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Connect London Schools Estate Distances Cross Bor to your AI

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. Get precise education and mobility metrics for London boroughs.

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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.

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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 London Datastore.

  1. 01

    Count schools

    This capability counts London schools and provides breakdowns by phase and status.

  2. 02

    Cross border mobility

    This capability provides primary school mobility data by resident borough, sorted by the number of resident children.

  3. 03

    Home school distances

    This capability calculates the share of London pupils living far from their school based on borough and phase filters.

  4. 04

    List schools

    This capability retrieves London school records including addresses, coordinates, and status using filters for local authority or phase.

Capability set02 / 02

05-08

4 capabilities in this set.

Part of 8 available through London Datastore.

  1. 05

    School distance trends

    This capability pulls pupil travel distance series from 2010 to 2016 for a specific school using its URN or name.

  2. 06

    School lookup

    This capability finds specific London schools using a URN or a name substring.

  3. 07

    Schools by borough

    This capability returns the number of schools found within each London local authority.

  4. 08

    Schools overview

    This capability provides a citywide snapshot of the entire London school estate.

Real work

What teams actually run

01

Analyzing School Catchments

Use the distance capabilities to see how many students in a specific borough travel beyond the standard distance for their phase.

02

Tracking Urban Mobility

Identify which boroughs have the highest rates of children attending state schools outside their home area.

03

School Location Research

Retrieve precise WGS84 coordinates and local authority details for any school in the 2016 estate.

04

Longitudinal Distance Studies

Pull a six-year series of travel distance percentiles for a single school to observe changing patterns.

One connector, every AI

London Datastore 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 London Datastore 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

1713ms average. Fast in production.

London Datastore is checked daily against the live service.

Daily averagePeak 1861ms
Sep 28Today
Fastest day
1531ms
Slowest day
1861ms
14-day trend
Improving-8%

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 London Datastore, so you can see the experience inside your AI.

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

London Datastore Connector

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

Connector linkhttps://edge.vinkius.com/vk_preview_6lJgF3vHfoGYkx8Rg4XpSsAK576F9f0ZStRr4EBV/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 — London Datastore capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "london-schools-estate-distances-cross-border-mobility-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_6lJgF3vHfoGYkx8Rg4XpSsAK576F9f0ZStRr4EBV/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 London Datastore in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work London Datastore owners hand off.

This MCP is designed for professionals working with London's educational and urban infrastructure.

  • 01

    Education Researchers

    They use the capability to pull longitudinal distance trends and census mobility data for academic studies.

  • 02

    Transport Planners

    They analyze pupil travel distances and cross-border movement to plan local infrastructure.

  • 03

    Housing Planners

    They use school catchment and distance data to assess pressure on local residential areas.

  • 04

    Journalists

    They query the school estate and mobility metrics to report on London's educational landscape.

FAQ

Questions London Datastore owners ask.

  • 01

    What years does the school distance data cover?

    The travel distance trends cover the period from 2010 to 2016.

  • 02

    Is the school estate data recent?

    The school estate data is a snapshot from 2016.

  • 03

    What kind of coordinates are provided?

    The capabilities provide WGS84 coordinates for schools in the estate.

  • 04

    Can I search for schools by name?

    Yes, you can use the school_lookup capability to find schools by name or URN.

  • 05

    Does this include census data?

    Yes, the cross_border_mobility capability uses 2011 census data to show how many children live and attend school in different boroughs.

  • 06

    Do I need an API key?

    No. data.london.gov.uk publishes every dataset as an anonymous file download (CSV or Excel). This MCP defines no credentials and needs nothing configured.

  • 07

    Which year is the school estate from?

    The estate extract is a 2016 snapshot. That vintage is stated in every capability title that reads it. list_schools, count_schools and the borough ranking all say 2016 extract in the title.

  • 08

    What do the distance percentiles mean?

    For a given school and year, the pupils field is how many attend, and the q25/median/q75 plus mean fields are the kilometres the pupils travel to get there. the 25th, 50th and 75th percentile of that distribution. Both series run 2010 to 2016.

  • 09

    What is cross-border mobility?

    From the 2011 census: for each borough, how many of the children who live there attend a state school, and what share of them attend one outside their home borough. The table has one row per borough plus a London row, with children counts, state-school counts and the share attending outside the home borough.

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