--- title: "Connect Google BigQuery to Metropolitan Museum of Art - APIANT.ai" description: "When \"New Job Completed\" fires in Google BigQuery, your agent runs \"Find objects by keyword\" in Metropolitan Museum of Art." canonical: "https://apiant.ai/connect/google-bigquery-to-metropolitan-museum-of-art" --- # Connect Google BigQuery to Metropolitan Museum of Art When **New Job Completed** fires in [Google BigQuery](https://apiant.ai/connections/google-bigquery), your agent runs **Find objects by keyword** in [Metropolitan Museum of Art](https://apiant.ai/connections/metropolitan-museum-of-art). Describe the whole flow and it is built, tested on every scenario and kept running. Google BigQuery **4** triggers Google BigQuery **10** actions Metropolitan Museum of Art **4** actions What the catalog holds today. Ask for anything missing and your agent reads the API documentation and adds it. ## What do you want Google BigQuery and Metropolitan Museum of Art to do together? Give your AI client this address. `https://mcp.apiant.ai` One connection does both: ask your client to act in an app once, or have it build an automation that keeps running. Any client that takes a remote MCP server. Nothing to install. Create your free account, then sign in once in your browser. ### Set it up in your client ### Claude Code Run this in the directory you want to work in. `claude mcp add --transport http -s project apiant-ai https://mcp.apiant.ai` Start Claude Code there, approve the server, and sign in in the browser page it opens. [Claude Code guide (opens in a new tab)](https://apiant.ai/docs/mcp/claude-code) ### Claude, web and desktop Open Customize, then Connectors, then Add custom connector, and paste the address. On Team and Enterprise an owner adds it first. [Setup guide (opens in a new tab)](https://apiant.ai/docs/mcp) ### Cursor Add the address to your MCP configuration as a server URL. Cursor signs you in in your browser. [Setup guide (opens in a new tab)](https://apiant.ai/docs/mcp) ### Zed Open Settings, then AI, then MCP Servers, then Add Server, then Add Remote Server. [Setup guide (opens in a new tab)](https://apiant.ai/docs/mcp) ### Codex and the ChatGPT desktop app Open Settings, then MCP servers, then Add server, and choose Streamable HTTP. From the terminal, Codex takes the address with its own add command and then signs you in. [Setup guide (opens in a new tab)](https://apiant.ai/docs/mcp) ### Any other MCP client Paste the address where the client asks for a remote MCP server URL, and name the server apiant-ai where it lets you. APIANT's skills reach the client over the same connection. [Setup guide (opens in a new tab)](https://apiant.ai/docs/mcp) Example flows ## What Google BigQuery can start in Metropolitan Museum of Art - When **New Job Completed** in Google BigQuery then **Find objects by keyword** in Metropolitan Museum of Art - When **New Row** in Google BigQuery then **Get object** in Metropolitan Museum of Art Starting points drawn from the triggers and actions in the catalog today. Describe the flow you need, at any depth, and your agent builds it and tests every scenario before it ships. Everything your agent can do with Google BigQuery and Metropolitan Museum of Art ## Triggers and actions 4 triggers · 14 actions - Trigger **New Job Completed** Triggers when a BigQuery job in the project finishes, whether it succeeded or failed. - Trigger **New Row** Triggers when a new row is added to a table. New rows are detected by a column you name that increases for newer rows, such as a creation time or a sequential ID. - Trigger **Query Job Completed (With Row Data)** Triggers when a SELECT query job in the project finishes successfully, and includes its result rows. - Trigger **Updated Row** Triggers when a row in a table is added or changed, detected by a last-updated timestamp column you name. - Action **Create Row** Creates a new row of data in a table. Note: Does not create a job. - Action **Create Rows** Creates new rows of data in a table (accepts line items). Note: Does not create a job. - Action **Delete Rows** Deletes every row in a table whose match column equals the value you give. - Action **Find One Row** Finds the first row in a table whose column equals the value you give. - Action **Get Rows for Job Completed** Returns the result rows of a finished query job. - Action **Import Data** Imports a JSON, CSV, Avro or Parquet file from Cloud Storage into a table. Waits up to about 40 seconds for the import to finish; larger imports keep running in BigQuery and return state RUNNING. - Action **List Datasets** Returns the datasets in a Google Cloud project. - Action **List Tables** Returns the tables in a BigQuery dataset. - Action **Run a Query** Runs a SQL query against a table in BigQuery. - Action **Update Row(s)** Updates every row in a table whose match column equals the value you give, setting the columns you choose. - Action **Find objects by keyword** Search for Object IDs matching a keyword query, with optional filters (title, tags, department, date, medium, etc.) and