GraphHopper 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 GraphHopper as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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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 GraphHopper. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in GraphHopper?"
)
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 GraphHopper MCP Server
Connect your GraphHopper account to any AI agent and take full control of your geospatial routing, geocoding, and fleet optimization through natural conversation.
LlamaIndex agents combine GraphHopper tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through 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
- Route Orchestration — Calculate optimal routes between multiple GPS stops, identifying precise asynchronous directions and time calculations bypassing URL length limits natively
- Geocoding discovery — Extract explicitly attached REST arrays targeting
/geocodeto translate human-readable addresses into precise LatLon coordinates for spatial analysis - Reverse Geocoding — Perform structural extraction of properties matching GPS pins exactly against named physical streets to verify localized entity bounds flawlessly
- Routing Matrix Calculation — Generate N x M arrays of travel times and distances to analyze complex grid logistics and distance tables between multiple points synchronously
- Isochrone Reachability — Identify precisely the boundary reachable in a specific time limit from a starting point, defining reachability polygons for site selection or delivery zones
- VRP Optimization — Command explicit JSON targets firing Traveling Salesman configs for multiple vehicles, checking time windows and capacity constraints to solve complex logistics synchronously
- Map Matching Auditing — Validate API logic correcting imprecise GPS jumps by snapping raw GPX tracks perfectly onto street vectors limitlessly
The GraphHopper 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 GraphHopper to LlamaIndex via MCP
Follow these steps to integrate the GraphHopper 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 GraphHopper
Why Use LlamaIndex with the GraphHopper MCP Server
LlamaIndex provides unique advantages when paired with GraphHopper through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine GraphHopper tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain GraphHopper tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query GraphHopper, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what GraphHopper tools were called, what data was returned, and how it influenced the final answer
GraphHopper + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the GraphHopper MCP Server delivers measurable value.
Hybrid search: combine GraphHopper real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query GraphHopper 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 GraphHopper for fresh data
Analytical workflows: chain GraphHopper queries with LlamaIndex's data connectors to build multi-source analytical reports
GraphHopper MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect GraphHopper to LlamaIndex via MCP:
calculate_distance_isochrone
Provision a highly-available JSON Payload generating physical borders
calculate_heavy_route
Identify precise active arrays spanning native multi-stop geometries
calculate_reachability_polygon
Enumerate explicitly attached structured rules exporting active Reachability
calculate_routing_matrix
Inspect deep internal arrays mitigating specific Math tables
calculate_url_route
Retrieve explicit Cloud logging tracing explicit lightweight Directions
poll_vrp_solution
Retrieve the exact structural matching verifying Delivery alternatives
reverse_geocode
Perform structural extraction of properties driving active OSM bindings
search_geocode
Identify bounded routing spaces inside the Headless GraphHopper Engine
snap_gpx_to_road
Irreversibly vaporize explicit validations extracting GPX logic natively
submit_vrp_optimizer
Dispatch an automated validation check routing explicit jsprit solves
Example Prompts for GraphHopper in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with GraphHopper immediately.
"Calculate a car route between '40.71, -74.00' and '40.75, -73.98'"
"Show me the 10-minute reachability zone from central Berlin"
"Reverse geocode these coordinates: '48.85, 2.35'"
Troubleshooting GraphHopper MCP Server with LlamaIndex
Common issues when connecting GraphHopper to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpGraphHopper + LlamaIndex FAQ
Common questions about integrating GraphHopper 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 GraphHopper 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 GraphHopper to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.