# How to integrate Intelliprint MCP with LlamaIndex

```json
{
  "title": "How to integrate Intelliprint MCP with LlamaIndex",
  "toolkit": "Intelliprint",
  "toolkit_slug": "intelliprint",
  "framework": "LlamaIndex",
  "framework_slug": "llama-index",
  "url": "https://composio.dev/toolkits/intelliprint/framework/llama-index",
  "markdown_url": "https://composio.dev/toolkits/intelliprint/framework/llama-index.md",
  "updated_at": "2026-05-12T10:16:04.022Z"
}
```

## Introduction

This guide walks you through connecting Intelliprint to LlamaIndex using the Composio tool router. By the end, you'll have a working Intelliprint agent that can create and mail a letter using template, list all print jobs from last week, cancel a pending print job by id through natural language commands.
This guide will help you understand how to give your LlamaIndex agent real control over a Intelliprint account through Composio's Intelliprint MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Intelliprint with

- [OpenAI Agents SDK](https://composio.dev/toolkits/intelliprint/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/intelliprint/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/intelliprint/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/intelliprint/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/intelliprint/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/intelliprint/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/intelliprint/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/intelliprint/framework/cli)
- [Google ADK](https://composio.dev/toolkits/intelliprint/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/intelliprint/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/intelliprint/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/intelliprint/framework/mastra-ai)
- [CrewAI](https://composio.dev/toolkits/intelliprint/framework/crew-ai)

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

The Intelliprint MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Intelliprint account. It provides structured and secure access to your hybrid mail and print workflow, so your agent can automate print jobs, manage backgrounds and templates, merge documents, and track mailing activities on your behalf.
- Automated print job creation and management: Instantly submit new print jobs, confirm submissions, or cancel and delete print jobs with full tracking and status updates.
- Background and template organization: Create, update, retrieve, and list print backgrounds and templates to keep your mailouts branded and personalized.
- File merging for streamlined print workflows: Merge multiple files into a single document before sending them to print, optimizing document preparation.
- Print job tracking and detailed retrieval: Retrieve comprehensive details of any print job by its ID, allowing for real-time status checks and reporting.
- Bulk management and filtering: List all backgrounds, templates, or print jobs with advanced filtering and pagination to efficiently handle high-volume mailing operations.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `INTELLIPRINT_CANCEL_DELETE_PRINT_JOB` | Cancel or Delete Print Job | Cancel or delete an existing print job by its ID. Unconfirmed jobs are deleted immediately; confirmed jobs (not yet shipped) are cancelled with a refund. Important: Testmode print jobs cannot be cancelled - only live print jobs support cancellation. |
| `INTELLIPRINT_CREATE_BACKGROUND` | Create background | Create a new Background by uploading a PDF file. Backgrounds are used as base layers for print jobs (e.g., letterheads, invoice templates). Use this tool when you need to upload a new background PDF that can be applied to print jobs. |
| `INTELLIPRINT_CREATE_MAILING_LIST` | Create Mailing List | Create a mailing list to store thousands of recipients and send mail items to all of them at once. Use this tool when you need to create a new mailing list for bulk mailings. Recipients can be added in bulk during creation or added later. Mailing lists support dynamic fields (variables) for personalizing templates per recipient. |
| `INTELLIPRINT_CREATE_MAILING_LIST_RECIPIENT` | Create Mailing List Recipient | Tool to create a new recipient within a mailing list. Use when you need to add an individual contact who will receive mail campaigns. Recipients must have at minimum an address line; name, postcode, and country are optional. |
| `INTELLIPRINT_CREATE_PRINT_JOB` | Create Print Job | Create a new print job in Intelliprint by uploading a document. Use this tool to submit documents for postal mailing. Jobs can be created as drafts (confirmed=False) for review/editing, or confirmed immediately (confirmed=True) for printing and dispatch. Test mode (testmode=True) allows testing without charges or actual mailing. |
| `INTELLIPRINT_DELETE_BACKGROUND` | Delete Background | Tool to delete a background by ID. Use when you need to remove a background that is no longer needed. Fails if any print job has used this background in the last 90 days. |
| `INTELLIPRINT_DELETE_MAILING_LIST` | Delete Mailing List | Tool to delete a mailing list along with all the recipients in the mailing list. Use when you need to remove a mailing list permanently. |
| `INTELLIPRINT_DELETE_MAILING_LIST_RECIPIENT` | Delete Mailing List Recipient | Delete a recipient by ID from a specified mailing list. Use when you need to remove a recipient from a mailing list. |
| `INTELLIPRINT_GET_MAILING_LIST` | Get Mailing List | Tool to retrieve a single mailing list by its ID. Use when you need details about a specific mailing list including recipients count, address validation status, and dynamic variables. |
| `INTELLIPRINT_GET_MAILING_LIST_RECIPIENT` | Get Mailing List Recipient | Tool to retrieve a single recipient by ID within a specified mailing list. Use when you need details about a specific mailing list recipient. |
| `INTELLIPRINT_LIST_BACKGROUNDS` | List Backgrounds | Tool to list backgrounds with optional filtering and pagination. Use after uploading or managing backgrounds to retrieve current entries. Example: 'List backgrounds for team=team_1234 with limit=50 and skip=10'. |
| `INTELLIPRINT_LIST_MAILING_LIST_RECIPIENTS` | List Mailing List Recipients | Tool to list all recipients in a mailing list with pagination and sorting options. Use when you need to retrieve recipients from a specific mailing list. |
| `INTELLIPRINT_LIST_MAILING_LISTS` | List Mailing Lists | Tool to list all available mailing lists with pagination and sorting options. Use when you need to retrieve mailing lists for print jobs or to view existing lists. |
| `INTELLIPRINT_LIST_PRINT_JOBS` | List Print Jobs | Tool to list print jobs with optional filters and pagination. Use after confirming API key. |
| `INTELLIPRINT_LIST_TEMPLATES` | List Templates | Tool to list all available templates. Use after authenticating API key to browse available templates. |
| `INTELLIPRINT_MERGE_FILES` | Merge Files | Merges multiple PDF, RTF, or Word documents into a single PDF file. Use this tool when you need to: - Combine multiple PDF files into one - Merge Word documents (.doc, .docx) into a single PDF - Combine RTF files with PDFs or Word documents - Create a unified document from separate files The merged output is always a PDF file, regardless of input file types. Files are merged in the order they are provided in the request. A download link is returned that is valid for approximately 1 hour. |
| `INTELLIPRINT_RETRIEVE_BACKGROUND` | Retrieve Background | Tool to retrieve a specific Background by ID. Use when you need background details before further processing. |
| `INTELLIPRINT_RETRIEVE_PRINT_JOB` | Retrieve Print Job | Tool to retrieve details of a Print Job by its ID. Use when you have a valid print_id and need full job specifications. |
| `INTELLIPRINT_UPDATE_BACKGROUND` | Update Background | Tool to update an existing Background's name or team. Use when you need to change a background after creation. Example: 'Update background bg_123abc to new name'. |
| `INTELLIPRINT_UPDATE_MAILING_LIST` | Update Mailing List | Tool to update a mailing list. Use when you need to update the name, add or replace recipients in bulk, or configure address validation. Can add recipients to existing list or replace all recipients by setting delete_old_recipients to True. |
| `INTELLIPRINT_UPDATE_MAILING_LIST_RECIPIENT` | Update Mailing List Recipient | Tool to update an existing recipient in a mailing list by ID. Use when you need to modify recipient address details or custom variables for personalized mailings. |

