# How to integrate Pdf co MCP with LlamaIndex

```json
{
  "title": "How to integrate Pdf co MCP with LlamaIndex",
  "toolkit": "Pdf co",
  "toolkit_slug": "pdf_co",
  "framework": "LlamaIndex",
  "framework_slug": "llama-index",
  "url": "https://composio.dev/toolkits/pdf_co/framework/llama-index",
  "markdown_url": "https://composio.dev/toolkits/pdf_co/framework/llama-index.md",
  "updated_at": "2026-05-12T10:21:38.259Z"
}
```

## Introduction

This guide walks you through connecting Pdf co to LlamaIndex using the Composio tool router. By the end, you'll have a working Pdf co agent that can extract invoice data from uploaded pdf file, convert excel spreadsheet at url to json, generate a qr code for a payment link through natural language commands.
This guide will help you understand how to give your LlamaIndex agent real control over a Pdf co account through Composio's Pdf co MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Pdf co with

- [ChatGPT](https://composio.dev/toolkits/pdf_co/framework/chatgpt)
- [OpenAI Agents SDK](https://composio.dev/toolkits/pdf_co/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/pdf_co/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/pdf_co/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/pdf_co/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/pdf_co/framework/codex)
- [Cursor](https://composio.dev/toolkits/pdf_co/framework/cursor)
- [VS Code](https://composio.dev/toolkits/pdf_co/framework/vscode)
- [OpenCode](https://composio.dev/toolkits/pdf_co/framework/opencode)
- [OpenClaw](https://composio.dev/toolkits/pdf_co/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/pdf_co/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/pdf_co/framework/cli)
- [Google ADK](https://composio.dev/toolkits/pdf_co/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/pdf_co/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/pdf_co/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/pdf_co/framework/mastra-ai)
- [CrewAI](https://composio.dev/toolkits/pdf_co/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 Pdf co
- Connect LlamaIndex to the Pdf co MCP server
- Build a Pdf co-powered agent using LlamaIndex
- Interact with Pdf co 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 Pdf co MCP server, and what's possible with it?

