Vinkius
Parknav

Parknav MCP for AI. Know where to park before you leave home.

Claude Claude
ChatGPT ChatGPT
Cursor Cursor
Gemini Gemini
Windsurf Windsurf
VS Code VS Code
JetBrains JetBrains
Vercel Vercel
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Works with every AI agent you already use

…and any MCP-compatible client

Parknav MCP on Cursor AI Code EditorParknav MCP on Claude Desktop AppParknav MCP on OpenAI Agents SDKParknav MCP on Visual Studio CodeParknav MCP on GitHub Copilot AI AgentParknav MCP on Google Gemini AIParknav MCP on Lovable AI DevelopmentParknav MCP on Mistral AI AgentsParknav MCP on Amazon AWS Bedrock

Connect to your AI in seconds.

Parknav provides predictive parking intelligence for urban environments. This MCP Server gives your agent access to real-time block occupancy rates, predicts spot availability hours out, and helps you find the nearest open space using deep learning data.

What your AI can do

Optimize parking route

Calculates and returns a revised travel route that factors in optimal stopping points for parking.

Predict availability

Estimates the probability of finding an open spot at a location given a specific date and time.

Get street segments

Checks the live, granular status of individual sections of an on-street parking area.

+ 5 more capabilities included
Predict Future Availability

Your agent predicts the likelihood of finding a parking spot at any specific future time or location.

Get Live Block Occupancy Rates

You retrieve current, minute-by-minute usage data for designated street blocks in a city zone.

Locate Nearest Open Spot

The system immediately directs you to the closest currently unoccupied parking spot based on live data.

Determine Parking Zone Rules

You pull up local regulations, including time limits and associated fees for any given street segment.

Analyze Historical Patterns

The agent analyzes past data to show how parking availability changes across different days or times of the week.

Optimize Parking Routes

You generate a planned route that incorporates estimated time spent finding and paying for parking along the way.

Included with Plan

Waiting for input…

AI Agent

Parknav MCP Server: 8 Tools for Urban Mobility

Use these tools to analyze, predict, and locate parking spots across any city zone.

Make your AI actually useful.

Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.

Start using Parknav on Vinkius

Optimize Parking Route

Calculates and returns a revised travel route that factors in optimal stopping points for parking.

Predict Availability

Estimates the probability of finding an open spot at a location given a specific...

Get Street Segments

Checks the live, granular status of individual sections of an on-street parking area.

Get Historical Trends

Retrieves past availability data to show how parking spots are used at specific...

Get Parking Zones

Retrieves specific local rules, time limits, and associated fees for designated...

Get City Insights

Provides high-level data points about general parking conditions within a specified city area.

Get Nearest Spot

Finds and returns the coordinates of a currently open parking spot closest to a given location.

Get Realtime Occupancy

Gathers the current number of occupied spaces on a block or street segment right now.

Security and governance baked right in.

Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.

Claude AI

Claude AI

1

Open Claude Settings

Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.

2

Add Custom Connector

Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL:

https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp

Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. For OAuth-protected servers, expand Advanced settings to add credentials.

3

Start a conversation

Open a new chat. The Parknav integration is available immediately — no restart needed.

Choose How to Get Started

Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.

Build Your Own

Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.

  • Import from OpenAPI, Swagger, or YAML specs
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  • Real time usage dashboard and cost metering
  • Publish to catalog or keep private
Start building

Make Your AI Do More

Start with Parknav, then connect any of our 5,100+ other servers whenever your AI needs more. One click, no limits.

  • Use this MCP plus 5,100+ others, all in one place
  • Add new capabilities to your AI anytime you want
  • Every connection is secured and compliant automatically
  • Track usage and costs across all your servers
  • Works with Claude, ChatGPT, Cursor, and more
  • New servers added to the catalog every week
Parknav MCP server cover

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Parknav. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Works with Claude, ChatGPT, Cursor, and more

The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.

This connection provides 8 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.

Figuring out where to park shouldn't feel like a game of chance.

Before this, figuring out parking meant endless manual work: opening multiple city websites for zone rules, cross-referencing maps for current occupancy, and guessing if your destination was even worth the drive. You spent time copying codes and switching tabs just to get a vague idea of 'maybe.'

Now, Parknav handles that mess entirely. Your agent takes all those inputs—the date, the location, the rules—and spits out one answer: an accurate prediction or a clear path with open spots. It’s instant, specific intelligence.

