How to integrate Doppler MCP with LangChain

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Introduction

This guide walks you through connecting Doppler to LangChain 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 LangChain 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.

TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Connect your Doppler project to Composio
  • Create a Tool Router MCP session for Doppler
  • Initialize an MCP client and retrieve Doppler tools
  • Build a LangChain agent that can interact with Doppler
  • Set up an interactive chat interface for testing

What is LangChain?

LangChain is a framework for developing applications powered by language models. It provides tools and abstractions for building agents that can reason, use tools, and maintain conversation context.

Key features include:

  • Agent Framework: Build agents that can use tools and make decisions
  • MCP Integration: Connect to external services through Model Context Protocol adapters
  • Memory Management: Maintain conversation history across interactions
  • Multi-Provider Support: Works with OpenAI, Anthropic, and other LLM providers

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 & Triggers

Tools
Get Authenticated User InfoTool to retrieve information about the authenticated user or token.
Get Config LogTool to retrieve a specific config log from Doppler.
List Config LogsTool to retrieve configuration change logs for a specific config in a project.
Get ConfigTool to retrieve a specific Doppler config by project and config name.
List Doppler ConfigsTool to list configurations from a Doppler project.
Create Encrypted Share LinkTool to generate a Doppler Share link by sending an encrypted secret.
Create Plain Text Share LinkTool to generate a Doppler Share link by sending a plain text secret.
List EnvironmentsTool to list all environments in a Doppler project.
List IntegrationsTool to retrieve all existing integrations in Doppler.
List Change RequestsTool to list existing change requests in the Doppler workplace.
Get Project RoleTool to retrieve details of a specific project role in Doppler.
List Project RolesTool to list all available project roles in Doppler.
List Project Role PermissionsTool to list all available permissions for project roles in Doppler.
Get Project DetailsTool to retrieve details of a specific Doppler project by its identifier.
List Doppler ProjectsTool to list all Doppler projects in your workspace.
Delete SecretTool to delete a secret from a Doppler config.
Download SecretsTool to download secrets from a Doppler config in various formats.
Get SecretTool to retrieve a specific secret from a Doppler project config.
List Doppler SecretsTool to list all secrets for a specific Doppler config within a project.
List Secret NamesTool to retrieve the list of secret names from a specific Doppler config.
Update Doppler SecretsTool to update secrets in a Doppler config.
Update Secret NoteTool to update a note for a secret in Doppler.
Get Workplace InformationTool to retrieve workplace information from Doppler.
Get Workplace RoleTool to retrieve workplace role information from Doppler.
List Workplace RolesTool to list all workplace roles in your Doppler workspace.
List Workplace PermissionsTool to retrieve all available workplace permissions in Doppler.

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

What is Tool Router?

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

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

How the Tool Router works

The Tool Router follows a three-phase workflow:

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

Step-by-step Guide

Prerequisites

Before starting this tutorial, make sure you have:
  • Python 3.10 or higher installed on your system
  • A Composio account with an API key
  • An OpenAI API key
  • Basic familiarity with Python and async programming

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard and create an API key. You'll need credits to use the models, or you can connect to another model provider.
  • Keep the API key safe.
Composio API Key
  • Log in to the Composio dashboard.
  • Navigate to your API settings and generate a new API key.
  • Store this key securely as you'll need it for authentication.

Install dependencies

pip install composio-langchain langchain-mcp-adapters langchain python-dotenv

Install the required packages for LangChain with MCP support.

What's happening:

  • composio-langchain provides Composio integration for LangChain
  • langchain-mcp-adapters enables MCP client connections
  • langchain is the core agent framework
  • python-dotenv loads environment variables

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_composio_user_id_here
OPENAI_API_KEY=your_openai_api_key_here

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates your requests to Composio's API
  • COMPOSIO_USER_ID identifies the user for session management
  • OPENAI_API_KEY enables access to OpenAI's language models

Import dependencies

from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()
What's happening:
  • We're importing LangChain's MCP adapter and Composio SDK
  • The dotenv import loads environment variables from your .env file
  • This setup prepares the foundation for connecting LangChain with Doppler functionality through MCP

