# How to integrate Shotstack MCP with LlamaIndex

```json
{
  "title": "How to integrate Shotstack MCP with LlamaIndex",
  "toolkit": "Shotstack",
  "toolkit_slug": "shotstack",
  "framework": "LlamaIndex",
  "framework_slug": "llama-index",
  "url": "https://composio.dev/toolkits/shotstack/framework/llama-index",
  "markdown_url": "https://composio.dev/toolkits/shotstack/framework/llama-index.md",
  "updated_at": "2026-05-12T10:26:06.027Z"
}
```

## Introduction

This guide walks you through connecting Shotstack to LlamaIndex using the Composio tool router. By the end, you'll have a working Shotstack agent that can create a video slideshow from uploaded images, generate a branded video intro with logo, combine multiple video clips into one file through natural language commands.
This guide will help you understand how to give your LlamaIndex agent real control over a Shotstack account through Composio's Shotstack MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Shotstack with

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

The Shotstack MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Shotstack account. It provides structured and secure access to powerful video, image, and audio automation features—so your agent can create dynamic media content, edit assets, manage rendering jobs, and retrieve results at scale on your behalf.
- Automated video and image generation: Let your agent assemble and render videos or images programmatically using templates, custom assets, and dynamic data.
- Media editing and composition: Enable your agent to cut, trim, overlay, and combine media clips—adding text, transitions, or audio tracks as needed.
- Batch rendering and job management: Have your agent submit, track, and manage multiple rendering jobs, so you can scale creative automation for campaigns or client deliverables.
- Asset and template organization: Allow your agent to upload, list, and organize reusable templates and media assets, keeping your creative workflow streamlined.
- Result retrieval and download: Automatically fetch completed renders and download media files, making finished content instantly available for distribution or review.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `SHOTSTACK_CREATE_TEMPLATE` | Create Template | Tool to create a new template for video editing. Use when you want to save a reusable timeline configuration as a template. Template changes do not retroactively affect past renders. |
| `SHOTSTACK_CREATE_TEMPLATE2` | Create Template (v2) | Tool to save an Edit as a re-usable template. Templates can be retrieved and modified before rendering. Use when you want to create a template with merge fields for dynamic content. |
| `SHOTSTACK_DELETE_INGESTED_MEDIA` | Delete Ingested Media | Tool to delete an ingested media asset. Use when you've confirmed the ingest ID and need to remove the file from Shotstack storage. |
| `SHOTSTACK_DELETE_TEMPLATE2` | Delete Template | Tool to delete a specific Shotstack template by its ID. Use when you need to remove a template permanently. |
| `SHOTSTACK_DELETE_WORKFLOW` | Delete Shotstack Workflow | Tool to delete a specific Shotstack workflow. Use when you need to permanently remove a workflow after confirming its ID. |
| `SHOTSTACK_FETCH_SOURCE` | Fetch Source | Tool to fetch a remote media file and store it as a source asset. Operation is asynchronous — poll SHOTSTACK_GET_INGEST_STATUS or SHOTSTACK_INSPECT_MEDIA until the asset is ready before passing it to SHOTSTACK_RENDER_VIDEO or other downstream tools. Use when you need to ingest a file before rendering. |
| `SHOTSTACK_GET_ASSETS` | Get Asset | Tool to fetch details of a hosted asset by its unique identifier. Use when you need to retrieve information about videos, images, audio files, thumbnails, or poster images hosted on Shotstack's CDN. |
| `SHOTSTACK_GET_ASSETS_RENDER` | Get Assets by Render ID | Tool to retrieve hosted assets by render ID. Use when you need to fetch one or more files (video, thumbnail, poster image) generated by a specific render job. |
