEY is not set");
if (!COMPOSIO_USER_ID) throw new Error("COMPOSIO_USER_ID is not set");

What's happening:

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

7

Create a Tool Router session and build the agent function

async function buildAgent() {

  console.log(`Initializing Composio client...${COMPOSIO_USER_ID!}...`);
  console.log(`COMPOSIO_USER_ID: ${COMPOSIO_USER_ID!}...`);

  const composio = new Composio({
    apiKey: COMPOSIO_API_KEY,
    provider: new LlamaindexProvider(),
  });

  const session = await composio.create(
    COMPOSIO_USER_ID!,
    {
      toolkits: ["lettr"],
    },
  );

  const mcpUrl = session.mcp.url;
  console.log(`Composio Tool Router MCP URL: ${mcpUrl}`);

  const server = mcp({
    url: mcpUrl,
    clientName: "composio_tool_router_with_llamaindex",
    requestInit: {
      headers: {
        "x-api-key": COMPOSIO_API_KEY!,
      },
    },
    // verbose: true,
  });

  const tools = await server.tools();

  const llm = openai({ apiKey: OPENAI_API_KEY, model: "gpt-5" });

  const agent = createAgent({
    name: "composio_tool_router_with_llamaindex",
        description : "An agent that uses Composio Tool Router MCP tools to perform actions.",
    systemPrompt:
      "You are a helpful assistant connected to Composio Tool Router."+
"Use the available tools to answer user queries and perform Lettr actions." ,
    llm,
    tools,
  });

  return agent;
}

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, lettr)
  • 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 Lettr tools.
  • The MCP tools are mapped to LlamaIndex-compatible tools and plug them into the Agent.
8

Create an interactive chat loop

async function chatLoop(agent: ReturnType<typeof createAgent>) {
  const rl = readline.createInterface({ input, output });

  console.log("Type 'quit' or 'exit' to stop.");

  while (true) {
    let userInput: string;

    try {
      userInput = (await rl.question("\nYou: ")).trim();
    } catch {
      console.log("\nAgent: Bye!");
      break;
    }

    if (!userInput) {
      continue;
    }

    const lower = userInput.toLowerCase();
    if (lower === "quit" || lower === "exit") {
      console.log("Agent: Bye!");
      break;
    }

    try {
      process.stdout.write("Agent: ");

      const stream = agent.runStream(userInput);
      let finalResult: any = null;

      for await (const event of stream) {
        // The event.data contains the streamed content
        const data: any = event.data;

        // Check for streaming delta content
        if (data?.delta) {
          process.stdout.write(data.delta);
        }

        // Store final result for fallback
        if (data?.result || data?.message) {
          finalResult = data;
        }
      }

      // If no streaming happened, show the final result
      if (finalResult) {
        const answer =
          finalResult.result ??
          finalResult.message?.content ??
          finalResult.message ??
          "";
        if (answer && typeof answer === "string" && !answer.includes("[object")) {
          process.stdout.write(answer);
        }
      }

      console.log(); // New line after streaming completes
    } catch (err: any) {
      console.error("\nAgent error:", err?.message ?? err);
    }
  }

  rl.close();
}

What's happening:

  • We're creating a direct terminal interface to chat with Lettr
  • The LLM's responses are streamed to the CLI for faster interaction.
  • The agent uses context to maintain conversation history
  • The agent processes the request, selects appropriate Lettr tools, and returns a result
  • We extract the answer from the result data structure and display it to the user
  • You can type 'quit' or 'exit' to stop the chat loop gracefully
  • Agent responses and any errors are streamed in a clear, readable format
9

Define the main entry point

async function main() {
  try {
    const agent = await buildAgent();
    await chatLoop(agent);
  } catch (err) {
    console.error("Failed to start agent:", err);
    process.exit(1);
  }
}

main();

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 Lettr
10

Run the agent

npx ts-node llamaindex-agent.ts

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

Complete Code

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

import "dotenv/config";
import readline from "node:readline/promises";
import { stdin as input, stdout as output } from "node:process";

import { Composio } from "@composio/core";
import { LlamaindexProvider } from "@composio/llamaindex";

import { mcp } from "@llamaindex/tools";
import { agent as createAgent } from "@llamaindex/workflow";
import { openai } from "@llamaindex/openai";

dotenv.config();

const OPENAI_API_KEY = process.env.OPENAI_API_KEY;
const COMPOSIO_API_KEY = process.env.COMPOSIO_API_KEY;
const COMPOSIO_USER_ID = process.env.COMPOSIO_USER_ID;

if (!OPENAI_API_KEY) {
    throw new Error("OPENAI_API_KEY is not set in the environment");
  }
if (!COMPOSIO_API_KEY) {
    throw new Error("COMPOSIO_API_KEY is not set in the environment");
  }
if (!COMPOSIO_USER_ID) {
    throw new Error("COMPOSIO_USER_ID is not set in the environment");
  }