The Pdf co MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Pdf co account. It provides structured and secure access to your PDF.co capabilities, so your agent can extract data, generate documents, convert files, process barcodes, and manage asynchronous jobs on your behalf.
- Automated PDF data extraction and parsing: Let your agent extract structured data from PDFs using templates or parse documents for key information—perfect for receipts, invoices, and more.
- PDF creation, splitting, and merging: Generate new PDF files, combine multiple PDFs, or split documents into separate files without manual intervention.
- File format conversion: Seamlessly convert Excel files to CSV, HTML, JSON, text, or XML, enabling efficient data analysis and workflow automation.
- Barcode generation and processing: Instantly create various barcode formats (QR codes, Code128, PDF417, etc.) or encode data into barcodes for labeling and tracking.
- Job management and file uploads: Upload documents to PDF.co, track the status of asynchronous jobs, and retrieve results—all through your agent, hands-free.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `PDF_CO_ACCOUNT_BALANCE_INFO` | Get Account Balance Info | Tool to get account balance info. Use after authenticating to check remaining credits. |
| `PDF_CO_CONVERT_EXCEL_TO_CSV` | Convert Excel to CSV | Tool to convert an Excel file (XLS/XLSX) to CSV. Use when you have a public Excel file URL and need CSV output. Inline option returns data inline; otherwise provides download URL. |
| `PDF_CO_CONVERT_EXCEL_TO_HTML` | Convert Excel to HTML | Tool to convert an Excel file to HTML. Use when you have an Excel URL and need HTML output. |
| `PDF_CO_CONVERT_EXCEL_TO_JSON` | Convert Excel to JSON | Tool to convert an online Excel or CSV file to JSON format. Use when you have a public file URL and need structured data extraction. |
| `PDF_CO_CONVERT_EXCEL_TO_TEXT` | Convert Excel to Text | Tool to convert Excel files to plain text. Use after providing an Excel file URL to extract spreadsheet content. |
| `PDF_CO_CONVERT_EXCEL_TO_XML` | Convert Excel to XML | Tool to convert an Excel file to XML. Use when needing XML output from xls/xlsx/csv synchronously or asynchronously. |
| `PDF_CO_DOCUMENT_PARSER` | Document Parser | Tool to parse documents based on predefined templates to extract structured data. Use when you need to extract structured fields from a PDF by supplying a custom template. |
| `PDF_CO_FILE_UPLOAD` | Upload File | Tool to upload a local file or remote URL to PDF.co, returning a hosted URL for downstream processing. Use when a PDF.co tool (e.g., PDF_CO_PDF_FROM_HTML) requires a remote URL but you have a local file. |
| `PDF_CO_JOB_CHECK` | Check Job Status | Tool to check status and result of an asynchronous job. Use after submitting a job to poll for completion. |
| `PDF_CO_PDF_ADD` | Add Content to PDF | Tool to add content to an existing PDF. Use when you need to overlay text, images, barcodes, or links before distributing the file. |
| `PDF_CO_PDF_CHANGE_TEXT_SEARCHABLE` | Change PDF Text Searchable | Tool to make PDF text searchable using OCR. Use when you need to add a searchable text layer to scanned or image-only PDF documents. |
| `PDF_CO_BARCODE_GENERATE` | Generate Barcode | Tool to generate high quality barcode images in 45+ formats including QR Code, Code 128, Code 39, and more. Use when you need to create barcodes with customization options like rotation, decoration images for QR codes, or async processing. |
| `PDF_CO_PDFCO_POST_FILE_UPLOAD_BASE64` | Upload File from Base64 | Tool to create a temporary file using base64-encoded source data. Use when you need to upload file content as base64 to PDF.co for downstream processing. Temporary files are automatically deleted after 1 hour (or custom expiration time). |
| `PDF_CO_PDF_DELETE_PAGES` | Delete PDF Pages | Tool to delete specific pages from a PDF file. Use when you need to remove unwanted pages before further processing. |
| `PDF_CO_PDF_EXTRACT_ATTACHMENTS` | Extract PDF Attachments | Tool to extract embedded attachments from a PDF. Use when you need to retrieve embedded files from a PDF after uploading. |
| `PDF_CO_PDF_FIND` | Find Text in PDF | Tool to find text in a PDF document. Use when you need to locate keywords or regex patterns and get their page positions. |
| `PDF_CO_PDF_FORMS_INFO_READER` | PDF Forms Info Reader | Tool to extract form field information from a PDF. Use when you need to retrieve names, types, and values of form fields. Returns field names, types (CheckBox, EditBox, RadioButton, ComboBox), values, and position coordinates. |
| `PDF_CO_PDF_FROM_DOCUMENT_TXT` | Convert Text to PDF | Tool to convert a plain text (.txt) file to PDF. Use when you have a public URL to a text file; raw inline text is not accepted by the endpoint. |
| `PDF_CO_PDF_FROM_EMAIL` | Convert Email to PDF | Tool to convert email files (.eml/.msg) to PDF. Use when you need to transform standalone email messages into PDF documents. |
| `PDF_CO_PDF_FROM_HTML` | Convert HTML to PDF | Tool to convert HTML code or webpage URL into a PDF document. Use when you need to capture a webpage or HTML snippet as a PDF file. |
| `PDF_CO_PDF_INFO_READER` | PDF Info Reader | Tool to retrieve detailed information and metadata of a PDF. Use when you need page count, author, encryption details, and other document properties. |
| `PDF_CO_PDF_MERGE` | Merge PDFs | Tool to merge multiple PDF files into one document. Use when you need to combine several PDF URLs into a single PDF file. |
| `PDF_CO_PDF_ROTATE` | Rotate PDF Pages | Tool to rotate selected pages in a PDF. Use when you need to adjust the orientation of specific pages in an online PDF file before further processing. |
| `PDF_CO_PDF_SEARCH_AND_DELETE_TEXT` | Search and Delete Text in PDF | Tool to search for and delete text in a PDF by keyword or regex. Use when you need to remove sensitive or unwanted text from a PDF document. |
| `PDF_CO_PDF_SEARCH_AND_REPLACE_TEXT` | Search and Replace Text in PDF | Tool to search for and replace text in a PDF document. Use when you need to update specific text instances within an existing PDF file (e.g., changing invoice numbers). |
| `PDF_CO_PDF_SPLIT` | Split PDF | Tool to split a PDF into multiple files by page ranges. Use when you need to extract specific pages or page ranges from a PDF. |
| `PDF_CO_PDF_TO_CSV` | Convert PDF to CSV | Tool to convert PDF or scanned images to CSV format. Use when you need to extract tabular data from a PDF into CSV format. |
| `PDF_CO_PDF_TO_HTML` | Convert PDF to HTML | Tool to convert PDF documents to HTML. Use when you need an HTML rendition of a PDF or scanned image. |
| `PDF_CO_PDF_TO_IMAGE` | Convert PDF to Image | Tool to convert PDF pages to images (PNG, JPG, TIFF). Use when you need image previews of PDF content. |
| `PDF_CO_PDF_TO_JSON` | Convert PDF to JSON | Tool to convert PDF or scanned images to JSON format. Use when you need a structured JSON representation of PDF content. |
| `PDF_CO_PDF_TO_TEXT` | Convert PDF to Text | Tool to convert PDF or scanned images to plain text. Use when you need raw text output preserving layout. |
| `PDF_CO_PDF_TO_XLS` | Convert PDF to XLS | Tool to convert PDF or scanned images to XLS format. Use when you need to extract tabular data into an Excel spreadsheet. |
| `PDF_CO_PDF_TO_XLSX` | Convert PDF to XLSX | Tool to convert PDF or scanned images to XLSX (Excel) format. Use when you need structured spreadsheet output from a PDF. |
| `PDF_CO_PDF_TO_XML` | Convert PDF to XML | Tool to convert PDF or scanned images to XML format. Use when you need to extract structured data from PDF into XML. |

