Overpass (OpenStreetMap) MCP Server for LlamaIndex 16 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Overpass (OpenStreetMap) 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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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 Overpass (OpenStreetMap). "
"You have 16 tools available."
),
)
response = await agent.run(
"What tools are available in Overpass (OpenStreetMap)?"
)
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 Overpass (OpenStreetMap) MCP Server
Connect to Overpass API (OpenStreetMap) and query the world's largest free geographic database through natural conversation — no API key needed.
LlamaIndex agents combine Overpass (OpenStreetMap) tool responses with indexed documents for comprehensive, grounded answers. Connect 16 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
- Amenity Search — Find restaurants, cafes, hospitals, schools, pharmacies, ATMs, fuel stations and more
- Shop Search — Discover shops, supermarkets, bakeries, clothing stores and retail outlets
- Nearby Search — Find any amenity within a radius of any GPS coordinate
- Hotel Search — Locate hotels, hostels and tourist accommodation
- Park Search — Find parks, gardens and green spaces
- EV Charging — Locate electric vehicle charging stations
- Custom Queries — Execute custom Overpass QL queries for any OSM data
The Overpass (OpenStreetMap) MCP Server exposes 16 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 Overpass (OpenStreetMap) to LlamaIndex via MCP
Follow these steps to integrate the Overpass (OpenStreetMap) 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 16 tools from Overpass (OpenStreetMap)
Why Use LlamaIndex with the Overpass (OpenStreetMap) MCP Server
LlamaIndex provides unique advantages when paired with Overpass (OpenStreetMap) through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Overpass (OpenStreetMap) tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Overpass (OpenStreetMap) tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Overpass (OpenStreetMap), a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Overpass (OpenStreetMap) tools were called, what data was returned, and how it influenced the final answer
Overpass (OpenStreetMap) + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Overpass (OpenStreetMap) MCP Server delivers measurable value.
Hybrid search: combine Overpass (OpenStreetMap) real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Overpass (OpenStreetMap) 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 Overpass (OpenStreetMap) for fresh data
Analytical workflows: chain Overpass (OpenStreetMap) queries with LlamaIndex's data connectors to build multi-source analytical reports
Overpass (OpenStreetMap) MCP Tools for LlamaIndex (16)
These 16 tools become available when you connect Overpass (OpenStreetMap) to LlamaIndex via MCP:
custom_query
The query should be valid Overpass QL syntax. The output format is automatically set to JSON. If no out statement is included, "out geom;" is appended automatically. Example: `node["amenity"="cafe"](51.5,-0.15,51.51,-0.14); out geom;` Execute a custom Overpass QL query
search_amenities
Common amenities: "restaurant", "cafe", "school", "hospital", "pharmacy", "bank", "atm", "fuel", "parking", "toilets", "library", "police", "fire_station", "post_office", "cinema", "theatre", "nightclub", "bar", "pub", "fast_food", "ice_cream". Bbox format: lat_min,lon_min,lat_max,lon_max. Search for amenities (restaurants, schools, hospitals, etc.) in a bounding box
search_atms
Returns ATM locations, operator/bank names, addresses, 24/7 availability and network info. Bbox format: lat_min,lon_min,lat_max,lon_max. Search for ATMs in a bounding box
search_by_name
Optional amenity filter to narrow results. Returns matching elements with full details including addresses, phone numbers and websites. Search for OSM elements by name
search_by_tag
Bbox format: lat_min,lon_min,lat_max,lon_max (e.g. "51.249,-0.15,51.251,-0.10" for central London). Useful for finding specific OSM-tagged features. Search OpenStreetMap elements by tag key/value in a bounding box
search_charging_stations
Returns station names, addresses, connector types, charging speeds, operator info and access details. Bbox format: lat_min,lon_min,lat_max,lon_max. Search for EV charging stations in a bounding box
search_fuel_stations
Returns station names, brands, addresses, fuel types offered, opening hours and operator info. Bbox format: lat_min,lon_min,lat_max,lon_max. Search for fuel/gas stations in a bounding box
search_hospitals
Returns facility names, addresses, phone numbers, emergency services info, specialties and operator info. Bbox format: lat_min,lon_min,lat_max,lon_max. Search for hospitals and clinics in a bounding box
search_hotels
Returns hotel names, addresses, star ratings, phone numbers, websites and room info. Bbox format: lat_min,lon_min,lat_max,lon_max. Search for hotels in a bounding box
search_nearby
Useful for finding nearby amenities without defining a full bounding box. Returns names, addresses, distances and details. Search for OSM elements near a specific location
search_nearby_amenities
Common amenities: "restaurant", "cafe", "pharmacy", "atm", "bank", "hospital", "school", "supermarket", "fuel", "charging_station", "parking", "toilets", "police", "fire_station", "post_office". Search for specific amenities near a location
search_parks
Returns park names, addresses, area sizes, features (playgrounds, sports facilities) and operator info. Bbox format: lat_min,lon_min,lat_max,lon_max. Search for parks and green spaces in a bounding box
search_pharmacies
Returns pharmacy names, addresses, phone numbers, opening hours, dispensing info and operator info. Bbox format: lat_min,lon_min,lat_max,lon_max. Search for pharmacies in a bounding box
search_restaurants
Optional cuisine filter: "italian", "chinese", "japanese", "indian", "mexican", "thai", "french", "american", "pizza", "burger", "sushi", "vegan", "vegetarian". Bbox format: lat_min,lon_min,lat_max,lon_max. Search for restaurants in a bounding box
search_schools
Returns school names, addresses, phone numbers, websites, student capacity and operator info. Bbox format: lat_min,lon_min,lat_max,lon_max. Search for schools in a bounding box
search_shops
Optional shop type filter: "supermarket", "convenience", "clothes", "bakery", "butcher", "electronics", "furniture", "hardware", "jewelry", "mall", "bookmaker", "car", "car_repair", "chemist", "florist", "gift", "hairdresser", "mobile_phone", "shoes", "sports", "toys". Bbox format: lat_min,lon_min,lat_max,lon_max. Search for shops in a bounding box
Example Prompts for Overpass (OpenStreetMap) in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Overpass (OpenStreetMap) immediately.
"Find all restaurants in Lower Manhattan."
"Find ATMs within 500m of Times Square (40.7580, -73.9855)."
"Find EV charging stations in downtown San Francisco."
Troubleshooting Overpass (OpenStreetMap) MCP Server with LlamaIndex
Common issues when connecting Overpass (OpenStreetMap) to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpOverpass (OpenStreetMap) + LlamaIndex FAQ
Common questions about integrating Overpass (OpenStreetMap) 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 Overpass (OpenStreetMap) with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
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Python SDK for building production-grade OpenAI agent workflows.
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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 Overpass (OpenStreetMap) to LlamaIndex
Get your token, paste the configuration, and start using 16 tools in under 2 minutes. No API key management needed.