How do I find my Workload API Key?

Log in to your account, navigate to **Settings** > **API**, and copy your unique Access Token from the credentials section.

Can I check workflow execution results via AI?

Yes! The `list_workflow_executions` tool allows your agent to retrieve high-fidelity success/failure metadata for all your automated runs.

How do I list my active workflows?

Use the `list_workload_workflows` tool to retrieve your complete high-fidelity directory along with the unique identifiers for all managed automations.

INSTANT PLUG AND PLAY

Workload MCP Server for CrewAIGive CrewAI instant access to 13 tools to Check Workload Status, Create Workflow, Disable Workflow, and more

Name: Workload
Availability: InStock
Author: Vinkius

Built by Vinkius GDPR 13 Tools Framework

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

Use with Your AI Agent

Ask AI about this App Connector for CrewAI

Open in ChatGPT Open in Claude Open in Perplexity

The Workload app connector for CrewAI is a standout in the Productivity category — giving your AI agent 13 tools to work with, ready to go from day one.

Vinkius delivers Streamable HTTP and SSE to any MCP client

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Workload Specialist",
    goal="Help users interact with Workload effectively",
    backstory=(
        "You are an expert at leveraging Workload 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 Workload "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 13 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 Workload MCP Server

Connect your Workload account to any AI agent and take full control of your business process automation and automated workflow orchestration through natural conversation.

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

Automation Portfolio Orchestration — List and manage your entire high-fidelity database of workflows programmatically, retrieving detailed trigger and action metadata
Execution Intelligence Architecture — Programmatically query and monitor workflow execution history and success rates to maintain a perfectly coordinated audit trail
Task & Resource Monitoring — Access real-time status updates for active automations and track task volume directly through your agent for instant reporting
Metadata Management — Programmatically retrieve high-fidelity workflow IDs and connection statuses to coordinate your organizational productivity ecosystem
Operational Monitoring — Verify account-level API connectivity and monitor orchestration volume directly through your agent for perfectly coordinated service scaling

The Workload MCP Server exposes 13 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.

All 13 Workload tools available for CrewAI

When CrewAI connects to Workload through Vinkius, your AI agent gets direct access to every tool listed below — spanning workflow-automation, process-orchestration, business-process, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.

check_workload_status

Verify connectivity

create_workflow

Create a workflow

disable_workflow

Disable a workflow

enable_workflow

Enable a workflow

get_connection

Get connection details

get_execution

Get execution details

get_workflow

Get workflow details

list_connections

List connections

list_executions

List executions

list_executions_by_workflow

List executions by workflow

list_logs

List workflow logs

list_workflows

List workflows

retry_execution

Retry an execution

Connect Workload to CrewAI via MCP

Follow these steps to wire Workload into CrewAI. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.

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 13 tools from Workload

Why Use CrewAI with the Workload MCP Server

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

Workload + CrewAI Use Cases

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

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

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

Example Prompts for Workload in CrewAI

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

"List all active workflows in my Workload account."

"Show the execution history for the 'Invoice Flow' (ID: wf_123)."

"Check my Workload orchestration metrics for this month."

Troubleshooting Workload MCP Server with CrewAI

Common issues when connecting Workload 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.

Workload + CrewAI FAQ

Common questions about integrating Workload 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.