# How to integrate Zeplin MCP with LlamaIndex

```json
{
  "title": "How to integrate Zeplin MCP with LlamaIndex",
  "toolkit": "Zeplin",
  "toolkit_slug": "zeplin",
  "framework": "LlamaIndex",
  "framework_slug": "llama-index",
  "url": "https://composio.dev/toolkits/zeplin/framework/llama-index",
  "markdown_url": "https://composio.dev/toolkits/zeplin/framework/llama-index.md",
  "updated_at": "2026-05-12T10:31:12.652Z"
}
```

## Introduction

This guide walks you through connecting Zeplin to LlamaIndex using the Composio tool router. By the end, you'll have a working Zeplin agent that can list all project styleguides in zeplin, get all screens for a specific project, fetch comments from a specific zeplin screen through natural language commands.
This guide will help you understand how to give your LlamaIndex agent real control over a Zeplin account through Composio's Zeplin MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Zeplin with

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

The Zeplin MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Zeplin account. It provides structured and secure access to your Zeplin workspace, so your agent can perform actions like listing projects, fetching screens, exporting assets, managing components, and collaborating with your design team on your behalf.
- Project and styleguide management: Let your agent list, fetch, or organize your Zeplin projects and associated styleguides for faster design handoff and reference.
- Screen and asset retrieval: Automatically pull screen details, preview images, or export assets from any project directly into your workflow, no copy-paste required.
- Component library access: Have your agent fetch, list, or update components from your shared libraries to keep your design system in sync.
- Commenting and collaboration: Enable your agent to read, create, or manage comments on screens or components, streamlining feedback and design review cycles.
- Resource linking and metadata extraction: Allow your agent to extract, organize, or provide direct links to design resources and metadata, making documentation and developer handoff seamless.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `ZEPLIN_AUTH_O_AUTH_AUTHORIZE` | Start OAuth authorization (PKCE) | Tool to start OAuth 2.0 authorization code flow for Zeplin apps. Use when initiating user authorization; call before exchanging the code. |
| `ZEPLIN_CONNECTED_COMPONENTS_PROJECT_LIST` | List Project Connected Components | Tool to list connected components in a Zeplin project. Use when you have the project_id and need to retrieve connected components in a specific project. |
| `ZEPLIN_PROJECT_COLORS_LIST` | List Project Colors | Tool to list colors in a Zeplin project. Use when you need to fetch defined color tokens at the project level after obtaining the project ID. |
| `ZEPLIN_PROJECT_COLOR_UPDATE` | Update Project Color | Tool to update a color in a Zeplin project. Use when you need to modify RGBA channels or source ID of an existing color after confirming the project and color IDs. |
| `ZEPLIN_PROJECTS_GET` | Get Zeplin Project by ID | Tool to get a Zeplin project by ID. Use when you need detailed info about a specific project after confirming its project_id. |
| `ZEPLIN_PROJECTS_MEMBERS_INVITE` | Invite Project Member | Tool to invite a user to a Zeplin project. Use when you need to add a member by email or username to a project after obtaining the project ID. |
| `ZEPLIN_PROJECT_TEXT_STYLES_LIST` | List Project Text Styles | Tool to list text styles in a Zeplin project. Use when you need to fetch typography tokens defined at the project level after obtaining the project ID. |
| `ZEPLIN_PROJECT_TEXT_STYLE_UPDATE` | Update Project Text Style | Tool to update a text style in a Zeplin project. Use when you need to modify typography settings of an existing text style after confirming the project and text style IDs. |
| `ZEPLIN_SCREEN_ANNOTATION_DELETE` | Delete Screen Annotation | Tool to delete a screen annotation in Zeplin. Use when you need to remove a specific annotation from a Zeplin screen given its IDs. |
| `ZEPLIN_SCREEN_ANNOTATION_GET` | Get Screen Annotation | Tool to fetch a single screen annotation. Use when you know the screen_id and annotation_id and need the detailed annotation data. |
| `ZEPLIN_SCREEN_ANNOTATIONS_LIST` | List Screen Annotations | Tool to list annotations for a Zeplin screen. Use when you have the screen_id and need its annotations. |
| `ZEPLIN_SCREEN_ANNOTATIONS_UPDATE` | Update Screen Annotation | Tool to update a screen annotation's content, position, or type. Use after confirming screen_id and annotation_id. |
| `ZEPLIN_SCREEN_COMPONENTS_LIST` | List Screen Components | Tool to list components in a Zeplin screen. Use when you have the screen_id and need to retrieve UI components in a specific screen. |
| `ZEPLIN_SCREEN_SECTION_GET` | Get Screen Section | Tool to get a single screen section. Use when you need to fetch detailed information of a screen section by its ID after confirming the project and section IDs. |
| `ZEPLIN_SCREEN_SECTIONS_LIST` | List Screen Sections | Tool to list screen sections in a Zeplin project. Use when you need a paginated list of screen sections after confirming the project ID. |
| `ZEPLIN_SCREEN_VERSION_GET` | Get Screen Version | Tool to retrieve a specific screen version. Use after specifying both screen_id and version_id when you need detailed snapshot metadata. |
| `ZEPLIN_SCREEN_VERSIONS_CREATE` | Create Screen Version | Tool to create a new version of a screen. Use when uploading a new design snapshot (PNG or JPEG image) as a screen version. Requires a valid project_id, screen_id, and an image file. Optionally include a commit_message. |
| `ZEPLIN_SCREEN_VERSIONS_LIST` | List Screen Versions | Tool to list all versions of a screen. Use when you need to enumerate past screen snapshots after obtaining a screen ID. |
| `ZEPLIN_STYLEGUIDE_COLOR_CREATE` | Create Styleguide Color | Tool to create a new styleguide color. Use after obtaining the styleguide ID to define custom color tokens. |
| `ZEPLIN_STYLEGUIDE_COLORS_LIST` | List Styleguide Colors | Tool to list colors in a Zeplin styleguide. Use when you need to fetch defined color tokens after obtaining the styleguide ID. |
| `ZEPLIN_STYLEGUIDE_COLOR_UPDATE` | Update Styleguide Color | Tool to update a color in a Zeplin styleguide. Use after obtaining the styleguide and color IDs. |
| `ZEPLIN_STYLEGUIDE_TEXT_STYLES_LIST` | List Styleguide Text Styles | Tool to list text styles in a Zeplin styleguide. Use when you need to fetch defined typography tokens after obtaining the styleguide ID. |
| `ZEPLIN_STYLEGUIDE_TEXT_STYLE_UPDATE` | Update Styleguide Text Style | Tool to update a text style in a Zeplin styleguide. Use when you need to modify typography settings of an existing text style after confirming the styleguide and text style IDs. |
| `ZEPLIN_USERS_GET_PERSONAL_PROJECTS` | List Personal Projects | Tool to list personal projects. Use when you need to fetch all projects in the current user's personal workspace after authentication. |