## Supported Triggers

None listed.

## Creating MCP Server - Stand-alone vs Composio SDK

The Intelliprint MCP server is an implementation of the Model Context Protocol that connects your AI agent to Intelliprint. It provides structured and secure access so your agent can perform Intelliprint operations on your behalf through a secure, permission-based interface.
With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster.

## Step-by-step Guide

### 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 Intelliprint account and project
- Basic familiarity with async Python/Typescript

### 1. Getting API Keys for OpenAI, Composio, and Intelliprint

No description provided.

### 2. Installing dependencies

No description provided.
```python
pip install composio-llamaindex llama-index llama-index-llms-openai llama-index-tools-mcp python-dotenv
```

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

### 3. Set environment variables

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

### 4. Import modules

No description provided.
```python
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()
```

```typescript
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();
```

### 5. Load environment variables and initialize Composio

No description provided.
```python
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")
```

```typescript
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");
```

### 6. Create a Tool Router session and build the agent function

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, intelliprint)
- 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 Intelliprint tools.
- The MCP tools are mapped to LlamaIndex-compatible tools and plug them into the Agent.
```python
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=["intelliprint"],
    )

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

```typescript
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: ["intelliprint"],
    },
  );

  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 Intelliprint actions." ,
    llm,
    tools,
  });

  return agent;
}
```

### 7. Create an interactive chat loop

No description provided.
```python
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}")
```

```typescript
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();
}
```

### 8. Define the main entry point

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 Intelliprint
```python
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!")
```

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

main();
```

### 9. Run the agent

When prompted, authenticate and authorise your agent with Intelliprint, then start asking questions.
```bash
python llamaindex_agent.py
```

```typescript
npx ts-node llamaindex-agent.ts
```

