How to integrate Encodian MCP with LlamaIndex

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Introduction

This guide walks you through connecting Encodian to LlamaIndex using the Composio tool router. By the end, you'll have a working Encodian agent that can resize all images in project folder, extract author and page count from pdf, add custom header to every pdf file through natural language commands.

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

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

Also integrate Encodian with

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

The Encodian MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Encodian account. It provides structured and secure access to your Encodian document automation suite, so your agent can perform actions like managing files, processing PDFs, encoding or decoding content, and automating workflow tasks on your behalf.

  • File management and property retrieval: Quickly get detailed information about files, move documents between containers, and keep your Microsoft 365 storage organized automatically.
  • Document processing and automation: Direct your agent to add headers and footers to PDFs, extract PDF metadata, or resize images for seamless document formatting and compliance tasks.
  • Base64 content conversion: Effortlessly encode text or files to Base64, or decode Base64 strings back to usable files for secure data exchange and workflow integration.
  • Archive extraction and manipulation: Unzip files and retrieve their contents, making it easy to automate bulk document handling or trigger downstream processing steps.
  • Data integrity and comparison tools: Use hashing and text comparison utilities to verify file integrity, detect changes, or ensure consistency across your documents and workflows.

Supported Tools & Triggers

Tools
Add Attachments to PDFTool to add file attachments to a PDF document.
Add Image Watermark to PDFTool to add an image watermark to a PDF document.
Add Image Watermark to PDF (Advanced)Tool to add an advanced image watermark to a PDF with precise control over positioning, opacity, scale, quality, and rotation.
Array Add ItemsTool to add items to a JSON array at a specified position (first, last, or specific index).
Create ZIP ArchiveTool to create a ZIP archive from multiple documents.
Apply AI OCR to PDFTool to apply AI-powered OCR to a PDF document with optional preprocessing filters.
Apply OCR to PDF (Standard)Tool to apply standard OCR to a PDF document with optional preprocessing filters.
Decode Base64 StringTool to decode a Base64 string to a file.
Base64 EncodeTool to encode a string to Base64.
Calculate DateTool to calculate a date by adding or subtracting a time interval from a given date.
Check Array Contains ValueTool to check if a value exists within a JSON array.
Check Text Contains ValueTool to check if a text string contains a specific value with configurable comparison rules.
Clean StringTool to clean text by removing control characters, invalid filename characters, and custom character sets.
Clean Up Photo ImageTool to clean up photo images by removing artifacts, correcting orientation, and enhancing quality.
Combine ArraysTool to combine two JSON arrays by matching a key attribute.
Compare TextTool to compare two text strings and determine if they match.
Compare Word DocumentsTool to compare two Microsoft Word or PDF documents and generate a document with tracked changes.
Compress ImageTool to compress an image in JPG or PNG format.
Compress PDFTool to compress a PDF document by optimizing images, removing unused objects, and applying various compression techniques.
Concatenate TextTool to concatenate an array of text values with an optional delimiter.
Array to JSONTool to convert an array to a named JSON object.
Array to XMLTool to convert a JSON array to XML format.
Convert File to PDFTool to convert a file to PDF format.
Convert HTML to ImageTool to convert HTML content to an image.
Convert HTML to PDF (V2)Tool to convert HTML content or a URL to PDF format (V2).
Convert HTML to WordTool to convert HTML content to Word (DOCX) format.
Convert Image to GrayscaleTool to convert an image to grayscale.
Convert Image to PDFTool to convert an image file to PDF format with optional OCR.
Convert JSON to ExcelTool to convert JSON data to Excel format.
Convert JSON to XMLTool to convert JSON data to XML format.
Convert Time ZoneTool to convert a date and time value from one time zone to another using Encodian's time zone conversion API.
Convert XML to JSONTool to convert XML strings to JSON format.
Count Array ItemsTool to count the number of items in a JSON array or object.
Create QR CodeTool to generate a QR code barcode image with customizable size, colors, border, and encoding options.
Format Text CaseTool to format text with various case transformations (uppercase, lowercase, title case, etc.
Hash DataTool to compute a cryptographic hash (MD5, SHA256, etc.
Unzip FileExtracts all files from a ZIP archive and returns their base64-encoded contents.
Get Convert Excel SchemaTool to retrieve the dynamic schema for Excel conversion operations.
Get Convert Word SchemaTool to retrieve the dynamic JSON schema for Word document conversion operations.
Get Convert CAD SchemaTool to retrieve the dynamic schema for CAD file conversion operations.
Get Convert Image to PDF SchemaTool to retrieve the dynamic schema for Convert - Image to PDF operations.
Get Convert PowerPoint SchemaTool to retrieve the dynamic schema for PowerPoint conversion operations.
Get Convert Visio SchemaTool to retrieve the dynamic schema for Visio file conversion.
Get Create Barcode SchemaTool to retrieve the dynamic schema for creating a barcode.
Get Crop Image SchemaTool to retrieve the dynamic schema for the Crop Image action.
Get Dynamic Schema for HTTP RequestTool to retrieve the dynamic schema for the HTTP Request utility based on authentication type.