## Supported Triggers

None listed.

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

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

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

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 Zeplin 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, zeplin)
- 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 Zeplin 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=["zeplin"],
    )

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

  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 Zeplin 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 Zeplin
```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 Zeplin, 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=["zeplin"],
    )

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

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

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

## Related Toolkits

- [Figma](https://composio.dev/toolkits/figma) - Figma is a collaborative interface design tool for teams and individuals. It streamlines design workflows with real-time collaboration and easy sharing.
- [Abyssale](https://composio.dev/toolkits/abyssale) - Abyssale is a creative automation platform for generating images, videos, GIFs, PDFs, and HTML5 content programmatically. It streamlines and scales visual content production for marketing, design, and operations teams.
- [Alttext ai](https://composio.dev/toolkits/alttext_ai) - AltText.ai is a service that generates alt text for images automatically. It helps boost accessibility and SEO for your visual content.
- [Bannerbear](https://composio.dev/toolkits/bannerbear) - Bannerbear is an API-driven platform for generating images and videos automatically at scale. It helps businesses create custom graphics, social visuals, and marketing assets using powerful templates.
- [Canva](https://composio.dev/toolkits/canva) - Canva is a drag-and-drop design suite for creating professional graphics, presentations, and marketing materials. It makes it easy for anyone to design with beautiful templates and a vast library of elements.
- [Claid ai](https://composio.dev/toolkits/claid_ai) - Claid.ai delivers AI-driven image editing APIs for tasks like background removal, upscaling, and color correction. It helps automate and enhance image workflows with powerful, developer-friendly tools.
- [Cloudinary](https://composio.dev/toolkits/cloudinary) - Cloudinary is a cloud-based platform for managing, uploading, and transforming images and videos. It streamlines media workflows and delivers optimized assets globally.
- [Cults](https://composio.dev/toolkits/cults) - Cults is a digital marketplace for 3D printing models, connecting designers and makers. It lets creators share, sell, and discover a huge variety of printable designs easily.
- [DeepImage](https://composio.dev/toolkits/deepimage) - DeepImage is an AI-powered image enhancer and upscaler. Get higher-quality images with just a few clicks.
- [Dreamstudio](https://composio.dev/toolkits/dreamstudio) - DreamStudio is Stability AI’s platform for generating and editing images with AI. It lets you easily turn ideas into stunning visuals, fast.
- [Dynapictures](https://composio.dev/toolkits/dynapictures) - Dynapictures is a cloud-based platform for generating personalized images at scale. Instantly create hundreds of custom visuals using your data sources, like Google Sheets.
- [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.
- [Gamma](https://composio.dev/toolkits/gamma) - Gamma is an AI-powered platform for making beautiful, interactive presentations and documents. It lets anyone create and share engaging decks with minimal effort.
- [Html to image](https://composio.dev/toolkits/html_to_image) - Html to image converts HTML and CSS into images or captures web page screenshots. Instantly generate visuals from code or web content—no manual screenshots needed.
- [Imagior](https://composio.dev/toolkits/imagior) - Imagior is an AI-powered image generation platform that lets you create and customize images using dynamic templates and APIs. Perfect for businesses and creators needing fast, scalable visuals without design hassle.
- [Imejis io](https://composio.dev/toolkits/imejis_io) - Imejis io is an API-based image generation platform with powerful customization and template support. It lets you create and modify images in seconds, no manual design work required.
- [Imgix](https://composio.dev/toolkits/imgix) - Imgix is a real-time image processing and delivery service for developers. It helps you optimize, transform, and deliver images efficiently at any scale.
- [Kraken io](https://composio.dev/toolkits/kraken_io) - Kraken.io is an image optimization and compression platform. It helps you shrink image file sizes while keeping visual quality intact.
- [Logo dev](https://composio.dev/toolkits/logo_dev) - Logo.dev is an API and database for high-resolution company logos and brand metadata. Instantly fetch official logos from any domain without scraping or manual searching.
- [Miro](https://composio.dev/toolkits/miro) - Miro is a collaborative online whiteboard platform for teams to brainstorm, design, and manage projects visually. It streamlines teamwork by enabling real-time idea sharing, diagramming, and workflow planning in a single space.

## Frequently Asked Questions

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

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

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

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

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