How do I get a Deputy API Token?

Log in to your Deputy account, navigate to **Account Settings** → **API**, and generate a new Permanent Token for your integration.

What is the subdomain?

It is the specific URL part of your Deputy portal, for example: 'mycompany.au.deputy.com'. You need to include the full host string.

Can the agent approve timesheets?

This integration currently focuses on listing and auditing workforce data. Approving timesheets or modifying rosters should be managed through the Deputy dashboard or mobile app.

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

Name: Deputy
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 10 Tools Framework

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

Get MCP Server for AI Agents

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python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Deputy Specialist",
    goal="Help users interact with Deputy effectively",
    backstory=(
        "You are an expert at leveraging Deputy 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 Deputy "
        "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 Deputy MCP Server

Integrate Deputy, the ultimate workforce management solution, directly into your AI workflow. Manage your employee directory, monitor real-time shift rosters, track submitted timesheets, and handle leave requests using natural language.

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

Workforce Visibility — List and retrieve detailed profiles for all employees in your Deputy organization.
Roster Monitoring — Track current and upcoming shift rosters to ensure proper coverage across locations.
Timesheet Tracking — Review submitted timesheets, including actual start and end times and approval statuses.
Leave Management — List and monitor employee leave and time-off requests pending approval.

The Deputy 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 Deputy to CrewAI via MCP

Follow these steps to integrate the Deputy 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 Deputy

Why Use CrewAI with the Deputy MCP Server

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

Deputy + CrewAI Use Cases

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

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

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

Deputy MCP Tools for CrewAI (10)

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

get_authenticated_user

Retrieve metadata for the current authenticated API user

get_employee_profile

Get detailed information for a specific employee

list_active_rosters

List all current and upcoming shift rosters

list_business_locations

List all physical business locations (companies) configured in Deputy

list_completed_timesheets

List timesheets submitted by employees

list_currently_active_shifts

Identify employees who are currently clocked in (mock logic)

list_leave_requests

List all employee leave and time-off requests

list_pending_leave_approvals

List only the leave requests that are awaiting manager approval

list_workforce_employees

List all employees in your Deputy organization

search_employees_by_name

Search for an employee by their display name

Example Prompts for Deputy in CrewAI

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

"List all employees currently clocked in."

"Show me the roster for the 'Downtown Kitchen' location tomorrow."

"Are there any pending leave requests?"

Troubleshooting Deputy MCP Server with CrewAI

Common issues when connecting Deputy 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.

Deputy + CrewAI FAQ

Common questions about integrating Deputy 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.