How do I create a Doppler Service Token?

Log in to the [**Doppler Dashboard**](https://dashboard.doppler.com), select your project, go to **Settings** > **Tokens** and click **Generate Token**. Choose the scope (project + config/environment), set the access level (Read or Read+Write) and copy the token immediately — it won't be shown again.

Can I update multiple secrets at once?

Yes! Use the `change_secrets` tool with a JSON object mapping names to values, e.g. `{"DATABASE_URL":"postgres://new-host","API_KEY":"sk-new"}`. This creates or updates all specified secrets in a single atomic operation.

What is the difference between a Personal Token and a Service Token?

A **Personal Token** is scoped to your user account and can access all workspaces and projects you have permission for. A **Service Token** is scoped to a specific project and config, with either read-only or read+write access. Service tokens are recommended for CI/CD and automated integrations, while personal tokens are better for development and admin tasks.

Can I view the activity history for a project?

Yes! Use the `list_activity_logs` tool with the project_slug to see all audit events (secret reads, writes, config changes, user additions). Optionally filter by config_name to see activity for a specific environment only. Each log entry shows who performed the action, when, and what was affected.

Doppler MCP Server for LangChain 12 tools — connect in under 2 minutes

Name: Doppler
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 12 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect Doppler through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "doppler": {
            "transport": "streamable_http",
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
        }
    }) as client:
        tools = client.get_tools()
        agent = create_react_agent(
            ChatOpenAI(model="gpt-4o"),
            tools,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using Doppler, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Doppler MCP Server

Connect your Doppler account to any AI agent and take full control of your secrets management through natural conversation.

LangChain's ecosystem of 500+ components combines seamlessly with Doppler through native MCP adapters. Connect 12 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

What you can do

Workspace & Project Discovery — List all workspaces and projects with their names, slugs and descriptions
Config (Environment) Management — View all configs (development, staging, production) per project and their metadata
Secret Auditing — List all secret names and computed values for any config, with environment fallback resolution
Secret Operations — Add, update and delete secrets in any environment with atomic change requests
Activity Logging — Review the full audit log of secret reads, writes, config changes and user activity per project

The Doppler MCP Server exposes 12 tools through the Vinkius. Connect it to LangChain in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Doppler to LangChain via MCP

Follow these steps to integrate the Doppler MCP Server with LangChain.

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 12 tools from Doppler via MCP

Why Use LangChain with the Doppler MCP Server

LangChain provides unique advantages when paired with Doppler through the Model Context Protocol.

The largest ecosystem of integrations, chains, and agents. combine Doppler MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across Doppler queries for multi-turn workflows

Doppler + LangChain Use Cases

Practical scenarios where LangChain combined with the Doppler MCP Server delivers measurable value.

RAG with live data: combine Doppler tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query Doppler, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain Doppler tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every Doppler tool call, measure latency, and optimize your agent's performance

Doppler MCP Tools for LangChain (12)

These 12 tools become available when you connect Doppler to LangChain via MCP:

change_secrets

Provide project_slug, config_name and a JSON object mapping secret names to values. For example: {"DATABASE_URL":"postgres://...","API_KEY":"sk-..."}. Existing secrets not included are not modified. Add or update secrets in a Doppler config

delete_secrets

Provide project_slug, config_name and comma-separated secret names. WARNING: deleted secrets cannot be recovered. If a secret inherits a value from a parent, it reverts to that value. Delete secrets from a Doppler config

get_account

Returns account email, name, and token metadata (type, scope, permissions). Use this to verify your token is working correctly and understand its access level. Get the current Doppler account details

get_config

Returns config name, project, root status, associated environment template, creation date and locked status. Get details for a specific Doppler config

get_project

Provide the project slug (e.g. "my-api-project") and optionally the workspace slug. Get details for a specific Doppler project

get_secret

Returns the secret name and its resolved value with fallbacks from parent environments applied. Get a specific secret value from a Doppler config

list_activity_logs

Each entry shows who performed what action, when and the affected config. Optionally filter by config_name. Useful for security auditing and compliance. List activity logs for a Doppler project

list_configs

Each config represents a deployment environment (development, staging, production) and contains its own set of secrets. Returns config name, project slug, root status and environment template used. List configs (environments) for a Doppler project

list_environments

g. development, staging, production, preview). Returns environment name, slug and whether it is the default environment. List Doppler environment types

list_projects

Optionally filter by workspace slug. Each project contains configs (environments) and secrets. Returns project name, slug, description, and creation date. List Doppler projects

list_secrets

Returns each secret's name, computed value (with environment fallbacks applied), visibility status. Provide the project_slug and config_name. List all secrets for a Doppler config

list_workspaces

A workspace is the top-level organizational unit in Doppler that groups projects. Returns workspace name, slug and creation date. List all Doppler workspaces

Example Prompts for Doppler in LangChain

Ready-to-use prompts you can give your LangChain agent to start working with Doppler immediately.

"Show me all configs for my 'backend-api' project."

"Update the DATABASE_URL secret in my prod config to point to the new database."

"Who changed secrets in my project in the last week?"

Troubleshooting Doppler MCP Server with LangChain

Common issues when connecting Doppler to LangChain through the Vinkius, and how to resolve them.

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Doppler + LangChain FAQ

Common questions about integrating Doppler MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.