mcp-server

• list_my_workspaces — List every workspace the authenticated user has access to, including their permission on each.
• create_workspace — Create a new workspace, owned by the authenticated user. Use this if list_my_workspaces returns none.
• list_data — List either the data specs (parsing + mapping rule sets, resource: "specs") or the data processing jobs (executions of a spec, resource: "jobs") defined in a wo
• get_status — Poll the status of either a data spec's own process (schema inference + code generation, run by start-analysis — pass specId, reaches "ready" or "failed") or a
• delete_data_spec — Permanently delete a data spec and its associated configuration.
• submit_query — Run a SQL query against the Iceberg tables loaded into a workspace. To list the tables that actually exist in the workspace, run `SHOW TABLES` — this is the aut
• manage_connection — Create, list, test, or delete a workspace connection to an external data source. Two types are supported: "sftp" and "aws_s3". For sftp, create generates a keyp
• manage_trigger — Create, list, update, delete, or fire a workspace job trigger. Four types: - "sftp"/"aws_s3": pulls files from a connection (sftp: remote server; aws_s3: S3 buc
• onboard_data_source — First step of setting up a new data integration: creates a data spec. By default (sourceType "file") this returns presigned upload URL(s) for the sample file (a
• finish_data_source_onboarding — Call after uploading the file(s) returned by onboard_data_source — kicks off AI analysis and waits until the spec reaches "ready" or "failed". If it returns bef
• update_data_spec — Change an existing data spec's configuration. If no replacement file names are given, this runs synchronously (no upload needed): saves changes and — by default
• finish_data_spec_update — Call after uploading the file(s) returned by update_data_spec — kicks off AI analysis and waits until the spec reaches "ready" or "failed". If it returns before

No long description beyond the summary.