| `SHOTSTACK_GET_RENDER_CALLBACK` | Get Render Callback | Tool to retrieve the webhook/callback URL configuration for a specific render job. Returns only callback settings (URL, method, headers), not render status or output URLs — use a separate render-status check to obtain final results. |
| `SHOTSTACK_GET_RENDER_STATUS` | Get Render Status | Tool to retrieve the current status and details of a Shotstack render job by render ID. Use when polling a render until done or failed, typically after creating a render with SHOTSTACK_RENDER_VIDEO. |
| `SHOTSTACK_GET_SOURCE` | Get Source Details | Tool to fetch the details of a specific source asset. Use when you need to inspect a source after uploading, check its status, or diagnose ingest/render failures—such as unsupported codecs, corrupt files, or bad URLs—before retrying. |
| `SHOTSTACK_GET_TEMPLATE` | Get Template | Tool to retrieve details of a specific template. Use when you have the ID of an existing template and need its metadata. |
| `SHOTSTACK_GET_TEMPLATE_BY_VERSION` | Get Template By Version | Tool to retrieve a template by template id and API version. Use when you need to fetch template details from a specific Edit API version. |
| `SHOTSTACK_GET_UPLOAD_URL` | Get Upload URL | Tool to request a signed URL for direct file upload to Shotstack. Use when you need to upload a file to Shotstack storage. The response returns a signed URL that you use to upload the file using a PUT request with the binary file. |
| `SHOTSTACK_INSPECT_MEDIA` | Inspect Media | Tool to inspect media metadata. Use before rendering to retrieve duration, resolution, frame rate, and format of an online media file — clip timecodes, trim points, and audio sync calculations depend on these values. Mixing assets without prior inspection can cause letterboxing, jitter, or audio sync issues in the final output. |
| `SHOTSTACK_LIST_SOURCES` | List Sources | Tool to list all source assets. Use when you need to retrieve source entries with optional pagination. |
| `SHOTSTACK_LIST_SOURCES2` | List Sources (with Environment) | Tool to list all ingested source files with environment selection. Use when you need to retrieve sources from stage (sandbox) or v1 (production) environment with optional pagination. |
| `SHOTSTACK_LIST_TEMPLATES` | List Templates | Tool to list all Shotstack templates for the account. Use after creating or updating templates to view your available templates. |
| `SHOTSTACK_LIST_TEMPLATES2` | List Templates with Environment | Tool to list all Shotstack templates for the specified environment. Use when you need to retrieve templates from a specific environment (stage or production). |
| `SHOTSTACK_POST_UPLOAD` | Request Upload URL | Tool to request a signed URL for direct file upload. Use when you need to upload a file to Shotstack storage. The response returns a signed URL that expires in one hour. |
| `SHOTSTACK_RENDER_VIDEO` | Render Video | Tool to initiate a new video render job. Use when you have defined a timeline and output settings and want to start rendering. |
| `SHOTSTACK_TRANSFER_ASSET` | Transfer Asset | Tool to transfer a file from any publicly available URL to one or more Serve API destinations. Use when you need to copy a file from an external source to Shotstack's hosting service or other configured destinations. |
| `SHOTSTACK_UPDATE_TEMPLATE` | Update Template | Tool to update an existing template by its ID. Use when you need to modify a template's name or edit configuration. Both name and complete template definition must be provided. |

## Supported Triggers

None listed.

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

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

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

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 Shotstack 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, shotstack)
- 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 Shotstack 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=["shotstack"],
    )

