Can I see the status of my dynos through my agent?

Yes. Use the `list_dynos` tool to see the real-time status of each container process (e.g. web.1, worker.1). You'll know exactly if a dyno is up, crashed, starting, or idle.

How do I check my application's environment variables?

The `list_config_vars` tool allows your agent to dump the full dictionary of environment variables injected into your dynos, including sensitive keys like DATABASE_URL or API tokens.

Can my agent restart a specific hung worker without affecting the web traffic?

Absolutely. Use the `restart_specific_dyno` tool to reboot a single instance (e.g. worker.2). This is perfect for unsticking hung asynchronous queue workers without impacting your primary frontend traffic.

Heroku (PaaS) MCP Server for CrewAI 10 tools — connect in under 2 minutes

Name: Heroku (PaaS)
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 10 Tools Framework

Connect your CrewAI agents to Heroku (PaaS) through the Vinkius — pass the Edge URL in the `mcps` parameter and every Heroku (PaaS) tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

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Vinkius supports streamable HTTP and SSE.

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Heroku (PaaS) Specialist",
    goal="Help users interact with Heroku (PaaS) effectively",
    backstory=(
        "You are an expert at leveraging Heroku (PaaS) tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token — get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in Heroku (PaaS) "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 10 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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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 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.

When paired with CrewAI, Heroku (PaaS) becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Heroku (PaaS) tools autonomously — one agent queries data, another analyzes results, a third compiles reports — all orchestrated through the Vinkius with zero configuration overhead.

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 CrewAI 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 CrewAI via MCP

Follow these steps to integrate the Heroku (PaaS) MCP Server with CrewAI.

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py — CrewAI auto-discovers 10 tools from Heroku (PaaS)

Why Use CrewAI with the Heroku (PaaS) MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Heroku (PaaS) through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles — one agent researches, another analyzes, a third generates reports — each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass the Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

Heroku (PaaS) + CrewAI Use Cases

Practical scenarios where CrewAI combined with the Heroku (PaaS) MCP Server delivers measurable value.

Automated multi-step research: a reconnaissance agent queries Heroku (PaaS) for raw data, then a second analyst agent cross-references findings and flags anomalies — all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries Heroku (PaaS), analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Heroku (PaaS) tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries Heroku (PaaS) against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Heroku (PaaS) MCP Tools for CrewAI (10)

These 10 tools become available when you connect Heroku (PaaS) to CrewAI 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 CrewAI

Ready-to-use prompts you can give your CrewAI 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 CrewAI

Common issues when connecting Heroku (PaaS) to CrewAI through the Vinkius, and how to resolve them.

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts — check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

The Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

Heroku (PaaS) + CrewAI FAQ

Common questions about integrating Heroku (PaaS) MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily — when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.