How to integrate Imgix MCP with LlamaIndex

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Introduction

This guide walks you through connecting Imgix to LlamaIndex using the Composio tool router. By the end, you'll have a working Imgix agent that can auto-optimize all images in this folder, overlay company logo on product photos, extract main color palette from image, add a 5px white border to image through natural language commands.

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

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

TL;DR

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

The Imgix MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Imgix account. It provides structured and secure access to your image library, so your agent can perform actions like optimizing images, applying overlays, adjusting visual properties, and extracting color palettes on your behalf.

  • Real-time image optimization: Ask your agent to automatically compress, enhance, or format images for faster delivery and better quality using Imgix's auto optimization tools.
  • Dynamic overlays and blending: Direct the agent to blend images, text, or solid colors over your base images—perfect for watermarks, banners, or creative composites.
  • Precision image adjustments: Have your agent modify image brightness, contrast, and border settings to meet your design and branding needs instantly.
  • Extract and analyze color palettes: Let your agent pull color palettes from any image, making it easy to generate theme colors or analyze brand consistency.
  • Fine-tune overlay positioning: Control exactly where overlays appear on your images by specifying alignment and pixel-level positioning through your agent.

Supported Tools & Triggers

Tools
Imgix Auto OptimizationTool to apply automatic best-effort image optimizations.
Blend OverlayTool to overlay an image, text, or color onto a base image using imgix blending parameters.
Imgix Blend AlignTool to align the overlay relative to the base image when blending.
Blend Color Over ImageTool to blend a solid color over an image using CSS keyword or hex.
Imgix Blend X PositionTool to position the overlay horizontally on an Imgix-rendered image.
Draw Image BorderTool to draw a border around an image.
Adjust Image BrightnessTool to adjust image brightness.
IMGIX_CHTool to opt in to Client Hints.
Extract Image Color PaletteTool to specify how many colors to return when extracting a color palette.
Adjust Image ContrastTool to adjust image contrast.
IMGIX_CROPTool to control cropping alignment and behavior using Imgix's 'crop' parameter.
IMGIX_CSTool to set or strip output color space/profile on an Imgix image.
IMGIX_DLTool to force asset download.
Adjust Image DPITool to embed DPI (dots-per-inch) metadata for print output on an Imgix-rendered image.
Set Device Pixel RatioTool to set device pixel ratio for an Imgix image.
Imgix URL ExpirationTool to append an expiration parameter to an Imgix URL so it returns 404 after a given time.
IMGIX_FITTool to control how an image fits target dimensions after resizing.
IMGIX_FMTool to choose output file format for the rendered asset.
Force Aspect RatioTool to force a target aspect ratio on an Imgix image.
IMGIX_HTool to set output image height in pixels or as a ratio of the source height.
Adjust Image HighlightsTool to adjust highlight tonal mapping (−100 to 0).
IMGIX_LIST_SOURCESTool to list all Sources for an account.
Set Watermark Base URLTool to set the base URL prepended to the watermark image path.
Watermark Fit ModeTool to set how a watermark fits its target dimensions.
Imgix Mark HeightTool to set watermark height on an Imgix URL in pixels or as a ratio of the watermark source.
IMGIX_MARK_PADTool to set pixel padding between a watermark and the image edge or between tiled watermarks.
Watermark WidthTool to set watermark width.
IMGIX_MASKTool to apply a mask to an image.
IMGIX_MAX_HTool to limit output image height.
IMGIX_MAX_WTool to set the maximum output width on an Imgix URL.
IMGIX_PALETTETool to extract a color palette from an image in CSS or JSON form.
Set CSS Palette PrefixTool to set class-name prefix for CSS palette output.
Set Output QualityTool to set output quality for lossy formats.
IMGIX_RECTTool to select a source-image rectangle region in Imgix before other resizing.
Imgix RotateTool to rotate an image on Imgix.
IMGIX_ROT_TYPETool to control rotation behavior when `rot` is applied.
Text OverlayTool to render a single-line UTF-8 text overlay on an image.
IMGIX_TXT_ALIGNTool to align a text overlay on an Imgix image.
Set Text ColorTool to set text overlay color on an Imgix image.
Set Text FontTool to choose font family/style for overlay text.
Set Text Outline WidthTool to set outline width around overlay text.
Text Outline ColorTool to set outline color for text.
IMGIX_TXT_SHADTool to set text shadow strength for overlay text.
IMGIX_TXT_SIZETool to set text font size in pixels.

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

What is Tool Router?

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

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

How the Tool Router works

The Tool Router follows a three-phase workflow:

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

Step-by-step Guide

Prerequisites

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

Getting API Keys for OpenAI, Composio, and Imgix

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 Imgix 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 imgix_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=["imgix"],
    )

    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 Imgix actions."
    system_prompt = """
    You are a helpful assistant connected to Composio Tool Router.
    Use the available tools to answer user queries and perform Imgix 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, imgix)
  • 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 Imgix 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 Imgix 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 Imgix

Run the agent

npx ts-node llamaindex-agent.ts

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

Complete Code

Here's the complete code to get you started with Imgix 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=["imgix"],
    )

    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 Imgix actions."
    system_prompt = """
    You are a helpful assistant connected to Composio Tool Router.
    Use the available tools to answer user queries and perform Imgix 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 Imgix to LlamaIndex through Composio's Tool Router MCP layer. Key takeaways:
  • Tool Router dynamically exposes Imgix 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 Imgix MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

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Claude Agent SDK

Claude Agent SDK

Use Imgix MCP with Claude Agent SDK

Google ADK

Google ADK

Use Imgix MCP with Google ADK

LangChain

LangChain

Use Imgix MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Imgix MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Imgix MCP with Mastra AI

CrewAI

CrewAI

Use Imgix MCP with CrewAI

Claude Code

Claude Code

Use Imgix MCP with Claude Code

FAQ

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

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

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

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

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