async function buildAgent() {

  console.log(`Initializing Composio client...${COMPOSIO_USER_ID!}...`);
  console.log(`COMPOSIO_USER_ID: ${COMPOSIO_USER_ID!}...`);

  const composio = new Composio({
    apiKey: COMPOSIO_API_KEY,
    provider: new LlamaindexProvider(),
  });

  const session = await composio.create(
    COMPOSIO_USER_ID!,
    {
      toolkits: ["lettr"],
    },
  );

  const mcpUrl = session.mcp.url;
  console.log(`Composio Tool Router MCP URL: ${mcpUrl}`);

  const server = mcp({
    url: mcpUrl,
    clientName: "composio_tool_router_with_llamaindex",
    requestInit: {
      headers: {
        "x-api-key": COMPOSIO_API_KEY!,
      },
    },
    // verbose: true,
  });

  const tools = await server.tools();

  const llm = openai({ apiKey: OPENAI_API_KEY, model: "gpt-5" });

  const agent = createAgent({
    name: "composio_tool_router_with_llamaindex",
    description:
      "An agent that uses Composio Tool Router MCP tools to perform actions.",
    systemPrompt:
      "You are a helpful assistant connected to Composio Tool Router."+
"Use the available tools to answer user queries and perform Lettr actions." ,
    llm,
    tools,
  });

  return agent;
}

async function chatLoop(agent: ReturnType<typeof createAgent>) {
  const rl = readline.createInterface({ input, output });

  console.log("Type 'quit' or 'exit' to stop.");

  while (true) {
    let userInput: string;

    try {
      userInput = (await rl.question("\nYou: ")).trim();
    } catch {
      console.log("\nAgent: Bye!");
      break;
    }

    if (!userInput) {
      continue;
    }

    const lower = userInput.toLowerCase();
    if (lower === "quit" || lower === "exit") {
      console.log("Agent: Bye!");
      break;
    }

    try {
      process.stdout.write("Agent: ");

      const stream = agent.runStream(userInput);
      let finalResult: any = null;

      for await (const event of stream) {
        // The event.data contains the streamed content
        const data: any = event.data;

        // Check for streaming delta content
        if (data?.delta) {
          process.stdout.write(data.delta);
        }

        // Store final result for fallback
        if (data?.result || data?.message) {
          finalResult = data;
        }
      }

      // If no streaming happened, show the final result
      if (finalResult) {
        const answer =
          finalResult.result ??
          finalResult.message?.content ??
          finalResult.message ??
          "";
        if (answer && typeof answer === "string" && !answer.includes("[object")) {
          process.stdout.write(answer);
        }
      }

      console.log(); // New line after streaming completes
    } catch (err: any) {
      console.error("\nAgent error:", err?.message ?? err);
    }
  }

  rl.close();
}

async function main() {
  try {
    const agent = await buildAgent();
    await chatLoop(agent);
  } catch (err: any) {
    console.error("Failed to start agent:", err?.message ?? err);
    process.exit(1);
  }
}

main();

Conclusion

You've successfully connected Lettr to LlamaIndex through Composio's Tool Router MCP layer. Key takeaways:
  • Tool Router dynamically exposes Lettr 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.
TOOLS

Supported Tools

Every Lettr action and event your agent gets out of the box.

Attach Contacts to Lists

Attach every combination of 1-1000 contacts and 1-50 audience lists, processing up to 50,000 contact-list pairs and reporting new and existing memberships.

Bulk Create Audience Contacts

Create or recognize up to 1000 normalized, deduplicated contacts.

Cancel Scheduled Email

Cancel a scheduled email before its delivery time; cancellation is unavailable once processing has begun.

Create Audience Contact

Create one audience contact with optional properties and list membership.

Create Audience Contact With Double Opt-In

Create an unverified audience contact and irreversibly send an external confirmation email; the contact becomes subscribed only after confirming.

Create Audience List

Create a uniquely named audience list for grouping contacts.

Create Audience Property

Define a custom contact property with an immutable name and value type.

Create Audience Segment

Create a dynamic audience segment whose condition groups are ANDed while conditions inside each group are ORed.

Create Audience Topic

Create a public or private subscription topic with an immutable opt-in or opt-out default.

Create Domain

Register a sending domain and return the DNS configuration needed to verify it.

Create Template

Create and publish an email template from either custom HTML or Topol editor JSON, extracting its merge tags.

Create Webhook

Create an active event webhook using no authentication, HTTP Basic credentials, or OAuth 2 client credentials.

Delete Audience Contact

Permanently delete one audience contact and its associated memberships.

Delete Audience Lists

Permanently delete between 1 and 50 audience lists in one request.

Delete Audience Property

Permanently delete a custom contact-property definition.

Delete Audience Segment

Permanently delete one dynamic audience segment.

Delete Audience Topic

Permanently delete one audience subscription topic.

Delete Domain

Permanently remove a sending domain from Lettr and SparkPost so it can no longer send email.

Delete Template

Permanently delete an email template and all of its versions.