Parknav MCP Server: Real-time and Predictive Data

The biggest thing that disappears is the need for multiple data calls. You don't have to call `get_realtime_occupancy` then check historical averages, and *then* run a route optimization; the system combines this into one actionable intelligence layer.

You get a complete picture of urban mobility—a prediction, not just a measurement. It changes your application from being merely directional to genuinely intelligent.

What your AI can actually do with this

Look, finding parking downtown is always a nightmare. This Parknav MCP Server gives your agent something way better than just pointing at a map; it lets you actually know where to go and when you'll get there. You connect this server to any AI client, and suddenly, your agent can predict spot availability hours out or check the minute-by-minute usage of entire blocks.

It’s about knowing the score before you even hit the curb.

If you need to know what’s happening on the street right now, you've got a few ways to check it. You can use get_realtime_occupancy to get the current number of occupied spaces across an entire block or street segment immediately. For more granular details, get_street_segments checks the live status of individual sections along an on-street parking area.

Need high-level context for a whole neighborhood? Run get_city_insights and you'll get general data points about parking conditions across a specified city zone.

Want to know if it’s worth driving out there at all? You can use predict_availability. This function estimates the probability of finding an open spot at any location given a specific date and time. It lets your agent forecast spot chances long before you get there. For deeper background, get_historical_trends pulls past availability data, showing how parking spots are used across different locations over extended periods of time.

If you're already on the street looking for a quick fix, get_nearest_spot finds and returns the coordinates of an currently unoccupied spot that’s closest to wherever you are. And if you just need general intel about what kind of streets you'll be hitting, get_parking_zones pulls up local regulations, time limits, and associated fees for any designated parking zone.

When it comes to planning the whole trip, Parknav handles it. You can run optimize_parking_route, which calculates a revised travel route that factors in optimal stopping points specifically for parking. This means your agent isn't just giving you directions; it’s building you a plan that accounts for time spent finding and paying for spots along the way.

Built · Hosted · Managed by Vinkius Parknav MCP Server - Predictive Parking Data
Server ID 019d75ef-1fde-731d-a556-602ae9692d70
Vinkius Inspector
Compliance Grade A+
Score 100/100
Vinkius Inspector Badge — Score 100/100

Questions you might have

How far in advance can I check parking with the `predict_availability` tool? +

You can predict availability for specific future dates and times. This uses deep learning models, so it's much better than simple averages because it accounts for day-of-week patterns.

Does `get_nearest_spot` only find spots on major roads? +

No. It finds the nearest spot regardless of whether it’s a main artery or a smaller side street, as long as live data confirms it's open.

What is the difference between `get_realtime_occupancy` and `predict_availability`? +

get_realtime_occupancy tells you what's happening right now. predict_availability forecasts what will happen later, which is essential for planning trips that start hours from now.

Can I use the API to find out local parking fees? +

Yes. The get_parking_zones tool provides access to regulations, including time limits and associated pricing information for specific zones.

What credentials do I need to use the Parknav MCP Server with my AI client? +

You must provide a valid API key and base URL. Your agent needs these two strings configured in your environment variables for all calls, like those made by get_city_insights.

Are there rate limits when calling the `get_realtime_occupancy` tool? +

Yes, usage is governed by your subscription tier. Exceeding the defined request quota results in a 429 error; you must implement exponential backoff logic in your agent's workflow.

Can I use `get_historical_trends` to compare availability across different city blocks? +

Yes, the tool accepts an array of coordinates or street IDs. This lets you run comparative analyses on multiple locations in a single call, which is useful for strategic planning.

Does `optimize_parking_route` account for varying time constraints between stops? +

Yes, you must pass a detailed sequence of waypoints and their associated required duration estimates. The algorithm calculates travel time plus predicted parking search time at each stop.

How far in advance can Parknav predict availability? +

Parknav's AI can typically predict availability up to 24 hours in advance with high confidence, and up to 7 days with moderate confidence.

Does it cover off-street garages too? +

Parknav primarily focuses on on-street parking, but also integrates occupancy data from select off-street garages where sensors are available.

What data sources does Parknav use? +

Parknav combines IoT sensor data, historical trends, city event data, and weather patterns using deep learning models to generate its predictions.

Built & Managed by Vinkius 30s setup 8 tools

We've already built the connector for Parknav. Just plug in your AI agents and start using Vinkius.

No hosting. No infrastructure. No complex setup.
All 8 tools are live and waiting. You're up and running in seconds.

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Vinkius runs on VS Code VS Code
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