Initialize Composio client

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))

    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
What's happening:
  • We're loading the COMPOSIO_API_KEY from environment variables and validating it exists
  • Creating a Composio instance that will manage our connection to Doppler tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding

Create a Tool Router session

# Create Tool Router session for Doppler
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['doppler']
)

url = session.mcp.url
What's happening:
  • We're creating a Tool Router session that gives your agent access to Doppler tools
  • The create method takes the user ID and specifies which toolkits should be available
  • The returned session.mcp.url is the MCP server URL that your agent will use
  • This approach allows the agent to dynamically load and use Doppler tools as needed

Configure the agent with the MCP URL

client = MultiServerMCPClient({
    "doppler-agent": {
        "transport": "streamable_http",
        "url": session.mcp.url,
        "headers": {
            "x-api-key": os.getenv("COMPOSIO_API_KEY")
        }
    }
})

tools = await client.get_tools()

agent = create_agent("gpt-5", tools)
What's happening:
  • We're creating a MultiServerMCPClient that connects to our Doppler MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • get_tools() retrieves all available Doppler tools that the agent can use
  • We're creating a LangChain agent using the GPT-5 model

Set up interactive chat interface

conversation_history = []

print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Doppler related question or task to the agent.\n")

while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ['exit', 'quit', 'bye']:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    conversation_history.append({"role": "user", "content": user_input})
    print("\nAgent is thinking...\n")

    response = await agent.ainvoke({"messages": conversation_history})
    conversation_history = response['messages']
    final_response = response['messages'][-1].content
    print(f"Agent: {final_response}\n")
What's happening:
  • We initialize an empty conversation_history list to maintain context across interactions
  • A while loop continuously accepts user input from the command line
  • When a user types a message, it's added to the conversation history and sent to the agent
  • The agent processes the request using the ainvoke() method with the full conversation history
  • Users can type 'exit', 'quit', or 'bye' to end the chat session gracefully

Run the application

if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • We call the main() function using asyncio.run() to start the application

Complete Code

Here's the complete code to get you started with Doppler and LangChain:

from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    
    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
    
    session = composio.create(
        user_id=os.getenv("COMPOSIO_USER_ID"),
        toolkits=['doppler']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "doppler-agent": {
            "transport": "streamable_http",
            "url": url,
            "headers": {
                "x-api-key": os.getenv("COMPOSIO_API_KEY")
            }
        }
    })
    
    tools = await client.get_tools()
  
    agent = create_agent("gpt-5", tools)
    
    conversation_history = []
    
    print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
    print("Ask any Doppler related question or task to the agent.\n")
    
    while True:
        user_input = input("You: ").strip()
        
        if user_input.lower() in ['exit', 'quit', 'bye']:
            print("\nGoodbye!")
            break
        
        if not user_input:
            continue
        
        conversation_history.append({"role": "user", "content": user_input})
        print("\nAgent is thinking...\n")
        
        response = await agent.ainvoke({"messages": conversation_history})
        conversation_history = response['messages']
        final_response = response['messages'][-1].content
        print(f"Agent: {final_response}\n")

if __name__ == "__main__":
    asyncio.run(main())

Conclusion

You've successfully built a LangChain agent that can interact with Doppler through Composio's Tool Router.

Key features of this implementation:

  • Dynamic tool loading through Composio's Tool Router
  • Conversation history maintenance for context-aware responses
  • Async Python provides clean, efficient execution of agent workflows
You can extend this further by adding error handling, implementing specific business logic, or integrating additional Composio toolkits to create multi-app workflows.

How to build Doppler MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Doppler MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Doppler MCP with Claude Agent SDK

Google ADK

Google ADK

Use Doppler MCP with Google ADK

Vercel AI SDK

Vercel AI SDK

Use Doppler MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Doppler MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Doppler MCP with LlamaIndex

CrewAI

CrewAI

Use Doppler MCP with CrewAI

Claude Code

Claude Code

Use Doppler MCP with Claude Code

Codex

Codex

Use Doppler MCP with Codex

FAQ

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 LangChain?

Yes, you can. LangChain 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.

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