How to Use the Trimble MCP in CrewAI

Q: Does Trimble assist with general location finding for CrewAI?

Absolutely. The searchtrimbleaddress tool allows the crew to search known commercial locations, and reversegeocode lets them identify a commercial address from any given set of coordinates.

Autonomous dispatch planning with specialized teams using CrewAI and Trimble MCP Server.

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Create your Vinkius account to connect Trimble to CrewAI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Trimble with CrewAI

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Advanced Route Analysis by the Routing Team

The `calculate_matrix_routing` tool lets your crew compare multiple origins to destinations, giving each agent a set of distances and travel times. The team can then use `calculate_driving_isochrone` to define the full reachable area from any given point. This setup allows for autonomous comparison: Agent A finds all options, while Agent B analyzes which option best fits time constraints.

Compliance and Address Validation

The crew can validate target locations by running `search_trimble_address` against the Trimble database. Another specialized agent uses `reverse_geocode` to confirm that raw coordinates actually correspond to a usable commercial address. This collaboration ensures that no route is planned until the destination has been verified by two separate tools.

Comprehensive Logistics Planning using MCP Server

The full suite of routing tools allows autonomous planning. One agent uses `calculate_truck_route` to generate the primary path, while a specialized compliance agent immediately runs `route_by_vehicle_size`. If dimensions are exceeded, the process flags it and suggests an alternative route. The team can also run a financial audit using `calculate_trip_tolls`, ensuring that all costs are accounted for before presenting a final plan.

Setup guide

Set up Trimble 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 Trimble tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

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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="Trimble Analyst",
        goal="Access and analyze Trimble data via MCP.",
        backstory="Expert analyst with direct Trimble access.",
        tools=mcp_tools,
    )

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

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Trimble. 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 Trimble MCP in CrewAI

You pass the MCP Server URL to your agents. The crew then assigns roles: one agent handles initial routing using `calculate_truck_route`, while another acts as a monitor, checking dimensions via `route_by_vehicle_size`.

Yes. You can set up two agents: one researches potential routes using `calculate_matrix_routing`, while a second agent calculates the associated mileage breakdown by state using `calculate_state_mileage`.

The server provides robust logistics data, including distance/time matrices, vehicle-specific routing rules (height/weight), and financial metrics like toll fees in USD. This gives your crew enough data to operate autonomously.

Absolutely. The `search_trimble_address` tool allows the crew to search known commercial locations, and `reverse_geocode` lets them identify a commercial address from any given set of coordinates.

You work with longitude/latitude for routing. The server also supports calculating isochrones (reachable areas) using `calculate_driving_isochrone`, which defines a polygon based on time.

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