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

  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 Shotstack 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 Shotstack
```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 Shotstack, 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=["shotstack"],
    )

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

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

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

## Related Toolkits

- [Youtube](https://composio.dev/toolkits/youtube) - YouTube is a leading video-sharing platform for uploading, streaming, and discovering content. It empowers creators and businesses to reach global audiences and monetize their work.
- [Amara](https://composio.dev/toolkits/amara) - Amara is a collaborative platform for creating and managing subtitles and captions for videos. It helps make content accessible and multilingual for global audiences.
- [Cats](https://composio.dev/toolkits/cats) - Cats is an API with a huge library of cat images, breed data, and cat facts. It makes finding adorable cat photos and trivia effortless for your apps and users.
- [Chatfai](https://composio.dev/toolkits/chatfai) - Chatfai is an AI platform that lets users talk to AI versions of fictional characters from books, movies, and games. It offers an engaging, interactive experience for fans to chat, roleplay, and explore creative dialogues.
- [Cincopa](https://composio.dev/toolkits/cincopa) - Cincopa is a multimedia platform for uploading, managing, and customizing videos, images, and audio. It helps you deliver engaging media experiences with robust APIs and flexible integrations.
- [Dungeon fighter online](https://composio.dev/toolkits/dungeon_fighter_online) - Dungeon Fighter Online (DFO) is an arcade-style, side-scrolling action RPG packed with dynamic combat and progression. Play solo or with friends to battle monsters, complete quests, and upgrade your characters.
- [Elevenlabs](https://composio.dev/toolkits/elevenlabs) - Elevenlabs is an advanced AI voice generation platform for lifelike, multilingual speech synthesis. Perfect for creating natural voices for videos, apps, and business content in seconds.
- [Elevenreader](https://composio.dev/toolkits/elevenreader) - Elevenreader is an AI-powered text-to-speech service by ElevenLabs that converts written content into lifelike audio. It enables fast, natural audio generation from any text.
- [Epic games](https://composio.dev/toolkits/epic_games) - Epic Games is a leading video game publisher and digital storefront, known for Fortnite and Unreal Engine. It lets gamers access, manage, and purchase games all in one place.
- [Fal.ai](https://composio.dev/toolkits/fal_ai) - Fal.ai is a generative media platform offering 600+ AI models for images, video, voice, and audio. Developers use Fal.ai for fast, scalable access to cutting-edge generative AI tools.
- [Giphy](https://composio.dev/toolkits/giphy) - Giphy is the largest online library for searching and sharing GIFs and stickers. Instantly add vibrant animated content to your apps, chats, and workflows.
- [Headout](https://composio.dev/toolkits/headout) - Headout is a global platform for booking travel experiences, tours, and entertainment. It helps users discover and secure activities at top destinations, all in one place.
- [Imagekit io](https://composio.dev/toolkits/imagekit_io) - ImageKit.io is a cloud-based media management platform for image and video delivery. Instantly optimize, transform, and deliver visuals globally via a lightning-fast CDN.
- [Listennotes](https://composio.dev/toolkits/listennotes) - Listennotes is a powerful podcast search engine with a massive global database. Discover, search, and curate podcasts from around the world in seconds.
- [News api](https://composio.dev/toolkits/news_api) - News api is a REST API for searching and retrieving live news articles from across the web. Instantly access headlines, coverage, and breaking stories from thousands of sources.
- [RAWG Video Games Database](https://composio.dev/toolkits/rawg_video_games_database) - RAWG Video Games Database is the largest video game discovery and info service. Instantly access comprehensive details, ratings, and release dates for thousands of games.
- [Seat geek](https://composio.dev/toolkits/seat_geek) - SeatGeek is a live event platform offering APIs for concerts, sports, and theater data. Instantly access events, venues, and performers info for smarter ticketing and discovery.
- [Spotify](https://composio.dev/toolkits/spotify) - Spotify is a streaming service for music and podcasts with millions of tracks from artists worldwide. Enjoy personalized playlists, recommendations, and seamless listening across all your devices.
- [Ticketmaster](https://composio.dev/toolkits/ticketmaster) - Ticketmaster is a global platform for event discovery, ticket sales, and live entertainment management. Get real-time access to events and streamline ticketing for fans and organizers.
- [Gmail](https://composio.dev/toolkits/gmail) - Gmail is Google's email service with powerful spam protection, search, and G Suite integration. It keeps your inbox organized and makes communication fast and reliable.

## Frequently Asked Questions

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

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

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

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

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