## Complete Code

```python
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=["intelliprint"],
    )

    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 Intelliprint actions."
    system_prompt = """
    You are a helpful assistant connected to Composio Tool Router.
    Use the available tools to answer user queries and perform Intelliprint 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!")
```

```typescript
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: ["intelliprint"],
    },
  );

  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 Intelliprint 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 Intelliprint to LlamaIndex through Composio's Tool Router MCP layer.
Key takeaways:
- Tool Router dynamically exposes Intelliprint 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 Intelliprint MCP Agent with another framework

- [OpenAI Agents SDK](https://composio.dev/toolkits/intelliprint/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/intelliprint/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/intelliprint/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/intelliprint/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/intelliprint/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/intelliprint/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/intelliprint/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/intelliprint/framework/cli)
- [Google ADK](https://composio.dev/toolkits/intelliprint/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/intelliprint/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/intelliprint/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/intelliprint/framework/mastra-ai)
- [CrewAI](https://composio.dev/toolkits/intelliprint/framework/crew-ai)

## Related Toolkits

- [Google Drive](https://composio.dev/toolkits/googledrive) - Google Drive is a cloud storage platform for uploading, sharing, and collaborating on files. It's perfect for keeping your documents accessible and organized across devices.
- [Google Docs](https://composio.dev/toolkits/googledocs) - Google Docs is a cloud-based word processor that enables document creation and real-time collaboration. Its seamless sharing and version history make team editing and content management a breeze.
- [Google Super](https://composio.dev/toolkits/googlesuper) - Google Super is an all-in-one suite combining Gmail, Drive, Calendar, Sheets, Analytics, and more. It gives you a unified platform to manage your digital life, boosting productivity and organization.
- [Affinda](https://composio.dev/toolkits/affinda) - Affinda is an AI-powered document processing platform that automates data extraction from resumes, invoices, and more. It streamlines document-heavy workflows by turning files into structured, actionable data.
- [Agility cms](https://composio.dev/toolkits/agility_cms) - Agility CMS is a headless content management system for building and managing digital experiences across platforms. It lets teams update content quickly and deliver omnichannel experiences with ease.
- [Algodocs](https://composio.dev/toolkits/algodocs) - Algodocs is an AI-powered platform that automates data extraction from business documents. It delivers fast, secure, and accurate processing without templates or manual training.
- [Api2pdf](https://composio.dev/toolkits/api2pdf) - Api2Pdf is a REST API for generating PDFs from HTML, URLs, and documents using powerful engines like wkhtmltopdf and Headless Chrome. It streamlines document conversion and automation for developers and businesses.
- [Aryn](https://composio.dev/toolkits/aryn) - Aryn is an AI-powered platform for parsing, extracting, and analyzing data from unstructured documents. Use it to automate document processing and unlock actionable insights from your files.
- [Boldsign](https://composio.dev/toolkits/boldsign) - Boldsign is a digital eSignature platform for sending, signing, and tracking documents online. Organizations use it to automate agreements and manage legally binding workflows efficiently.
- [Boloforms](https://composio.dev/toolkits/boloforms) - BoloForms is an eSignature platform built for small businesses, offering unlimited signatures, templates, and forms. It simplifies digital document signing and team collaboration at a predictable, fixed price.
- [Box](https://composio.dev/toolkits/box) - Box is a cloud content management and file sharing platform for businesses. It helps teams securely store, organize, and collaborate on files from anywhere.
- [Carbone](https://composio.dev/toolkits/carbone) - Carbone is a blazing-fast report generator that turns JSON data into PDFs, Word docs, spreadsheets, and more using flexible templates. It lets you automate document creation at scale with minimal code.
- [Castingwords](https://composio.dev/toolkits/castingwords) - CastingWords is a transcription service specializing in human-powered, accurate transcripts via a simple API. Get seamless audio-to-text conversion for interviews, meetings, podcasts, and more.
- [Cloudconvert](https://composio.dev/toolkits/cloudconvert) - CloudConvert is a powerful file conversion service supporting over 200 file formats. It streamlines converting, compressing, and managing documents, media, and more, all in one place.
- [Cloudlayer](https://composio.dev/toolkits/cloudlayer) - Cloudlayer is a document and asset generation service for creating PDFs and images via API or SDKs. It lets you automate high-quality doc creation, saving dev time and reducing manual work.
- [Cloudpress](https://composio.dev/toolkits/cloudpress) - Cloudpress is a content export tool for Google Docs and Notion. It automates publishing to your favorite Content Management Systems.
- [Contentful graphql](https://composio.dev/toolkits/contentful_graphql) - Contentful graphql is a content delivery API that lets you access Contentful data using GraphQL queries. It gives you efficient, flexible ways to fetch and manage structured content for any digital project.
- [Conversion tools](https://composio.dev/toolkits/conversion_tools) - Conversion Tools is an online service for converting documents between formats such as PDF, Word, Excel, XML, and CSV. It lets you automate complex document workflows with just a few clicks.
- [Convertapi](https://composio.dev/toolkits/convertapi) - ConvertAPI is a robust file conversion service for documents, images, and spreadsheets. It streamlines programmatic format changes and lets developers automate complex workflows with a single API.
- [Craftmypdf](https://composio.dev/toolkits/craftmypdf) - CraftMyPDF is a web-based service for designing and generating PDFs with templates and live data. It streamlines document creation by automating personalized PDFs at scale.

## Frequently Asked Questions

### What are the differences in Tool Router MCP and Intelliprint MCP?

With a standalone Intelliprint MCP server, the agents and LLMs can only access a fixed set of Intelliprint tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Intelliprint 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 Intelliprint tools.

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

Yes, absolutely. You can configure which Intelliprint 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 Intelliprint data and credentials are handled as safely as possible.

---
[See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)