Build with an AI agent

The Maker MCP lets you build on Toucan AI from the AI agent you already use, such as Claude, Cursor or VS Code, instead of the Toucan interface. You describe what you want. The agent reads your data through Toucan, so what it builds respects your definitions and your access rules.

It covers three jobs, each described below:

• Model your data: domains, measures, dimensions, metrics and joins.
• Secure your data: row-level and column-level access per end user.
• Build a data app: a small web app over your data, formerly called a custom front.

Looking for the other direction, letting your users ask questions from your product? See Integrate Toucan into your own AI chat, or which MCP do I need.

How it worksCopy Anchor Link

The Maker MCP is a server your agent connects to. It exposes Toucan tools (read data, write a domain, publish a data app) and prompts, ready-made briefs that tell the agent how to do a whole job, such as Build a data app.

• You sign in once, in your browser. There is no key to copy. The agent never sees your password.
• One connection is one person in one organization. It carries the account that approved it and the organization that was active in Toucan at that moment.
• Only makers and admins can connect. The tools can simulate any end user's attributes, so an explorer account is refused.
• Prompts guide, tools enforce. Safeguards live in the tools: you cannot apply an access policy that was not previewed, whatever the agent was told.
• Writes are logged. Every change an agent makes is recorded against your account.

Connect your agentCopy Anchor Link

Open Applications in the sidebar and select Connect an agent, or Build with your AI agent if you have no data app yet. Toucan fills in the server address for your environment and a name for your organization. If you don't see the button, either your role is not maker or admin, or data apps are not enabled for your organization: contact your Toucan representative, or connect with the server address below, which gives your agent every tool except the data app ones.

The server address is https://toucanai.cloud/api/maker-mcp. Connect before you start the agent: an agent cannot add a server to a session that is already running.

Any client that supports remote MCP servers with OAuth sign-in works. Need another tool, such as Codex? Ask us and we will add instructions.

The first call opens your browser, asks you to sign in to Toucan if needed, and shows a consent screen with the agent's name, your account, the organization it will be tied to and the permissions it asks for. Select Allow once.

To revoke a connection, open your account settings, go to the Security tab, and select Remove access next to the agent under Connected applications. It stops working on the next request.

Model your dataCopy Anchor Link

Your agent can read what your organization already has, then add to it.

Read

• list_databases lists the databases, tables and columns you are allowed to see, with the ids every query needs.
• list_domains and get_domain read your domains: their tables and joins, dimensions, metrics and measures.
• run_data_query runs a query against your semantic layer and returns the first 100 rows, with the same security as production.

Write (creating and changing domains)

• create_domain and update_domain define a domain over some of a database's tables, joined into a tree. find_domain_join_candidates proposes the relationships that can join them.
• create_domain_measure, create_domain_dimension and create_domain_metric add the building blocks. Matching update_… tools change them.

Two rules protect your model. The agent asks for your approval before each write, and it cannot delete anything: a domain's tables and joins can only be added to.

For a precise result, start your request with the business meaning: "Create a Sales domain over orders and customers, with revenue as the sum of order amount."

Secure your dataCopy Anchor Link

Access policies decide which rows and columns each end user sees. They apply live, to every user at once, with no draft. For that reason the agent can only apply a change that it previewed first.

1. list_user_attributes shows how your end users are described (for example company_id), and define_user_attribute creates one if none matches.
2. get_access_policy reads a table's row-level and column-level policies. A change replaces the whole policy, so the agent starts from the stored one.
3. preview_access_policy saves nothing. For two to five sample users, and one with no attribute values, it shows the rows they see now and after the change, the columns they keep, a few sample rows, and warnings: a user left with no rows, a rule that loosens the current one, a hidden column that a domain uses.
4. apply_access_policy applies the change only if it matches a preview from the same maker, for the same table and change, in the last 30 minutes, and the stored policies haven't changed since.

Invoke the Secure my data prompt (secure_my_data) to walk through these steps with the agent: pick the key column, describe your users, preview, apply once you approve, then check the result as each user.

See Row-level security and Column-level security for how policies work.

Build a data appCopy Anchor Link

A data app (formerly called a custom front) is a small web app that Toucan stores, serves and shows. It reads your data through Toucan, as the viewer, so row and column security apply automatically. It holds no key or token.

Two prompts drive the work:

• Build a data app (build_custom_front): the agent reads your organization's preferences and data, settles the query with you, writes the files, and shows you a draft.
• Edit a data app (edit_custom_front): the agent finds the app, reads its current code, and changes it.

Both take an optional instructions argument: what to show, for whom, and how it should look.

Every app has versions. The agent edits the draft, you review it at its address, then commit_custom_front_version freezes it and publish_custom_front_version serves it to readers. A draft can never be published directly, and the agent asks before publishing because it replaces what everyone sees. Values that people will want to change later, such as targets and labels, live in the app's store, and changing one takes effect immediately, with no publish.

An agent that can run a shell uploads files directly over HTTP (get_custom_front_upload_url), which is faster and cheaper for large files and built bundles than sending them through the conversation.

If your organization defines user attributes, tell the agent which values to preview with, for example "company is Green Flow".

Coming soonCopy Anchor Link

We are extending the Maker MCP so that an agent can run a whole project from start to finish:

• Keep your schema fresh and choose which tables people and agents can see.
• Analyze your data with AI, review the proposals and define joins, guided end to end.
• Manage access to data apps and dashboards for explorers, through user attributes.

TroubleshootingCopy Anchor Link

See MCP access and security for how authentication and permissions work.