How to Use the Heroku (PaaS) MCP in LangChain

Chain Heroku (PaaS) operations directly into your LangChain workflows for automated infrastructure management.

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Connect Heroku (PaaS) MCP to LangChain

Create your Vinkius account to connect Heroku (PaaS) to LangChain and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Heroku (PaaS) with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Automate Heroku (PaaS) state changes

Feed your agent a goal and let it sequence calls to `create_app` and `toggle_maintenance_mode`. You get a pipeline where each step builds on the last without manual intervention. Chain outputs directly into your next task. Use `list_apps` to verify the environment before your agent triggers a deployment or configuration update.

Audit infrastructure with LangChain

Pass `list_dynos` output into your agent's memory to track runtime performance. It identifies which instances are struggling based on current telemetry. If you see a memory leak, the agent uses `restart_all_dynos` to clear the state. It handles the recovery logic so you don't have to watch the logs.

Secure config variable management

Access your app settings securely using `list_config_vars`. Your agent processes these variables within your defined chain to verify that your secrets match expected values. Keep your environment consistent by mapping these results to your validation logic. It ensures your infrastructure stays aligned with your code requirements.

Setup guide

Set up Heroku (PaaS) MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Heroku (PaaS) tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

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

async with MultiServerMCPClient({
    "heroku-paas-mcp": {
        "transport": "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,
    )
    result = await agent.ainvoke({
        "messages": "List recent Heroku (PaaS) transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Heroku (PaaS) MCP in LangChain

Install the necessary adapters and initialize the server connection using your endpoint token. Point your LangChain client at the Vinkius URL to pull the available tool definitions.

Yes, you can define a chain that executes multiple operations in order. The agent handles the flow of data between each tool call to ensure the deployment sequence completes.

Security depends on your implementation and how you manage your API tokens. Use environment variables to keep your credentials out of your code and limit the scope of the agent's access.

The server exposes standard tools that fit right into any chain. You call the tool list and pass them into your agent constructor to start building.

The server retrieves data like config variables directly from the API. Your agent only sees the values you explicitly request, keeping the data within your local execution context.

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