# How to integrate Doppler MCP with LlamaIndex

```json
{
  "title": "How to integrate Doppler MCP with LlamaIndex",
  "toolkit": "Doppler",
  "toolkit_slug": "doppler",
  "framework": "LlamaIndex",
  "framework_slug": "llama-index",
  "url": "https://composio.dev/toolkits/doppler/framework/llama-index",
  "markdown_url": "https://composio.dev/toolkits/doppler/framework/llama-index.md",
  "updated_at": "2026-03-29T06:31:11.666Z"
}
```

## Introduction

This guide walks you through connecting Doppler to LlamaIndex using the Composio tool router. By the end, you'll have a working Doppler agent that can get secrets for staging environment in doppler, add a new secret to marketing project, list all projects and their environments through natural language commands.
This guide will help you understand how to give your LlamaIndex agent real control over a Doppler account through Composio's Doppler MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Doppler with

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

The Doppler MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Doppler account. It provides structured and secure access so your agent can perform Doppler operations on your behalf.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `DOPPLER_AUTH_ME` | Get Authenticated User Info | Tool to retrieve information about the authenticated user or token. Use when you need to verify authentication status or get details about the current token's workplace and permissions. |
| `DOPPLER_CONFIG_LOGS_GET` | Get Config Log | Tool to retrieve a specific config log from Doppler. Use when you need to view details about a particular configuration change or event. |
| `DOPPLER_CONFIG_LOGS_LIST` | List Config Logs | Tool to retrieve configuration change logs for a specific config in a project. Use when you need to view the history of configuration changes, track who made changes, or identify rollback actions. |
| `DOPPLER_CONFIGS_GET` | Get Config | Tool to retrieve a specific Doppler config by project and config name. Use when you need to get configuration details for a specific project environment. |
| `DOPPLER_LIST_DOPPLER_CONFIGS` | List Doppler Configs | Tool to list configurations from a Doppler project. Use when you need to retrieve all configs or filter by environment. Supports pagination for large result sets. |
| `DOPPLER_CREATE_ENCRYPTED_SHARE_LINK` | Create Encrypted Share Link | Tool to generate a Doppler Share link by sending an encrypted secret. Use when you need to securely share secrets with end-to-end encryption. The receive flow is end-to-end encrypted where the encrypted secret will be decrypted on the recipient's browser. |
| `DOPPLER_CREATE_PLAIN_TEXT_SHARE_LINK` | Create Plain Text Share Link | Tool to generate a Doppler Share link by sending a plain text secret. Use when you need to securely share secrets with expiration controls. The secret is not stored in plain text by Doppler; the receive flow is end-to-end encrypted where the encrypted secret is decrypted in the browser. |
| `DOPPLER_LIST_ENVIRONMENTS` | List Environments | Tool to list all environments in a Doppler project. Use when you need to retrieve the environments available in a specific project. |
| `DOPPLER_LIST_INTEGRATIONS` | List Integrations | Tool to retrieve all existing integrations in Doppler. Use when you need to list all configured integrations. |
| `DOPPLER_LIST_CHANGE_REQUESTS` | List Change Requests | Tool to list existing change requests in the Doppler workplace. Use when you need to retrieve all change requests and their current status. |
| `DOPPLER_GET_PROJECT_ROLE` | Get Project Role | Tool to retrieve details of a specific project role in Doppler. Use when you need to get information about a role's permissions and metadata. |
| `DOPPLER_LIST_PROJECT_ROLES` | List Project Roles | Tool to list all available project roles in Doppler. Use when you need to retrieve all roles for permission management or to see what roles are available. |
| `DOPPLER_LIST_PROJECT_ROLE_PERMISSIONS` | List Project Role Permissions | Tool to list all available permissions for project roles in Doppler. Use when you need to see what permissions can be assigned to custom project roles. |
| `DOPPLER_GET_PROJECT_DETAILS` | Get Project Details | Tool to retrieve details of a specific Doppler project by its identifier. Use when you need to get project metadata including name, description, and creation timestamp. |
| `DOPPLER_LIST_DOPPLER_PROJECTS` | List Doppler Projects | Tool to list all Doppler projects in your workspace. Use when you need to retrieve available projects for configuration management or to get project details. |
| `DOPPLER_DELETE_SECRET` | Delete Secret | Tool to delete a secret from a Doppler config. Use when you need to permanently remove a secret from a specific project and config. |
| `DOPPLER_DOWNLOAD_SECRETS` | Download Secrets | Tool to download secrets from a Doppler config in various formats. Use when you need to retrieve all secrets or a subset of secrets from a specific project and config. Supports multiple output formats and name transformations. |
| `DOPPLER_GET_SECRET` | Get Secret | Tool to retrieve a specific secret from a Doppler project config. Use when you need to get the value of a specific secret including its raw and computed values. |
| `DOPPLER_LIST_DOPPLER_SECRETS` | List Doppler Secrets | Tool to list all secrets for a specific Doppler config within a project. Use when you need to retrieve secret values and metadata. Returns both raw and computed values for each secret, along with visibility settings and optional notes. |
| `DOPPLER_LIST_SECRET_NAMES` | List Secret Names | Tool to retrieve the list of secret names from a specific Doppler config. Use when you need to list available secret names without their values. |
| `DOPPLER_UPDATE_DOPPLER_SECRETS` | Update Doppler Secrets | Tool to update secrets in a Doppler config. Use when you need to create or update secret values in a specific project and config. |
| `DOPPLER_UPDATE_SECRET_NOTE` | Update Secret Note | Tool to update a note for a secret in Doppler. Use when you need to add or modify documentation for a specific secret. The note will be applied to the specified secret across all environments in the project. |
| `DOPPLER_GET_WORKPLACE_INFORMATION` | Get Workplace Information | Tool to retrieve workplace information from Doppler. Use when you need to get workplace details including ID, name, billing email, and security email. |
| `DOPPLER_GET_WORKPLACE_ROLE` | Get Workplace Role | Tool to retrieve workplace role information from Doppler. Use when you need to get details about a specific role including its permissions and metadata. |
| `DOPPLER_LIST_WORKPLACE_ROLES` | List Workplace Roles | Tool to list all workplace roles in your Doppler workspace. Use when you need to retrieve available workplace roles for user management or permission configuration. |
| `DOPPLER_LIST_WORKPLACE_PERMISSIONS` | List Workplace Permissions | Tool to retrieve all available workplace permissions in Doppler. Use when you need to view the list of permissions that can be assigned to workplace roles. |

