List workbooks open in Excel and models open in Power BI Desktop (both usable as source), and explain the other source forms.

Every query with its kind (query/function/parameter), step count, load destination, and what it depends on / is used by.

Full M code of one query plus its parsed steps, data sources, dependencies and dependents.

Query-to-query lineage. With query, only its upstream and downstream. format: 'json' or 'mermaid'. Also reports roots (read external data), leaves (final outputs), orphans and cyclic references.

Inventory of every external source (files, folders, servers, URLs) each query reads, and whether it is hard-coded or parameterised.

Search the M of every query. scope: all | code | strings | comments. Returns query, step and line for each hit.

Audit queries for folding breakers, hard-coded paths/servers, dead steps, brittle column lists, untyped columns, duplicated logic, orphans and cycles. Each finding carries a concrete suggestion. min_severity: info | warning | error.

Replace a query's M (or create it with create_if_missing). Rejects unbalanced brackets/strings before writing. For standard actions prefer apply_transform; for renames prefer rename_query / rename_step.

Delete a query. Refuses when other queries reference it unless force=true.

Rename a query and rewrite every reference to it in all other queries (token-aware: strings, comments and same-named columns are left alone).

Rename a step inside a query and every reference to it.

Delete a step and rewire the steps that used it to the step it read from.

Find and replace across all (or the listed) queries in one go, e.g. repoint a server or folder. scope: 'strings' (only inside text literals; safest, the default), 'code' (outside strings and comments), 'all'.

Turn a hard-coded text value (path, server, URL) into a Power Query parameter and reference it everywhere. With match_prefix, "C:\Data" also rewrites "C:\Data\sales.csv" to SourceFolder & "\sales.csv".

Catalog of guided actions for apply_transform (unpivot, pivot, group_by, merge, filter, types, ...) with their params.

Add a standard Power Query step without hand-writing M. action and params come from list_transforms, e.g. action='unpivot_other_columns', params={'keep_columns': ['Region']}; or action='group_by', params={'group_columns': ['Region'], 'aggregations': [{'name': 'Total', 'operation': 'sum', 'column': 'Amount'}]}. The step is appended to the end unless after_step names an earlier step (the next step is rewired, as the editor does).

Copy queries between workbooks / projects / folders, bringing the queries they depend on along.

Write every query to folder as one .pq file each plus pq-manifest.json: ready to commit to git and diff in a PR. Existing .pq files in the folder are replaced.

Load .pq files from folder back into source. mode='update' changes/adds queries; mode='sync' also deletes queries that are not in the folder.

Compare the queries of two sources (workbook vs workbook, workbook vs exported folder, PBIP vs PBIP ...). Line endings are ignored. To compare against a snapshot pass its path from list_snapshots.

Snapshots taken automatically before each write to this source, newest first.

Roll back to a snapshot. With queries, only those are restored; otherwise the whole query set is put back (queries created since are deleted). 'latest' undoes the most recent write.

Check M before using it: syntax errors with line/column and a plain-English cause (missing comma between steps, unquoted step name, if without else ...), unknown steps/queries/functions with did-you-mean, and wrong argument counts for library functions. Pass formula (an expression or a whole section document); add source so its query names are known. Or pass source (+ optional query) alone to validate what is already there.

Pretty-print M (an expression or a section document): one step per line, long calls broken at their arguments, comments kept. The output is verified token-for-token against the input, so meaning can never change.

Format one query, or every query, in place (same guarantees as format_m).

Look up the M standard library (generated from the real engine: 850+ members). Give an exact name ('Table.Group') for its signature and description, a namespace ('Table.' or 'List') to list its members, or words to search names and descriptions ('unpivot', 'split text delimiter'). Use it instead of guessing signatures.

Run any M expression and get the result as data. Tables come back with column names, types and the first rows rows (cell errors shown as #ERROR); lists, records, scalars and functions (with signature) are described too; an M error comes back as reason + message. With source, the expression can reference that source's queries by name (e.g. Table.RowCount(Sales)), including unsaved edits of an open workbook. count_rows and profile (Table.Profile: min/max/nulls/distinct per column) scan the whole result, so they can be slow. ignore_privacy_levels=true disables the privacy firewall for this throwaway evaluation only.

Freshly evaluate a query, or the query only up to step, and return columns, types and sample rows. Use it to get real column names before apply_transform, to see what a step does to the data, or to find the step where an error first appears (evaluate step by step).

Evaluate EVERY step of a query in one engine call and describe each one (columns, types, first rows rows, or the error). The fastest way to see how a query transforms its data and to find the step where an error first appears: one call instead of evaluate_query once per step.

Refresh one query in an open Excel workbook and wait for it; returns the mashup engine's error text if it fails.

Column names and first rows of a query that is loaded to a worksheet table (open Excel workbook only). Use it to get real column names before apply_transform.