paging. - Action **Get object** Get the full open-access record for one artwork by Object ID, including images and provenance details. - Action **List departments** List all curatorial departments with their department ID and display name, for use as a filter on other operations. - Action **List objects** List valid Object IDs in the Met's collection, optionally filtered by department or last-updated date. ## Each app on its own - [ **Google BigQuery** BigQuery is Google's serverless and highly scalable enterprise data warehouse, designed to make all your...](https://apiant.ai/connections/google-bigquery) - [ **Metropolitan Museum of Art** The Metropolitan Museum of Art Collection API exposes the museum's open-access catalogue of more than four...](https://apiant.ai/connections/metropolitan-museum-of-art) Reviews ## What our customers say ## More from Google BigQuery - [ **Google BigQuery to HTML to Image** HTML to Image is a Powerful Image Generation API to convert HTML/CSS to Image and to take screenshots of...](https://apiant.ai/connect/google-bigquery-to-html-to-image) - [ **Google BigQuery to PaperClean** Clean document photos for printing, remove shadows, fix lighting](https://apiant.ai/connect/google-bigquery-to-paperclean) - [ **Google BigQuery to Resend** Resend is the email API for developers. It's designed for you to build, test, and send transactional and...](https://apiant.ai/connect/google-bigquery-to-resend) - [ **Google BigQuery to TypeSafe** TypeSafe's Jev model answers typed questions about a piece of content and tells you how certain it is.](https://apiant.ai/connect/google-bigquery-to-typesafe) - [ **Google BigQuery to HubSpot** HubSpot is your all-in-one stop for all of your marketing software needs.](https://apiant.ai/connect/google-bigquery-to-hubspot) - [ **Google BigQuery to Salesforce** Salesforce is a leading enterprise customer relationship management (CRM) platform.](https://apiant.ai/connect/google-bigquery-to-salesforce) ## More into Metropolitan Museum of Art - [ **Paperless to Metropolitan Museum of Art** Paperless lets you create, sign, and manage intelligent documents on a single platform.](https://apiant.ai/connect/paperless-to-metropolitan-museum-of-art) - [ **HubSpot to Metropolitan Museum of Art** HubSpot is your all-in-one stop for all of your marketing software needs.](https://apiant.ai/connect/hubspot-to-metropolitan-museum-of-art) - [ **Salesforce to Metropolitan Museum of Art** Salesforce is a leading enterprise customer relationship management (CRM) platform.](https://apiant.ai/connect/salesforce-to-metropolitan-museum-of-art) - [ **Slack to Metropolitan Museum of Art** Slack is a messaging platform for teams. Post messages, manage channels, users, reminders, and react to...](https://apiant.ai/connect/slack-to-metropolitan-museum-of-art) - [ **Google Sheets to Metropolitan Museum of Art** Google Sheets](https://apiant.ai/connect/google-sheets-to-metropolitan-museum-of-art) - [ **Gmail to Metropolitan Museum of Art** Gmail is Google’s email service, featuring spam protection, search functions, and seamless integration with...](https://apiant.ai/connect/gmail-to-metropolitan-museum-of-art) Questions ## Google BigQuery and Metropolitan Museum of Art, answered ### ### What can start an automation from Google BigQuery? Any of Google BigQuery's 4 triggers in the catalog, including New Job Completed, New Row, Query Job Completed (With Row Data) and Updated Row. Describe the moment in plain language and your agent picks the right one. ### ### What can your agent do in Metropolitan Museum of Art when something happens in Google BigQuery? Any of Metropolitan Museum of Art's 4 actions, including Find objects by keyword, Get object, List departments and List objects. Your agent maps the data both ways and tests every scenario before it ships. ### ### Can it also run from Metropolitan Museum of Art to Google BigQuery? Metropolitan Museum of Art has no triggers in the catalog today, so automations start in Google BigQuery. If you need the other direction, ask: your agent reads Metropolitan Museum of Art's API documentation and adds it. ### ### Do I have to build a connector for Google BigQuery or Metropolitan Museum of Art? No. Both are in the catalog with their triggers and actions. You describe the automation; your agent builds it, tests every scenario and keeps it running. ### ### Can my AI client use Google BigQuery and Metropolitan Museum of Art without building an automation? Yes. With your AI client connected to APIANT, ask it to do something in either app once and it runs that action on the spot. Ask for an automation and your agent builds it, tests every scenario and keeps it running. One connection covers both. ### ### What happens when something fails? Your agent works that out for you. It plans the failure paths along with the happy path, then tests every scenario before it ships. ### ### Is there a free plan? Yes. Every plan includes the agent, including free, and building from your own AI client spends no assistant credits.