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Connect Parknav to ChatGPT, Claude and Gemini

Set it up once and it works in ChatGPT, Claude, Cursor, and any other client you love. It all runs safely in the background, so you can relax and focus on what really matters. Stop circling blocks and start predicting availability before you arrive.

Ask AI about this Connector

Developed, maintained, and hosted by Vinkius.

MCP VERIFIED · PRODUCTION READY · VINKIUS GUARANTEED

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Works with modern AI clients that support MCP, including ChatGPT, Claude, Cursor, and more.

ChatGPTClaudeCursorPerplexityGeminiMicrosoft CopilotRaycastMeta AI

Your AI does the work. We keep it safe and sound.

No other AI tool gives you this. Your workflow runs in the client you already love, your account stays protected, and you decide what the agent can and cannot do. It is the whole reason to make the switch.

Each Connector lives inside our infrastructure, the same place where your workflow runs. The three things below are already part of it, working quietly in the background so you never have to think about them. Nothing to add, nothing to set up, nothing to worry about.

A futuristic MCP client docking bay
V8 Isolate01

Each Connector runs inside its own sealed environment.

Each Connector runs inside its own sealed V8 runtime — no access to your files, your system, or other servers. A Connector never sees past its own request.

Kill Switch02

One switch stops every agent. Instantly.

One action deactivates every Connector, revokes every token, and ends every session at once. AI Governance keeps the record.

FinOps per User03

Spending limits that pause instead of surprise.

Every user keeps their own connections and credentials, isolated from everyone else. Anything that would cross your spending limit is held for your approval, not billed.

Three controls, built into the layer your AI connects through.

Curated set · 8 capabilities

We ship only the actions that matter.

Capability set01 / 02

01-04

4 capabilities in this set.

Part of 8 available through Parknav.

  1. 01

    Get city insights

    This capability provides high-level parking data for an entire city.

  2. 02

    Get parking zones

    Use this to pull specific regulations and pricing for designated parking zones.

  3. 03

    Get realtime occupancy

    This capability returns the current occupancy rates for a specific location.

  4. 04

    Get street segments

    Get the live status of individual street blocks to see if they are full.

Capability set02 / 02

05-08

4 capabilities in this set.

Part of 8 available through Parknav.

  1. 05

    Predict availability

    This capability generates AI-driven forecasts for parking availability at a specific future time.

  2. 06

    Get nearest spot

    Find the closest available parking spot to your current coordinates.

  3. 07

    Optimize parking route

    This capability adjusts a route to prioritize paths with better parking options.

  4. 08

    Get historical trends

    Access past availability patterns to understand how parking changes by time or day.

Real work

What teams actually run

01

Trip Planning

Check if a destination will have parking available before you leave the house.

02

Delivery Optimization

Route drivers to areas where loading zones are currently open.

03

Urban Policy Research

Analyze how parking demand shifts throughout the week in specific cities.

04

Real-Time Navigation

Direct a vehicle to the closest open spot using live occupancy updates.

Automate a task

Put Parknav to work on one thing.

Step-by-step automations your AI runs through this Connector: the workflow, the prompts to start with, and the exact capabilities behind each move.

One connector, every AI

Parknav works with the most popular AI clients.

These are the most popular clients, each with a step-by-step guide: one link, set up once, with governance and visibility built in. And because everything runs on the MCP standard, the same connection also works in any other compatible client — nothing to rebuild.

ClaudeClaudeChatGPTChatGPTGeminiGeminiPerplexityPerplexityGrokGrokMicrosoft CopilotMicrosoft CopilotCursorCursorVS CodeVS CodeWindsurfWindsurfJetBrainsJetBrainsClineClineLangChainLangChainVercel AI SDKVercel AI SDKLovableLovableZ.aiZ.aiRaycastRaycastQwen CodeQwen CodeKimi CodeKimi CodeLe ChatLe Chat

Building your own app? The connector is yours to use.

You don't need a client to put Parknav to work: the same hosted connection plugs into your own applications and agent code, with the same governance on every request. Build with it, chat with it — one connection for both.

Observed, not estimated

951ms average. Fast in production.

Parknav is checked daily against the live service.

Daily averagePeak 1057ms
Oct 3Today
Fastest day
769ms
Slowest day
1057ms
14-day trend
Stable+3%

Connect your client

One URL. Every client.

Activate the Connector, copy your link, and paste it into the client you already use. 8 capabilities arrive ready to run.

Preview access · not provider authentication

The vk_preview_* token belongs to Vinkius preview infrastructure. It lets Claude discover and display the capabilities of Parknav, so you can see the experience inside your AI.

It does not authenticate your account with Parknav. Actions requiring credentials or live account data may not run until you activate the Connector and authorize the service.

Parknav Connector

You're all set. Choose your MCP client and follow the setup instructions.

Connector linkhttps://edge.vinkius.com/vk_preview_hf5g9sxHOem77wfPhUGwiNF12zw7j7HR2DxE2lwG/mcp

Claude Desktop

Follow the steps below to connect in seconds.

  1. 1In Claude Desktop, open Settings → Connectors.
  2. 2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
  3. 3Click Add and start a new chat — Parknav capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "parknav-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_hf5g9sxHOem77wfPhUGwiNF12zw7j7HR2DxE2lwG/mcp"
    }
  }
}
  • Claude
  • ChatGPT
  • Cursor
  • VS Code
  • Windsurf
  • Claude Code
  • JetBrains
  • Cline

Step-by-step instructions for each client are in the guide. How to connect

Guided setup for Claude? Set up Parknav in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work Parknav owners hand off.

This MCP is built for professionals managing movement in urban environments.

  • 01

    Navigation App Developers

    They feed predictive availability data into turn-by-turn guidance systems.

  • 02

    Smart City Planners

    They use historical trends to set better pricing and time limits.

  • 03

    Fleet Managers

    They optimize delivery routes based on real-time loading zone status.

FAQ

Questions Parknav owners ask.

  • 01

    How does Parknav predict parking availability?

    Parknav uses deep learning models and IoT sensor data to forecast when spots will become available.

  • 02

    Can I use this with Claude or Cursor?

    Yes, this MCP is compatible with any MCP-compatible client including Claude, Cursor, and Windsurf.

  • 03

    What kind of data does the MCP provide?

    It provides real-time occupancy, predictive availability, street segment status, and historical trends.

  • 04

    Does this cover both on-street and off-street parking?

    The data includes both on-street and off-street parking information.

  • 05

    How do I get started with Parknav?

    You subscribe through Vinkius and then provide your Parknav API credentials to enable the capabilities in your AI client.

  • 06

    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.

  • 07

    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.

  • 08

    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.

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