Can I stop a running Modal app through my agent to save costs?

Yes. Use the `stop_app` tool with an active App ID. Your agent will dispatch a termination command to Modal, gracefully stopping the serverless container spin-up and preventing further billing for that specific execution.

How do I check which web endpoints are active for my deployments?

The `list_deployments` and `get_deployment` tools retrieve the Promoted image data. Your agent will expose the public URL endpoints and serving metadata associated with your long-running Modal deployments.

Can my agent audit the secrets and persistent volumes in my workspace?

Absolutely. Use the `list_secrets` and `list_volumes` tools to monitor your infrastructure assets. Your agent will report the names and references for your stored secrets and network block storage mounts attached to your compute instances.

Modal (Serverless AI Infrastructure) MCP Server for CrewAI 7 tools — connect in under 2 minutes

Name: Modal (Serverless AI Infrastructure)
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 7 Tools Framework

Connect your CrewAI agents to Modal (Serverless AI Infrastructure) through Vinkius, pass the Edge URL in the `mcps` parameter and every Modal (Serverless AI Infrastructure) 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

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Modal (Serverless AI Infrastructure) Specialist",
    goal="Help users interact with Modal (Serverless AI Infrastructure) effectively",
    backstory=(
        "You are an expert at leveraging Modal (Serverless AI Infrastructure) 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 Modal (Serverless AI Infrastructure) "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 7 available tools "
        "and what they can do."
    ),
)

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

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IAMAccess control

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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 Modal (Serverless AI Infrastructure) MCP Server

Connect your Modal account to any AI agent and take full control of your high-performance AI infrastructure, serverless GPU deployments, and persistent storage through natural conversation.

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

What you can do

App Orchestration — List isolated active and historical Modal app contexts to track function execution states and resource allocation directly from your agent
Deployment Management — Enumerate promoted long-running deployments and retrieve detailed web endpoints and serving configurations securely
Operational Control — Force stop actively running Modal app executions gracefully via App ID to prevent unnecessary billing cycles and manage system resources natively
Security & Secret Audit — List stored secret dictionary references and verify environment variable mappings attached to your serverless functions securely
Storage Visibility — Monitor persisted disk network block volumes and data mount directories used across your distributed compute instances
Infrastructure Inspection — Deep-dive into specific App or Deployment IDs to retrieve precise JSON metadata representing your infrastructure's current state vectors

The Modal (Serverless AI Infrastructure) MCP Server exposes 7 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 Modal (Serverless AI Infrastructure) to CrewAI via MCP

Follow these steps to integrate the Modal (Serverless AI Infrastructure) 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 7 tools from Modal (Serverless AI Infrastructure)

Why Use CrewAI with the Modal (Serverless AI Infrastructure) MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Modal (Serverless AI Infrastructure) 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 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

Modal (Serverless AI Infrastructure) + CrewAI Use Cases

Practical scenarios where CrewAI combined with the Modal (Serverless AI Infrastructure) MCP Server delivers measurable value.

Automated multi-step research: a reconnaissance agent queries Modal (Serverless AI Infrastructure) 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 Modal (Serverless AI Infrastructure), analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Modal (Serverless AI Infrastructure) 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 Modal (Serverless AI Infrastructure) against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Modal (Serverless AI Infrastructure) MCP Tools for CrewAI (7)

These 7 tools become available when you connect Modal (Serverless AI Infrastructure) to CrewAI via MCP:

get_app

Get static specifics of an exact Modal App ID

get_deployment

Get an explicitly tracked deployment detail mapped bound

list_apps

List isolated active/historical Modal Apps contexts

list_deployments

List strictly managed Modal platform explicitly promoted deployments

list_secrets

List static secret dictionary configuration references

list_volumes

List Modal persisted disk network block volumes

stop_app

Force stop an actively running explicit Modal App execution

Example Prompts for Modal (Serverless AI Infrastructure) in CrewAI

Ready-to-use prompts you can give your CrewAI agent to start working with Modal (Serverless AI Infrastructure) immediately.

"List all active Modal apps running in my account"

"Force stop Modal app ID 'ap-123'"

"Show me all persistent volumes configured in my workspace"

Troubleshooting Modal (Serverless AI Infrastructure) MCP Server with CrewAI

Common issues when connecting Modal (Serverless AI Infrastructure) 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

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

Modal (Serverless AI Infrastructure) + CrewAI FAQ

Common questions about integrating Modal (Serverless AI Infrastructure) 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.