Stadia Maps MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Stadia Maps as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Stadia Maps. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Stadia Maps?"
)
print(response)
asyncio.run(main())
* 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 Stadia Maps MCP Server
Imbue your artificial intelligence environment with the geospatial and routing capabilities of Stadia Maps. Seamlessly audit logistical questions and compute optimal transit routes across numerous delivery points without leaving your conversational interface. Empower your assistant to translate standard addresses into precise geographic coordinates, calculate time-and-distance matrices objectively, or parse topographical elevation data efficiently, connecting global mapping infrastructure directly to your local workflows.
LlamaIndex agents combine Stadia Maps tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through the Vinkius and query live data alongside vector stores and SQL databases in a single turn — ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Geospatial Coordination — Convert physical addresses into exact coordinates using
forward_geocode, or deduce properties from latitude and longitude viareverse_geocode. - Route Computation — Instruct your AI to generate accurate driving vectors between locations via
calculate_route, and establish extensive routing cost-matrices utilizingcalculate_distance_matrix. - Logistical Optimization — Resolve complex routing problems automatically with
optimized_trip_route, and map exact reachable perimeters utilizingcalculate_isochrone. - Topography & Precision — Align raw GPS tracks to official street networks accurately with
execute_map_matching, and retrieve detailed elevation metrics applyingget_path_elevation.
The Stadia Maps MCP Server exposes 10 tools through the Vinkius. Connect it to LlamaIndex 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 Stadia Maps to LlamaIndex via MCP
Follow these steps to integrate the Stadia Maps MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 10 tools from Stadia Maps
Why Use LlamaIndex with the Stadia Maps MCP Server
LlamaIndex provides unique advantages when paired with Stadia Maps through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Stadia Maps tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Stadia Maps tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Stadia Maps, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Stadia Maps tools were called, what data was returned, and how it influenced the final answer
Stadia Maps + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Stadia Maps MCP Server delivers measurable value.
Hybrid search: combine Stadia Maps real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Stadia Maps to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Stadia Maps for fresh data
Analytical workflows: chain Stadia Maps queries with LlamaIndex's data connectors to build multi-source analytical reports
Stadia Maps MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Stadia Maps to LlamaIndex via MCP:
autocomplete_location
Provides predictive address suggestions based on partial input
calculate_distance_matrix
Calculates distances and travel times between multiple points
calculate_isochrone
Calculates an area reachable within a specific time or distance
calculate_route
Locations should be a JSON array of {lat, lon}. Costing can be "auto", "bicycle", or "pedestrian". Calculates a route between multiple geographic points
execute_map_matching
Snaps raw GPS points to the road network
forward_geocode
Converts a physical address string into geographic coordinates
get_path_elevation
Retrieves elevation/height data for a specific geographic path
get_timezone
Retrieves the local timezone for specific geographic coordinates
optimized_trip_route
Returns the optimized path. Calculates the most efficient route between multiple stops
reverse_geocode
Converts geographic coordinates into a physical address
Example Prompts for Stadia Maps in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Stadia Maps immediately.
"Locate and securely return the comprehensive latitude and longitude values associated with this address: '1600 Amphitheatre Parkway, Mountain View, CA'."
"Analyze these targeted locations formatting parameters into a complete trip route simulation enforcing an algorithmic analysis assuming optimal routing for automobiles."
Troubleshooting Stadia Maps MCP Server with LlamaIndex
Common issues when connecting Stadia Maps to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpStadia Maps + LlamaIndex FAQ
Common questions about integrating Stadia Maps MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Stadia Maps with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Stadia Maps to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.