## Supported Triggers

None listed.

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

The Pdf co MCP server is an implementation of the Model Context Protocol that connects your AI agent to Pdf co. It provides structured and secure access so your agent can perform Pdf co 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 Pdf co account and project
- Basic familiarity with async Python/Typescript

### 1. Getting API Keys for OpenAI, Composio, and Pdf co

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 Pdf co 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, pdf co)
- 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 Pdf co 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=["pdf_co"],
    )

    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 Pdf co actions."
    system_prompt = """
    You are a helpful assistant connected to Composio Tool Router.
    Use the available tools to answer user queries and perform Pdf co 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: ["pdf_co"],
    },
  );

  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 Pdf co 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 Pdf co
```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 Pdf co, 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=["pdf_co"],
    )

    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 Pdf co actions."
    system_prompt = """
    You are a helpful assistant connected to Composio Tool Router.
    Use the available tools to answer user queries and perform Pdf co 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: ["pdf_co"],
    },
  );

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

- [ChatGPT](https://composio.dev/toolkits/pdf_co/framework/chatgpt)
- [OpenAI Agents SDK](https://composio.dev/toolkits/pdf_co/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/pdf_co/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/pdf_co/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/pdf_co/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/pdf_co/framework/codex)
- [Cursor](https://composio.dev/toolkits/pdf_co/framework/cursor)
- [VS Code](https://composio.dev/toolkits/pdf_co/framework/vscode)
- [OpenCode](https://composio.dev/toolkits/pdf_co/framework/opencode)
- [OpenClaw](https://composio.dev/toolkits/pdf_co/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/pdf_co/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/pdf_co/framework/cli)
- [Google ADK](https://composio.dev/toolkits/pdf_co/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/pdf_co/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/pdf_co/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/pdf_co/framework/mastra-ai)
- [CrewAI](https://composio.dev/toolkits/pdf_co/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 Pdf co MCP?

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

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

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

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