How to Use the Mapbox (Maps & Geospatial) MCP in LlamaIndex

Index your location data into LlamaIndex knowledge bases with Mapbox (Maps & Geospatial).

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Connect Mapbox (Maps & Geospatial) MCP to LlamaIndex

Create your Vinkius account to connect Mapbox (Maps & Geospatial) 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 Mapbox (Maps & Geospatial) with LlamaIndex

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Perplexity

Ground spatial queries in real data

Use `reverse_geocode` to turn raw GPS pings into human-readable addresses, then index them into your LlamaIndex vector store. Your agents now answer questions grounded in actual site history. No more hallucinated locations. Every address retrieval becomes a persistent piece of knowledge for your RAG pipeline.

Perform semantic spatial search

Search for POIs via `search_places` and embed the results directly into your knowledge base. Your LlamaIndex agents retrieve relevant locations based on semantic similarity rather than just keyword matches. It connects your document store to the physical world. Your agents can now reason about places as if they were reading them from a report.

Analyze topography with elevation indexing

Query `get_elevation` for specific points and store the results as metadata in your index. This allows your agents to compare locations based on altitude and terrain when answering complex requests. You're essentially building a searchable map of your own data. It turns Mapbox (Maps & Geospatial) results into long-term assets for your system.

Setup guide

Set up Mapbox (Maps & Geospatial) 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 Mapbox (Maps & Geospatial) 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 Mapbox (Maps & Geospatial) tools.",
)
response = await agent.run("List recent Mapbox (Maps & Geospatial) data")

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Common questions about Mapbox (Maps & Geospatial) MCP in LlamaIndex

You pass the tool results into a document loader, which converts the API output into searchable nodes. This creates a bridge between your live location data and your static documents.

Absolutely, the tool results serve as context for your RAG queries. When you ask about a location, the agent retrieves the indexed API data to provide a factual answer.

Yes, once indexed, the information stays in your vector store. You can query your past location history anytime without re-calling the API.

It does, as the tool spec is designed for async execution. This allows your agents to fetch location data in parallel while processing other documents.

We enforce strict sandbox isolation for your indexed location data. Your geocoding history is stored only in your private vector store, shielded from other processes.

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