How to Use the Timeero MCP in CrewAI

Run autonomous multi-agent teams to monitor, audit, and dispatch field crews using CrewAI and Timeero.

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Connect Timeero MCP to CrewAI

Create your Vinkius account to connect Timeero to CrewAI — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup Timeero with CrewAI

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Run Autonomous Field Audits with CrewAI

Deploy a team of specialized agents using this MCP Server to monitor field operations. A CrewAI auditing agent can query `list_timeero_timesheets` to extract shift durations, while an analyst agent cross-references those numbers against job sites. If a discrepancy pops up, the analyst agent calls `get_timeero_timesheet` to pull the precise GPS coordinates and mileage logs. This lets your CrewAI agents flag anomalies without requiring manual manager oversight. Your team gets clean, pre-audited Timeero logs delivered directly to your dispatch dashboard.

Coordinate Schedules and Tasks Autonomously

Let your CrewAI agents manage shift assignments autonomously. A dispatcher agent uses `list_timeero_schedules` to review open slots and matches them with available crew members. The agent then queries `get_timeero_schedule` or `list_timeero_tasks` to ensure workers have the right instructions. It pulls specific details via `get_timeero_task` to prevent scheduling conflicts. This keeps your CrewAI autonomous operations running smoothly without manual scheduling head-scratchers.

Verify Field Coverage and API Health

Ensure your team is fully staffed across all job sites using CrewAI's shared memory. A monitoring agent runs `check_timeero_status` to verify connection, then calls `list_timeero_users` to see who is currently active. It checks active locations with `list_timeero_jobs` and gets the specific geofence parameters using `get_timeero_job`. If a worker is missing, it pulls their profile via `get_timeero_user` to send an automated check-in ping. This ensures no Timeero site is left unmonitored by your autonomous crew.

Setup guide

Set up Timeero MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke Timeero tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="Timeero Analyst",
    goal="Access and analyze Timeero data via MCP.",
    backstory="Expert analyst with direct Timeero access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent Timeero transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

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

Get your connection token →

Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

server_params = SseServerParameters(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="Timeero Analyst",
        goal="Access and analyze Timeero data via MCP.",
        backstory="Expert analyst with direct Timeero access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Timeero transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

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

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Timeero. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Timeero MCP in CrewAI

CrewAI agents use shared memory. When one agent calls the user list to get a list of active workers, that data is instantly accessible to other agents in the crew for downstream tasks.

Yes. You can filter tools using the CrewAI server configuration, exposing only read-only tools to your analyst agent while restricting write permissions.

Pass your Vinkius endpoint URL directly into the agent's parameters. The framework handles the connection over standard transports, giving your crew immediate access to the tools.

Yes. You can set up a hierarchical crew where a manager agent reviews schedules and assigns tasks, automatically alerting a human supervisor if conflicts cannot be resolved.

All MCP requests run through Vinkius inside secure, ephemeral sandboxes. Your sensitive employee timesheets, GPS paths, and API credentials are never stored or exposed to external networks.

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