How to Use the Trimble MCP in LlamaIndex

Q: Can LlamaIndex use the MCP Server for Trimble's geocoding data?

Yes. You can index outputs from reversegeocode and searchtrimbleaddress. This turns raw coordinate lookups into searchable knowledge, making your application much more powerful.

Q: Is LlamaIndex MCP Server useful for calculating tolls and mileage for Trimble?

Yes. By indexing the outputs of calculatetriptolls and calculatestatemileage, you build a searchable knowledge base containing cost breakdowns for specific routes.

Index logistics knowledge for LlamaIndex RAG applications.

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Connect Trimble MCP to LlamaIndex

Create your Vinkius account to connect Trimble to LlamaIndex 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 LlamaIndex

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Indexing Location Data with MCP Server

When you run `search_trimble_address`, the resulting location data can be indexed into a vector store. This lets your knowledge base answer questions like, 'What commercial addresses are near this GPS point?' using the `reverse_geocode` tool. The output of geocoding—the nearest commercial address to coordinates—becomes part of your searchable knowledge index, keeping your RAG applications current with real-world data.

MCP Server for Spatial Data Retrieval

The `calculate_driving_isochrone` tool generates a reachable area based on time from a central point. You can index the coordinates that define this reachable zone, allowing your LlamaIndex agent to answer complex spatial queries. Instead of just getting data, you build an index answering questions like, 'Which areas were within 30 minutes driving time last month?'

MCP Server for Indexed Route Metrics

To track past trips, use `calculate_truck_route` to get the distance, time, and polyline. Indexing this output allows your LlamaIndex application to recall specific route performance metrics. Pair that with `calculate_state_mileage`. You can build a unified index from these two outputs, making it easy to query past trip records—'Show me all routes over 500 miles in Pennsylvania.'

Setup guide

Set up Trimble MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Trimble MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Trimble tools.",
)
response = await agent.run("List recent Trimble data")

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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 LlamaIndex

The server allows you to index structured data, like mileage figures and address results. This means your RAG application can ground answers in actual API responses from Trimble, rather than hallucinated text.

Yes. You can index outputs from `reverse_geocode` and `search_trimble_address`. This turns raw coordinate lookups into searchable knowledge, making your application much more powerful.

The server handles distance, time, and polyline data. By indexing these metrics from tools like `calculate_truck_route`, you create a searchable history of operational performance.

It helps by allowing you to index the results of multiple routing calculations, such as comparing matrix times (`calculate_matrix_routing`) against actual compliant route data. You can query which option was cheapest or fastest historically.

Yes. By indexing the outputs of `calculate_trip_tolls` and `calculate_state_mileage`, you build a searchable knowledge base containing cost breakdowns for specific routes.

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