Heroku (PaaS) MCP Server for LangChain 10 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect Heroku (PaaS) through the Vinkius and LangChain agents can call every tool natively — combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
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({
"heroku-paas": {
"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 Heroku (PaaS), show me what tools are available.",
}]
})
print(response["messages"][-1].content)
asyncio.run(main())
* 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 Heroku (PaaS) MCP Server
Connect your Heroku account to any AI agent and take full control of your cloud-native application management and dyno orchestration through natural conversation.
LangChain's ecosystem of 500+ components combines seamlessly with Heroku (PaaS) through native MCP adapters. Connect 10 tools via the 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
- App Management — List all hosted applications, create new deployment boundaries, and fetch intricate runtime constraints and framework details directly from your agent
- Dyno Orchestration — List individual containerized dynos, track their status (up, crashed, idle), and selectively reboot specific instances or entire clusters
- Environment & Config — Audit decrypted application environment variables (Config Vars) and retrieve third-party platform add-ons like Postgres or Redis
- Operational Control — Rapidly toggle maintenance mode to block inbound requests during migrations and perform hard reboots on stalled application clusters
- Infrastructure Audit — Identify underlying executing stacks (e.g. heroku-24), regional datacenter placements (US/EU), and total slug size in memory
The Heroku (PaaS) MCP Server exposes 10 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 Heroku (PaaS) to LangChain via MCP
Follow these steps to integrate the Heroku (PaaS) 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 10 tools from Heroku (PaaS) via MCP
Why Use LangChain with the Heroku (PaaS) MCP Server
LangChain provides unique advantages when paired with Heroku (PaaS) through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents — combine Heroku (PaaS) 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 Heroku (PaaS) queries for multi-turn workflows
Heroku (PaaS) + LangChain Use Cases
Practical scenarios where LangChain combined with the Heroku (PaaS) MCP Server delivers measurable value.
RAG with live data: combine Heroku (PaaS) tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Heroku (PaaS), synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Heroku (PaaS) tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Heroku (PaaS) tool call, measure latency, and optimize your agent's performance
Heroku (PaaS) MCP Tools for LangChain (10)
These 10 tools become available when you connect Heroku (PaaS) to LangChain via MCP:
create_app
Provision a fresh structural App container on Heroku
delete_app
Traffic routing instantly yields persistent 404/no web-dynos responses. Highly destructive. Permanently wipe an active App from Heroku servers
get_app_info
g. heroku-22, heroku-24). Confirms exact application routing URL mapping, total slug (code) size in memory, and regional datacenter placements (US or EU) verifying global latency strategies. Fetch intricate runtime constraints and framework details of an App
list_addons
Retrieve third-party Platform Add-ons mapping to an App
list_apps
Use this to discover App IDs, web URL designations, and git repository targets required to execute operational commands downstream. List all standard applications actively hosted on Heroku PaaS
list_config_vars
Retrieves highly confidential database tokens `DATABASE_URL`, SendGrid passwords, or OAuth keys. Dump decrypted Application Environment Variables
list_dynos
1, worker.1). Tracks exactly whether the dyno is "up", "crashed", "idle", or "starting" based on the internal slug runner engine's telemetry. List discrete containerized Dynos executing inside an App
restart_all_dynos
Often resolves ephemeral memory-leaks in Node.js or Ruby runtimes stalling standard request processing. Hard reboot all containers tied to an entire Application
restart_specific_dyno
Exceedingly useful for unsticking hung asynchronous queue workers without impacting active web traffic on the primary frontend replicas. Selectively reboot one isolated Dyno instance (e.g. worker.2)
toggle_maintenance_mode
Crucial for orchestrating complex sequential database migrations without encountering corrupted states from active sessions. Rapidly switch an Application's Maintenance Mode switch
Example Prompts for Heroku (PaaS) in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with Heroku (PaaS) immediately.
"List all my Heroku apps"
"Restart all dynos for 'production-api'"
"What's the current maintenance mode status for the 'staging-web' app?"
Troubleshooting Heroku (PaaS) MCP Server with LangChain
Common issues when connecting Heroku (PaaS) to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersHeroku (PaaS) + LangChain FAQ
Common questions about integrating Heroku (PaaS) MCP Server with LangChain.
How does LangChain connect to MCP servers?
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?
Can I trace MCP tool calls in LangSmith?
Connect Heroku (PaaS) with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Heroku (PaaS) to LangChain
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.