Get Word Insert Text SchemaTool to retrieve the dynamic schema for Word Insert Text operations.
Get File PropertiesTool to retrieve properties of a file.
Get Operation Status for AIRunPromptTextTool to get the operation status of an AIRunPromptText operation.
Get Operation Status for Encodian Send to FilerTool to get the operation status for an Encodian Send to Filer operation.
Get Operation Status Extract ImageTool to retrieve the operation status of a PDF ExtractImage operation.
Get Operation Status for ExtractTextRegionTool to retrieve the operation status of an ExtractTextRegion operation.
Get Operation Status File OnlyTool to retrieve operation status for file-only operations.
Get Operation Status for Image Extract TextTool to get the operation status of an ImageExtractText operation.
Get Operation Status for Multiple FilesTool to retrieve the operation status of a Word MultipleFiles operation.
Get Operation Status - PDF Split BarcodeTool to retrieve operation status for a PDF split barcode operation.
Get Operation Status for Split DocumentTool to retrieve the operation status of a PDF SplitDocument operation.
Get Sign PDF SchemaTool to retrieve the dynamic schema for PDF signing operations.
Get Subscription StatusTool to retrieve Encodian subscription status for Flowr and Vertr.
Resize ImageTool to resize an image by percentage or dimensions.
Move FileTool to move a file between containers.
Add PDF Header FooterTool to add HTML header and footer to a PDF.
Get PDF MetadataExtract comprehensive metadata and properties from PDF documents.
Watermark PDFTool to apply a text watermark to a PDF.
Read QR Code from DocumentTool to read QR codes from PDF or DOCX documents.
Word - Replace Text With ImageTool to replace text with an image in a Word document.
Validate Email AddressValidates an email address string against a custom regex pattern using Encodian's validation API.
Validate URL AvailabilityTool to validate the availability of a specified URL.
Write Range to ExcelTool to write values to a cell range in an Excel worksheet.

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

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 Encodian account and project
  • Basic familiarity with async Python/Typescript

Getting API Keys for OpenAI, Composio, and Encodian

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

Installing dependencies

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

Create a new Python project and install the necessary dependencies:

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

Set environment variables

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

Create a .env file in your project root:

These credentials will be used to:

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

Import modules

import asyncio
import os
import dotenv

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

dotenv.load_dotenv()

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

Key imports:

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

Load environment variables and initialize Composio

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

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

What's happening:

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

Create a Tool Router session and build the agent function

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

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

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

What's happening here:

  • We create a Composio client using your API key and configure it with the LlamaIndex provider
  • We then create a tool router MCP session for your user, specifying the toolkits we want to use (in this case, encodian)
  • 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 Encodian tools.
  • The MCP tools are mapped to LlamaIndex-compatible tools and plug them into the Agent.

Create an interactive chat loop

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

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

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

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

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

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

What's happening here:

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

Define the main entry point

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

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

What's happening here:

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

Run the agent

npx ts-node llamaindex-agent.ts

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

Complete Code

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

import asyncio
import os
import signal
import dotenv

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

dotenv.load_dotenv()

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

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

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

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

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

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

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

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

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

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

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

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

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

Conclusion

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

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FAQ

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

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

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

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

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