Do I need an API key?

No! Overpass API is completely free and open. No authentication required. Please be respectful with query sizes — avoid queries returning more than 10,000 elements.

What is a bounding box?

A bounding box defines a geographic area using min_latitude, min_longitude, max_latitude, max_longitude. Example: "40.70,-74.01,40.72,-73.98" covers Lower Manhattan, NYC.

Can I search near my current location?

Yes! Use search_nearby or search_nearby_amenities with your GPS coordinates and a radius in meters (e.g. lat=40.7128, lon=-74.0060, radius=1000 for 1km around NYC).

What amenities can I search for?

Common amenities: restaurant, cafe, fast_food, bar, pub, hospital, clinic, pharmacy, school, university, library, atm, bank, fuel, parking, police, fire_station, post_office, toilets, cinema, theatre, nightclub, museum.

Overpass (OpenStreetMap) MCP Server for CrewAI 16 tools — connect in under 2 minutes

Name: Overpass (OpenStreetMap)
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 16 Tools Framework

Connect your CrewAI agents to Overpass (OpenStreetMap) through Vinkius, pass the Edge URL in the `mcps` parameter and every Overpass (OpenStreetMap) tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

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python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Overpass (OpenStreetMap) Specialist",
    goal="Help users interact with Overpass (OpenStreetMap) effectively",
    backstory=(
        "You are an expert at leveraging Overpass (OpenStreetMap) tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token. get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in Overpass (OpenStreetMap) "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 16 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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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.

When paired with CrewAI, Overpass (OpenStreetMap) becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Overpass (OpenStreetMap) tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.

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 CrewAI 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 CrewAI via MCP

Follow these steps to integrate the Overpass (OpenStreetMap) MCP Server with CrewAI.

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py. CrewAI auto-discovers 16 tools from Overpass (OpenStreetMap)

Why Use CrewAI with the Overpass (OpenStreetMap) MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Overpass (OpenStreetMap) through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

Overpass (OpenStreetMap) + CrewAI Use Cases

Practical scenarios where CrewAI combined with the Overpass (OpenStreetMap) MCP Server delivers measurable value.

Automated multi-step research: a reconnaissance agent queries Overpass (OpenStreetMap) for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries Overpass (OpenStreetMap), analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Overpass (OpenStreetMap) tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries Overpass (OpenStreetMap) against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Overpass (OpenStreetMap) MCP Tools for CrewAI (16)

These 16 tools become available when you connect Overpass (OpenStreetMap) to CrewAI 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 CrewAI

Ready-to-use prompts you can give your CrewAI 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 CrewAI

Common issues when connecting Overpass (OpenStreetMap) to CrewAI through the Vinkius, and how to resolve them.

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

Overpass (OpenStreetMap) + CrewAI FAQ

Common questions about integrating Overpass (OpenStreetMap) MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.