Every data-driven campaign, report, or automation eventually runs into the same question: “What are the exact columns in this Postgres table?” If you’re a founder, marketer, or agency lead, you don’t want to live inside psql—but you do need reliable answers. Let’s walk through practical ways to get Postgres column names, from hands-on methods to fully automated AI-agent workflows.

1. Manual and traditional ways to get Postgres column names

1.1 Use psql’s built-in table description

If you or your team can access Postgres via the psql CLI, this is the fastest manual option.

1. Open a terminal and connect:
   psql postgresql://user:password@host:5432/dbname
2. At the psql prompt, run:
   \d+ public.your_table_name
3. psql prints all columns, data types, modifiers, and comments.

Docs: https://www.postgresql.org/docs/current/app-psql.html

Pros: Very quick, rich detail, no extra setup.
Cons: Requires terminal access and comfort with CLI; not friendly for non-technical users.

1.2 Query information_schema.columns

The standard, portable way—great when you want to embed this into scripts or dashboards.

Run a query like:

SELECT column_name, data_type
FROM information_schema.columns
WHERE table_schema = 'public'
AND table_name = 'your_table_name'
ORDER BY ordinal_position;


Docs: https://www.postgresql.org/docs/current/infoschema-columns.html

Pros: ANSI-standard, works across many SQL engines; easy to filter or reuse.
Cons: Can be slower on very large schemas; gives minimal extras (no comments unless you join more).

1.3 Use system catalog pg_attribute for speed and depth

For power users or engineers, Postgres system catalogs are faster and more flexible.

SELECT
a.attname AS column_name,
a.atttypid::regtype AS data_type
FROM pg_attribute a
WHERE a.attrelid = 'public.your_table_name'::regclass
AND a.attnum > 0
AND NOT a.attisdropped
ORDER BY a.attnum;


Docs: https://www.postgresql.org/docs/current/catalog-pg-attribute.html

Pros: Very fast, works across versions, exposes advanced metadata.
Cons: Less familiar to many teams; not portable to other databases.

1.4 Include column descriptions with col_description

If your team documents columns with comments, surface them alongside names and types:

SELECT
c.column_name,
c.data_type,
col_description('public.your_table_name'::regclass,
c.ordinal_position) AS description
FROM information_schema.columns c
WHERE c.table_schema = 'public'
AND c.table_name = 'your_table_name'
ORDER BY c.ordinal_position;


Docs: https://www.postgresql.org/docs/current/functions-info.html

Pros: Perfect for building human-friendly data dictionaries.
Cons: Only as good as your existing comments; still manual to run.

1.5 Quick schema peek with a no-row SELECT

When you just need column headers fast, not data:

SELECT *
FROM public.your_table_name
WHERE false;


Most client tools will display the column names even though zero rows return.

Pros: Works almost anywhere; trivial SQL.
Cons: Not structured for reuse or documentation; no comments.

2. No-code and low-code methods with automation tools

As a business owner or marketer, you may live more in GUIs than in SQL. You can still get column names reliably without touching the terminal.

2.1 Use your BI or dashboard tool’s schema browser

Most BI tools (Metabase, Looker Studio connectors, Power BI, etc.) have a schema or field explorer.

Typical flow:

1. Connect the tool to your Postgres database.
2. Navigate to the dataset or table.
3. Open the “Fields”, “Columns”, or “Schema” panel.
4. Export or copy the list of columns into your documentation or Sheet.

Pros: Friendly UI; great for non-technical teammates.
Cons: Still manual; you’ll repeat this any time tables change.

2.2 Use no-code automation (Zapier/Make) with Postgres connectors

Many automation platforms offer native Postgres connectors.

A pattern you can use:

1. Create a new automation (Zap/Scenario/Flow).
2. Add a Postgres action such as “Custom Query”.
3. Paste the information_schema.columns query from section 1.2.
4. Map the result rows into a Google Sheet, Airtable base, or Notion database.
5. Schedule it to run daily or weekly.

Pros: Keeps a living schema inventory in the tools you already use.
Cons: Still requires maintaining queries; automations can silently fail without monitoring.

2.3 Embed schema queries in internal tools

If you have an internal admin panel (Retool, Appsmith, Budibase, etc.), add a “Schema Inspector” page:

1. Create a Postgres resource connection.
2. Add a query using information_schema.columns with input fields for schema and table.
3. Display results in a table UI with search and filters.

Pros: Central place for everyone to look up columns; fast self-serve.
Cons: Someone still has to build and maintain the page.

3. At-scale, automated workflows with AI agents (Simular)

Manual and low-code options work—until you’re juggling dozens of databases, clients, or schemas. This is where an AI computer agent like Simular Pro becomes a force multiplier.

Simular is a production-grade computer-use agent that can automate nearly any task a human can perform on a desktop or in the browser. That includes logging into database consoles, running SQL, exporting results, and updating documentation—reliably, thousands of steps at a time.

3.1 Simular agent that inventories schemas nightly

Imagine you run an agency managing analytics for 30 SaaS clients, each with its own Postgres instance.

Workflow:

1. Configure a Simular Pro agent with access to your database client (DBeaver, TablePlus, or a web-based console) and your central Google Sheet/Notion.
2. Teach it a standard playbook: for each client DB, connect, run the information_schema.columns query, export results.
3. The agent cleans and merges results into a master “Column Catalog” sheet, tagged by client, schema, and table.
4. Schedule the workflow nightly via Simular’s webhook integration.

Pros: Zero manual repetition; always-fresh column lists for every client.
Cons: Requires initial setup and secure credential management.

Learn more about Simular Pro’s capabilities: https://www.simular.ai/simular-pro

3.2 Agent that documents columns for business users

Now shift into storytelling: marketers and sales ops don’t care about customer_id; they care about “Customer ID used in CRM syncs.”

Workflow:

1. A Simular agent runs the col_description-based query from section 1.4 across key tables.
2. For columns without comments, the agent opens your analytics dashboards and CRM, infers usage patterns, and drafts plain-language descriptions.
3. It then logs into your documentation tool (Notion, Confluence, or a shared doc), updates a “Data Dictionary” page, and pings the team in Slack.

Pros: Business-readable documentation grows automatically alongside your schema.
Cons: Needs human review initially to calibrate tone and accuracy.

3.3 Agent-driven quality checks on column changes

Schema drift kills dashboards. Simular can watch for that.

Workflow:

1. On a schedule, the agent compares today’s information_schema.columns snapshot to yesterday’s.
2. If it detects new, renamed, or dropped columns, it writes a change log and alerts owners.
3. It can even open your BI tool, locate affected reports, and flag them for review.

Pros: Early warning whenever Postgres changes underneath your campaigns.
Cons: More advanced automation; best for teams with multiple live reports.

Because Simular agents operate with transparent, inspectable actions, you can see every SQL query they run and every cell they touch—critical when you’re dealing with customer data. Combined with Postgres’s rock-solid metadata views, this gives your business a living, trusted map of your data, without forcing your best people to live in the database all day.

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