Can I check my API gateway metrics through the agent?

Yes. The `get_metrics` tool allows your AI agent to retrieve real-time traffic and latency data for any specific project ID, helping you monitor gateway performance directly through chat.

How do I create a new consumer identity for API key authentication?

You can use the `create_consumer` tool. Provide the project ID and a JSON payload with the consumer's details, and Zuplo will provision the new identity for your API gateway instantly.

Is it possible to see my project's configuration files via conversation?

Absolutely. Use the `list_project_files` tool to retrieve the declarative configuration files for your API project, allowing you to audit routing and policy definitions through your agent.

Zuplo MCP Server for CrewAI 10 tools — connect in under 2 minutes

Name: Zuplo
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 10 Tools Framework

Connect your CrewAI agents to Zuplo through Vinkius, pass the Edge URL in the `mcps` parameter and every Zuplo 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="Zuplo Specialist",
    goal="Help users interact with Zuplo effectively",
    backstory=(
        "You are an expert at leveraging Zuplo 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 Zuplo "
        "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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Ed25519Audit chain

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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 Zuplo MCP Server

Connect your Zuplo account to any AI agent and manage your programmable API infrastructure through natural conversation.

When paired with CrewAI, Zuplo becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Zuplo 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

Project Management — List all Zuplo projects and API gateways in your account to monitor your integration landscape
Edge Deployments — Trigger new configuration deployments to the global edge network or audit previous deployment history
Consumer Governance — Create, manage, and revoke API key consumers to control who has access to your gateway authentication
Environment Auditing — List available project environments (e.g., working, production) and verify their current operational status
DNS & Domains — Retrieve and manage configured custom DNS domains for each of your API projects directly from your agent
Real-Time Metrics — Monitor gateway traffic, latency, and throughput metrics directly from Zuplo's edge analytics network
Declarative Config — Browse and list the configuration files that define your gateway's routing and policy logic

The Zuplo 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 Zuplo to CrewAI via MCP

Follow these steps to integrate the Zuplo 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 Zuplo

Why Use CrewAI with the Zuplo MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Zuplo 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

Zuplo + CrewAI Use Cases

Practical scenarios where CrewAI combined with the Zuplo MCP Server delivers measurable value.

Automated multi-step research: a reconnaissance agent queries Zuplo 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 Zuplo, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Zuplo 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 Zuplo against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Zuplo MCP Tools for CrewAI (10)

These 10 tools become available when you connect Zuplo to CrewAI via MCP:

create_consumer

Create API key consumer

create_deployment

Create a new deployment

delete_consumer

Delete API key consumer

get_metrics

Get edge metrics

list_consumers

List API key consumers

list_custom_domains

List custom domains

list_deployments

List deployments

list_environments

g., working, production) for a Project. List project environments

list_files

List configuration files

list_projects

List Zuplo projects

Example Prompts for Zuplo in CrewAI

Ready-to-use prompts you can give your CrewAI agent to start working with Zuplo immediately.

"List all my Zuplo projects."

"Check the latency metrics for 'proj-101'."

"Create a new consumer for 'Acme Partner' in project 'proj-101'."

Troubleshooting Zuplo MCP Server with CrewAI

Common issues when connecting Zuplo 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.

Zuplo + CrewAI FAQ

Common questions about integrating Zuplo 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.