Entry point for building a Glean connector. Returns an opening prompt that orients the AI and asks the user what data source they want to connect. Call this before any other tool.

Scaffold a new Glean connector project using the standard template. Call this after get_started. Sets the active project directory for this session.

Parse a data file (.csv, .json, .ndjson) and return field analysis: detected types, null rates, cardinality, and sample values. Use this to understand the source data before defining mappings.

Read the current field schema from .glean/schema.json.

Write field definitions to .glean/schema.json. Call this after infer_schema to save the agreed schema, or to make manual edits. Set merge: true to merge incoming fields with the existing schema (fields with the same name are replaced; new names are appended) instead of replacing the entire field list.

Deep-dive on a single field from the current schema: samples, type details, and Glean mapping suggestions.

Return the current source schema alongside the Glean entity model so you can decide which source field maps to which Glean field.

Save field mapping decisions to .glean/mappings.json. Merges with any existing mappings.

Check current mappings against Glean's entity model. Reports missing required fields and type mismatches.

Read the current connector configuration from .glean/config.json.

Write connector configuration to .glean/config.json. Merges with existing config. Code-generation keys (consumed by build_connector): name, display_name, datasource_category, url_regex, icon_url, connector_type. Runtime-only keys (used by the connector at execution time, not during code generation): auth_type, endpoint, api_key_header, page_size, rate_limit_rps.

Generate Python connector files from schema + mappings + config. Use dry_run: true to preview the generated code without writing files.

Start async execution of the Python connector. Returns an execution_id immediately. Poll status with inspect_execution.

Check execution status and retrieve records. Returns status, records fetched, per-record validation results, and recent logs.

Manage connector recordings. action: "record" saves fetched data, "replay" runs from a saved file, "list" shows available recordings, "delete" removes one.

List all connector classes found in this project with their module paths.

Read data_client.py for a module — use before asking AI to implement real API calls.

Write a new data_client.py implementation (replaces the mock with real API calls).

Check that all required tools and credentials are installed and configured.