Ask your data from an AI client

The Model Context Protocol endpoint lets an AI client read your instance directly: list the saved queries, run one, read a dashboard. Wiring it up takes an endpoint and a key. How useful it turns out to be depends mostly on your query library rather than on the client, which is what most of this guide is about.

What has to be true

The endpoint is read-only: it lists and runs saved queries and reads dashboards. It cannot create, edit or delete anything, so connecting a client does not give an assistant a way to change your instance.

The entire surface is saved queries. An assistant cannot write new SQL against your warehouse through this endpoint; it can only run what someone already saved. A library of eleven queries named test 3 makes for a useless MCP connection, and prompting will not fix that.

The credential is a person's key. It carries your permissions, so an MCP client can only see what you can see. Treat it like a password.

There are five tools in total: list_queries, get_query, run_query, list_dashboards, get_dashboard.

A saved query can reach the history as well as the live data. A query against the historical warehouse is a saved query like any other, so a library that includes a few captured tables gives an assistant the trend as well as the snapshot. Capture is offered on every source type, so that surface is as large as you have chosen to make it. Without those tables an assistant can say how many bikes are at a station; with them it can say whether that station has been emptying earlier than it did in June.

Before you start

An account on the instance, and an MCP-capable client: Claude Desktop, an IDE with MCP support, or anything else speaking the protocol.

The steps

1. Get the endpoint and the config

Connect → MCP carries your instance's real /mcp URL with a copy button, and a copyable JSON block for your client's MCP settings. Use the page rather than assembling the values by hand; it reads its own address, so what it hands you works as pasted.

2. Get your key

Profile → Security holds your personal API key, masked, with controls to reveal, copy and regenerate it. The client authenticates with it as a header:

Authorization: Key <your-api-key>

Regenerate it if it ever leaks. Anything using the old key stops working immediately.

3. Make the library worth asking

The assistant picks a query by reading what the library says about itself, which makes this the step that decides the outcome.

• Name queries as questions or subjects, the way you would say them out loud: Weekday boardings by route, last 90 days rather than ridership v4.
• Fill in descriptions. A description is the only place to record what a query excludes, which agency it covers, and what its units are. An assistant reading boardings has no way to know that shuttles were dropped.
• Tag consistently. Tags cross object types and are how a subject gets found at all. If your instance uses domains, the domain:<key> tags already do this work.
• Save the warehouse queries too. A captured table with no saved query against it is invisible here, however good its catalog entry looks. One saved rollup per captured dataset is usually enough to open it up.
• Prefer parameters to near-duplicates. One query with a route parameter can be used for any route, where fourteen copies with route names in their titles are easy to pick wrongly from.

4. Ask something you already know the answer to

The first question through a new connection should be one you can check by hand. At this point you are testing the wiring and the permissions rather than the model.

How you know it worked

The client lists your queries, running one returns the same rows the query's own page shows, and a query you know you cannot open does not appear. That last check confirms the connection is scoped to your account rather than to the instance.

What this does not do

Write SQL against your warehouse	No. It runs saved queries
Create or edit queries, dashboards or anything else	No. The endpoint is read-only in both editions
Give the assistant a credential of its own	No. It uses yours, with your permissions
Work without an AI provider configured on the instance	Yes, it works. This is a client of yours talking to your instance. The in-product AI features are the separate thing that needs a provider