## Supported Triggers

None listed.

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

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

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

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 Doppler 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, doppler)
- 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 Doppler 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=["doppler"],
    )

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

  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 Doppler 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 Doppler
```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 Doppler, 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=["doppler"],
    )

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

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

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

## Related Toolkits

- [Supabase](https://composio.dev/toolkits/supabase) - Supabase is an open-source backend platform offering scalable Postgres databases, authentication, storage, and real-time APIs. It lets developers build modern apps without managing infrastructure.
- [Codeinterpreter](https://composio.dev/toolkits/codeinterpreter) - Codeinterpreter is a Python-based coding environment with built-in data analysis and visualization. It lets you instantly run scripts, plot results, and prototype solutions inside supported platforms.
- [GitHub](https://composio.dev/toolkits/github) - GitHub is a code hosting platform for version control and collaborative software development. It streamlines project management, code review, and team workflows in one place.
- [Ably](https://composio.dev/toolkits/ably) - Ably is a real-time messaging platform for live chat and data sync in modern apps. It offers global scale and rock-solid reliability for seamless, instant experiences.
- [Abuselpdb](https://composio.dev/toolkits/abuselpdb) - Abuselpdb is a central database for reporting and checking IPs linked to malicious online activity. Use it to quickly identify and report suspicious or abusive IP addresses.
- [Alchemy](https://composio.dev/toolkits/alchemy) - Alchemy is a blockchain development platform offering APIs and tools for Ethereum apps. It simplifies building and scaling Web3 projects with robust infrastructure.
- [Algolia](https://composio.dev/toolkits/algolia) - Algolia is a hosted search API that powers lightning-fast, relevant search experiences for web and mobile apps. It helps developers deliver instant, typo-tolerant, and scalable search without complex infrastructure.
- [Anchor browser](https://composio.dev/toolkits/anchor_browser) - Anchor browser is a developer platform for AI-powered web automation. It transforms complex browser actions into easy API endpoints for streamlined web interaction.
- [Apiflash](https://composio.dev/toolkits/apiflash) - Apiflash is a website screenshot API for programmatically capturing web pages. It delivers high-quality screenshots on demand for automation, monitoring, or reporting.
- [Apiverve](https://composio.dev/toolkits/apiverve) - Apiverve delivers a suite of powerful APIs that simplify integration for developers. It's designed for reliability and scalability so you can build faster, smarter applications without the integration headache.
- [Appcircle](https://composio.dev/toolkits/appcircle) - Appcircle is an enterprise-grade mobile CI/CD platform for building, testing, and publishing mobile apps. It streamlines mobile DevOps so teams ship faster and with more confidence.
- [Appdrag](https://composio.dev/toolkits/appdrag) - Appdrag is a cloud platform for building websites, APIs, and databases with drag-and-drop tools and code editing. It accelerates development and iteration by combining hosting, database management, and low-code features in one place.
- [Appveyor](https://composio.dev/toolkits/appveyor) - AppVeyor is a cloud-based continuous integration service for building, testing, and deploying applications. It helps developers automate and streamline their software delivery pipelines.
- [Backendless](https://composio.dev/toolkits/backendless) - Backendless is a backend-as-a-service platform for mobile and web apps, offering database, file storage, user authentication, and APIs. It helps developers ship scalable applications faster without managing server infrastructure.
- [Baserow](https://composio.dev/toolkits/baserow) - Baserow is an open-source no-code database platform for building collaborative data apps. It makes it easy for teams to organize data and automate workflows without writing code.
- [Bench](https://composio.dev/toolkits/bench) - Bench is a benchmarking tool for automated performance measurement and analysis. It helps you quickly evaluate, compare, and track your systems or workflows.
- [Better stack](https://composio.dev/toolkits/better_stack) - Better Stack is a monitoring, logging, and incident management solution for apps and services. It helps teams ensure application reliability and performance with real-time insights.
- [Bitbucket](https://composio.dev/toolkits/bitbucket) - Bitbucket is a Git-based code hosting and collaboration platform for teams. It enables secure repository management and streamlined code reviews.
- [Blazemeter](https://composio.dev/toolkits/blazemeter) - Blazemeter is a continuous testing platform for web and mobile app performance. It empowers teams to automate and analyze large-scale tests with ease.
- [Blocknative](https://composio.dev/toolkits/blocknative) - Blocknative delivers real-time mempool monitoring and transaction management for public blockchains. Instantly track pending transactions and optimize blockchain interactions with live data.

## Frequently Asked Questions

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

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

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

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

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