Does it return visual maps or raw data?

Raw structured JSON only — coordinates, distances, durations, and elevation values. No interactive map tiles are rendered. You can use the data to plot maps in your own application.

Does `optimized_trip_route` solve the Traveling Salesman Problem?

Yes. Pass an unordered set of coordinates and it returns the optimal visit sequence minimizing total travel time or distance.

Is there a free tier?

Yes. Stadia Maps offers a free tier with generous limits for geocoding, routing, and elevation queries. Sign up at stadiamaps.com and generate an API key from the dashboard.

Stadia Maps MCP Server for CrewAI 10 tools — connect in under 2 minutes

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 10 Tools Framework

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

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Stadia Maps Specialist",
    goal="Help users interact with Stadia Maps effectively",
    backstory=(
        "You are an expert at leveraging Stadia Maps 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 Stadia Maps "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 10 available tools "
        "and what they can do."
    ),
)

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

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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.

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

What you can do

Geospatial Coordination — Convert physical addresses into exact coordinates using forward_geocode, or deduce properties from latitude and longitude via reverse_geocode.
Route Computation — Instruct your AI to generate accurate driving vectors between locations via calculate_route, and establish extensive routing cost-matrices utilizing calculate_distance_matrix.
Logistical Optimization — Resolve complex routing problems automatically with optimized_trip_route, and map exact reachable perimeters utilizing calculate_isochrone.
Topography & Precision — Align raw GPS tracks to official street networks accurately with execute_map_matching, and retrieve detailed elevation metrics applying get_path_elevation.

The Stadia Maps MCP Server exposes 10 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 Stadia Maps to CrewAI via MCP

Follow these steps to integrate the Stadia Maps 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 10 tools from Stadia Maps

Why Use CrewAI with the Stadia Maps MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Stadia Maps 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 the 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

Stadia Maps + CrewAI Use Cases

Practical scenarios where CrewAI combined with the Stadia Maps MCP Server delivers measurable value.

Automated multi-step research: a reconnaissance agent queries Stadia Maps 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 Stadia Maps, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Stadia Maps 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 Stadia Maps against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Stadia Maps MCP Tools for CrewAI (10)

These 10 tools become available when you connect Stadia Maps to CrewAI 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 CrewAI

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

Common issues when connecting Stadia Maps 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

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

Stadia Maps + CrewAI FAQ

Common questions about integrating Stadia Maps 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.