mcp-engine-ai-readines…
Use when preparing or assessing a Power BI semantic model for Copilot, Fabric data agents, or natural-language Q&A — clear…
Use when creating, updating, renaming, or deleting tables, columns, calculated columns, relationships, hierarchies, calendars, or partitions, or changing refresh strategy and incremental refresh policy. For measures, calculation groups, or named expressions, use
$ npx -y skills add maxanatsko/mcp-engine-public --skill mcp-engine-schema-authoring --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/mcp-engine-schema-authoringContext preview
The summary Claude sees to decide when to auto-load this skill.
Use when creating, updating, renaming, or deleting tables, columns, calculated columns, relationships, hierarchies, calendars, or partitions, or changing refresh strategy and incremental refresh policy. For measures, calculation groups, or named expressions, use
name: mcp-engine-schema-authoring description: Use when creating, updating, renaming, or deleting tables, columns, calculated columns, relationships, hierarchies, calendars, or partitions, or changing refresh strategy and incremental refresh policy. For measures, calculation groups, or named expressions, use mcp-engine-semantic-authoring; for RLS roles or perspectives, use mcp-engine-security-governance; for multi-object refactors or renames with downstream consumers, use mcp-engine-refactoring.
Physical model structure work through `manage_schema`. Keep edits explicit, ordered, and dependency-aware.
1. Inspect current state: `list_model` `{ "operation": "list", "spec": { "type": "tables" } }`, then repeat for `columns` and `relationships` (`spec.type` takes exactly one value per call). 2. Before renaming or deleting, check consumers: `manage_dependencies` `{ "operation": "used_by", "spec": { "target": { "type": "column", "table": "<table>", "name": "<column>" } } }` (`spec.target` is required on every call, with the object's singular type); `{ "operation": "summary", "spec": { "target": ... } }` to gauge impact breadth. 3. For multi-object or risky edits, pin a rollback point first: `manage_model_changes` `{ "operation": "pin_checkpoint", "name": "before-<edit>" }` (`name` is required), and prefer `dry_run: true` on the write. 4. Apply the `manage_schema` operation (`create_table`, `update_column_properties`, `create_calc_column`, `create_relationship`, `create_hierarchy`, `create_partition`, `refresh_partition`, …) with per-item identifiers inside each bulk item. 5. Validate after the edit: a scoped `run_query` `{ "operation": "execute", "query": "<validation query>" }` that exercises the changed relationship, column, or partition.
After schema work, report:
1. Each operation applied, with table and target names. 2. Whether `dry_run` or `transaction` was used and the outcome. 3. Dependency-check results for renamed or deleted objects. 4. The validation query run and its result. 5. The checkpoint or transaction available for rollback.
The SemanticOps MCP (formerly MCP Engine) is a Power BI tool that lets AI assistants like Claude interact with your Power BI models programmatically: read your model structure, run DAX queries, modify your semantic model, and perform advanced analytics - all through natural conversation.
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