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carto-explore-datawarehouse

carto-explore-datawarehouse

github.com/cartodbData & databases656 installsUpdated 1w ago
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About carto-explore-datawarehouse

carto-explore-datawarehouse is a skill for discovering connections, tables, columns, and named sources before querying or building maps. It uses CARTO MCP tools where available, with CLI commands as a fallback for restricted or scripted sessions.

Key features

  1. 01List and inspect connections
  2. 02Browse project, dataset, and table hierarchies
  3. 03Search for resources by text
  4. 04Inspect table columns and types
  5. 05Find and inspect named sources

Use cases

  1. 01Find tables before writing a query
  2. 02Check column types before authoring SQL
  3. 03Locate a named source referenced by name
  4. 04Explore data in a scripted or headless session

MCP support

It primarily uses CARTO MCP tools such as `explore_data` and `manage_named_sources` to inspect connections and resources. CARTO CLI commands provide a fallback when the MCP server is unavailable or the session lacks required scopes.

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/cartodb/agent-skills --skill carto-explore-datawarehouse

After installing, type/carto-explore-datawarehousein Claude Code to confirm it loaded.

Bundled filesreferences/connection-browse.mdreferences/named-sources.md

Details

From the project README

carto-explore-datawarehouse

Before writing SQL or building maps, an agent usually needs to know what's in the warehouse. This skill covers discovery, matched across MCP and CLI:

TaskMCP (primary)CLI (fallback)
List / inspect connectionsexplore_data (list_connections, get_connection)carto connections list / get
Walk the hierarchy (project → dataset → table)explore_data (list_resources)carto connections browse
Full-text find a resourceexplore_data (search)—
Columns + types for a tableexplore_data (describe)carto connections describe
Find / inspect a named sourcemanage_named_sources (list, get)carto named-sources list / get

Access-path routing. explore_data works on any attached MCP session, but not all its methods do on a token: list_connections, list_resources, and search need only the MCP Server scope, while describe additionally needs the Maps API on a token session (it's always available over OAuth). manage_named_sources is an authoring tool, so it needs an OAuth session (it's hidden on token sessions). Use the carto connections / carto named-sources CLI when the server isn't attached, when a token lacks the scope for describe, or when the exploration is scripted/headless. Detection signals and the full routing table: carto-basics/references/access-paths.md.

When to use this skill

  • You don't know which tables / schemas exist in a connection.
  • You need a column list and types before writing SQL or authoring a map.
  • The user references "the named source for X" and you need to find it.

If you already know the table and just want to query it, jump to carto-query-datawarehouse.

Path syntax by engine

The path passed to list_resources / describe (or connections browse / describe) depends on the engine:

EnginePath shape
BigQueryproject.dataset.table
SnowflakeDATABASE.SCHEMA.TABLE
Postgres / Redshiftschema.table (no leading project/database)
Databrickscatalog.schema.table

CLI examples:

carto connections browse <name>                                  # top level
carto connections browse <name> "carto-demo-data.demo_tables"    # drill in
carto connections describe <name> "carto-demo-data.demo_tables.nyc_collisions"

What's in this skill

TopicReference
Browsing and describing a connection in detailreferences/connection-browse.md
Named sources — what they are, how to list and inspect themreferences/named-sources.md

Always-on guidance

  • Browse before you query. A two-second list_resources usually saves a five-minute "table not found" loop.
  • describe returns column types — use them to write correct SQL (e.g. don't ST_DWithin against a STRING column mistakenly named geom).
  • Paginate long lists — CLI browse defaults to --page-size 30; bump it for datasets with hundreds of tables.
  • Named sources ≠ tables. They're parameterized queries — inspect the underlying tables before assuming a column you see in the source exists in raw form.
  • carto-demo-data is a public BigQuery dataset CARTO ships — browsing it works on any BigQuery connection with the right IAM, a fast way to validate a fresh connection without touching customer data.

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