toolfare

Ask your AI what is actually in your warehouse.

The datasets in your BigQuery project, the tables and views inside each one, and every column’s name and type — answered inside ChatGPT, Claude, Codex or Cursor, so the person writing the query starts from the real schema. Connect BigQuery to toolfare once; your AI reads it with your own credentials, and every read costs 0 credits.

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Set it up in

Claude CodeCodexCursorVS CodeChatGPTClaude (web & desktop)Your own agent
toolfare

Sign in, connect the account once, paste the setup prompt to your agent, ask something. That’s the whole setup.

Your accounts, one connection

ads, analytics, commerce and revenue

BigQuery

Datasets, tables and schemas in your warehouse

AppsFlyer

AppsFlyer

Installs, ROAS and retention by media source

PostHog

Product events, funnels and retention

200+

APIs

40+

apps

30+

skills

Ask once and your AI reads the accounts you connected — no paid data call involved.

Your own account0 credits per readRead-only
About five minutes

Set it up once, in the app you already use.

Connect your BigQuery account in toolfare once. After that, every AI app you approve reads it through the same connection, with nothing to install for BigQuery itself.

Claude Code

Use the MCP connection in your AI app, or run the toolfare CLI in its terminal. Both use browser approval, discover capabilities and quote paid calls before they run.

  1. 1

    Create your toolfare workspace

    The connection is public and identical for everyone — your toolfare sign-in is what points it at your data. Connect one account in that workspace too: that is what your AI reads. The first time your AI uses the connection, toolfare asks you to approve the access and names the workspace it will read through. No workspace yet? toolfare has you create one right there, then brings you back to approve.

    Create your workspace

    Already have one? Nothing to do here — sign in when the connection asks for it.

  2. 2

    Copy the toolfare MCP connection

    https://api.toolfare.ai/mcp
  3. 3

    Add it to Claude Code

    claude mcp add --transport http toolfare https://api.toolfare.ai/mcp
    1. Run the command in any project. It writes the configuration and prints an `Added …` line.
    2. Authorize separately — adding a server does not open a browser.
    3. Run `claude mcp list` to confirm the server reads `✔ Connected`.
  4. 4

    Approve the access, then ask it something

    1. Inside Claude Code, run `/mcp` and follow the browser flow — or, from your shell, `claude mcp login toolfare`.
    2. The toolfare consent page names the host, the workspace the grant will read through, and what the connection will be able to do.
    3. Approving creates the grant listed above on this page. Cancelling creates nothing.
    4. On a machine with no browser (SSH, headless Linux), `claude mcp login toolfare --no-browser` prints the URL and takes the pasted callback.

    Paste this first

    Using toolfare, search for what capabilities I can reach.

    A list of capabilities with names, what each reads and what each costs. An empty list means the search matched nothing — try broader words. An error naming authorization means the host never completed the browser step.

Checked 2026-08-25 against Claude Code documentation. Menu names change between releases — look for connectors, apps, tools or integrations.

Open the console to connect your accounts
4 things you can ask it

Ask in plain English. These are the tools behind the answer.

Each one is a tool your AI is offered by name once you connect. You never have to type the name — it is here so you can tell what actually ran.

  • The datasets in a project

    bigquery_list_datasets

    Every dataset the connection can see, with its region — the starting point for knowing what data exists at all.

    • “What datasets do we have in BigQuery?”
    • “Which of our datasets are in the EU region?”

  • The tables and views inside a dataset

    bigquery_list_tables

    Tables, views, materialised views and external tables in one dataset, each with its type — views are often the curated entry point, and they are listed alongside tables.

    • “What tables are in our marketing dataset?”
    • “Is there a view for daily revenue in the analytics dataset?”

  • Every column’s name and type

    bigquery_get_table_schema

    A table or view’s columns — name, type and mode, with nested fields one level deep — plus its row and byte counts.

    • “What columns does the orders table have?”
    • “How many rows are in the sessions table, and how big is it?”

And 1 more, same connection

Named rather than worked through — your AI discovers all of them through the connection itself, so you never have to pick one.

  • Projects available through your connectionbigquery_list_projects
Pricing

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