Coupler.io

DEPRECATED. Use "list-datasets" instead. Reconnect to refresh the list of available tools if needed.

list-dataflows

List datasets across the user's dataflows. Returns up to 200 datasets plus the true total in meta.total_count — if total_count exceeds the number returned, you are seeing only the first 200; narrow with dataflow_id/filters, or use search-datasets when you know the name, source, or a keyword. Once you've picked a dataset, use get-schema to read its columns and get-data to query it. An empty result does not always mean a new workspace — a narrow dataflow_id/filter, or a search-datasets query, can return nothing too. The reliable new-user signal is no credentials (list-credentials empty); an empty unfiltered list-datasets is the backup. When both say the account is bare, don't report "no datasets" — fetch the get-started skill (get-skill) and guide the user's first setup.

list-datasets

List templates available in Coupler.io. Use this to discover available templates, optionally filtered by a specific source, metric or category, provide the link to set up the template and provide insights about their contents.

list-templates

Search Coupler.io skills for one that fits the user's request; following a skill gives better results than ad-hoc tool calls. Once you know what the user wants, call this before other Coupler.io tools, with `query` set to a few keywords such as the data source, domain or task (e.g. "stripe revenue", "google ads", "create dataflow"). The search covers skill names, descriptions, slugs, categories and data sources. Omit `query` only to browse every skill. Returns skill slugs and short descriptions. If one matches, even loosely, call get-skill with its slug and follow the procedure. Skip the search for pure platform questions and tiny direct reads.

list-skills

List the user's existing credentials (authorized connections) for source or destination integrations. Use this as a prerequisite check before create-dataflow-source or create-dataflow-destination — those calls need an active credential for the integration and fail without one. If none exists for the integration the user wants, do not stop at "you have no credentials": the response includes a setup URL for connecting a new credential — share it inline in your reply and guide the user through the connect round-trip (see the get-started skill; fetch it with get-skill if not already loaded). Pass a provider to get a setup URL that links straight to that provider's connect page. Returns each credential with its provider, type, and status.

list-credentials

List the source or destination integrations Coupler.io supports. Use this to find the integration key for the service the user wants, then pass that key to get-integration to learn its parameters and auth requirements before creating a source or destination. Pass type "source" or "destination" to scope the list.

list-integrations

Search for datasets by name, source, or general text query. Use this instead of list-datasets when looking for a specific dataset and you need to narrow down from a large list. Parameters can be combined — they filter with AND logic. Use get-data and get-schema on the results to inspect dataset contents.

search-datasets

Update dataset metadata: the name and/or the AI context (markdown description). Use ai_context to persist what you learned about the dataset contents so it can be recalled in future sessions; pass null to clear it. Record conventions and meaning rather than ready-to-run queries: a column's name can change when it is relabelled, so any column name written here is a snapshot — check it against get-schema before relying on it in a later session.

update-dataset

Update the SQL query of a customSql dataset. Only datasets with type `customSql` support query updates. In your SQL query, reference other datasets from the SAME dataflow by their `table_name` (returned by list-datasets/search-datasets/create-dataset). Use get-schema on each dataset snapshot to discover column names; take each `columnName` verbatim and wrap it in double quotes, since names routinely contain spaces, dots and punctuation. The query is validated and dataset columns are re-detected. It is stored and re-runs on every refresh, so alias output columns with `AS` to pin the names this dataset exposes. If a stored query later fails with "no such column", an upstream column was relabelled — re-read get-schema instead of guessing a variant. The query must be read-only — a `SELECT`, or a `WITH … SELECT`. `PIVOT` must use the SQL-standard form; the simplified `PIVOT … ON … USING` syntax is not supported. ALWAYS include the link (`preview_url`) in your response as it is the only way for users to run the query and verify data correctness.

update-dataset-query