Delete Webhook

Permanently remove a webhook so Lettr stops delivering events to it.

Detach Contacts from Lists

Detach every combination of 1-1000 contacts and 1-50 audience lists, reporting removed and absent memberships.

Get Audience Contact

Retrieve one contact's email, status, custom properties, list memberships, and topic subscriptions by ID.

Get Audience List

Retrieve one audience list and its current contact count by ID.

Get Audience Property

Retrieve one custom contact-property definition and its fallback value.

Get Audience Segment

Retrieve one dynamic segment's list restriction, complete AND-of-OR condition definition, and cached contact count.

Get Audience Topic

Retrieve one subscription topic's defaults, visibility, and current contact count.

Get Campaign

Retrieve one campaign's name, subject, sender and reply-to configuration, schedule, rendered HTML, lifecycle state, recipient counts, and engagement statistics.

Get Domain

Retrieve one sending domain's send readiness, DNS records, provider detection, and verification status.

Get Email

Retrieve a sent email's delivery state, recipients, schedule metadata, and events by request ID.

Get Scheduled Email

Retrieve a scheduled email's current state and events by transmission ID, including after it has been processed.

Get Template

Retrieve an email template's metadata, active HTML or editor JSON content, and version counts by slug.

Get Template Merge Tags

Return required and optional merge tags for a template's active or specified version before sending with substitution data.

Get Webhook

Retrieve one webhook's target, enabled state, event selection, authentication mode, and latest delivery status.

List Audience Contacts

Return one cursor-controlled page of contacts, optionally filtered by search text, status, list, or segment.

List Audience Lists

Return one cursor-controlled page of audience lists with each list's contact count.

List Audience Properties

Return one cursor-controlled page of custom contact-property definitions and fallback values.

List Audience Segments

Return one cursor-controlled page of dynamic audience segments, including their complete AND-of-OR condition definitions, optionally filtered by list.

List Audience Topics

Return one cursor-controlled page of subscription topics with defaults, visibility, and contact counts.

List Campaign Events

Return one cursor-controlled page of a campaign's delivery and engagement events, with event, recipient, and time filters.

List Campaigns

Return one cursor-controlled page of campaigns with complete delivery and engagement statistics, optionally filtered by lifecycle status.

List Domains

Return all sending domains with approval, DNS verification, and send-readiness status.

List Email Events

Return one cursor-controlled page of delivery and engagement events across sent emails, with event, recipient, transmission, bounce, and date filters.

List Sent Emails

Return one cursor-controlled page of sent emails, optionally filtered by recipient and date range.

List Projects

Return one cursor-controlled page of Lettr projects and their IDs for use with template operations.

List Templates

Return one cursor-controlled page of email templates for a project or the team's default project.

List Webhooks

Return all event webhooks with target, enabled state, event selection, authentication mode, and latest delivery status.

Publish Template Version

Create and activate a new version of an existing email template from exactly one of HTML or Topol editor JSON, optionally renaming the template atomically.

Schedule Campaign

Schedule a campaign for future delivery or reschedule it.

Schedule Email

Create a transactional email scheduled 5 minutes to 3 days in the future; it remains cancellable until processed.

Send Campaign

Immediately and irreversibly start sending a sendable draft campaign to its resolved audience.

Send Email

Immediately queue a transactional email to up to 50 recipients using inline content or a Lettr template.

Unschedule Campaign

Cancel a scheduled campaign send and return the campaign to draft.

Update Audience Contact

Change a contact's email, subscribed or unsubscribed status, or selected custom property values.

Update Audience List

Rename an audience list while preserving its contacts.

Update Audience Property

Set or clear a custom contact property's fallback value.

Update Audience Segment

Change a dynamic segment's name, optional audience-list restriction, or complete AND-of-OR condition definition.

Update Audience Topic

Change a subscription topic's name, description, or public/private visibility.

Update Template

Rename an email template's metadata without publishing a new version.

Update Webhook

Partially update a webhook's name, callback URL, authentication, selected events, or active state.

Verify Domain

Run Lettr's DNS verification for a registered domain and return DKIM, CNAME, DMARC, SPF, and ownership results.

FRAMEWORKS

How to build Lettr MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Lettr MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Lettr MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Lettr MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Lettr MCP with Claude Cowork

Codex logo

Codex

Use Lettr MCP with Codex

OpenClaw logo

OpenClaw

Use Lettr MCP with OpenClaw

Hermes logo

Hermes

Use Lettr MCP with Hermes

CLI logo

CLI

Use Lettr MCP with CLI

Google ADK logo

Google ADK

Use Lettr MCP with Google ADK

LangChain logo

LangChain

Use Lettr MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Lettr MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Lettr MCP with Mastra AI

CrewAI logo

CrewAI

Use Lettr MCP with CrewAI

FAQ

Frequently asked questions

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

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 Lettr tools.

Yes, absolutely. You can configure which Lettr 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.

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 Lettr data and credentials are handled as safely as possible.

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