PBI Bootstrap
SkillProductivityUse when starting a SemanticOps MCP session, connecting to a Power BI model, checking the current connection, applying saved preferences, composing bulk or write payloads, or recovering from empty results, stale metadata, or argument validation errors. For task work after the session is healthy, use the matching mcp-engine skill (query, schema-authoring, semantic-authoring, testing-changes, security-governance).
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the PBI Bootstrap skill
What this skill tells your AI
The instructions your AI receives, as published by maxanatsko/mcp-engine-public in skills/mcp-engine-bootstrap/SKILL.md and read by ahel’s review.
Session setup and recovery for SemanticOps MCP. Use the bundled references as the working documentation.
Quick Start
- Check the connection:
manage_model_connection{ "operation": "get_current" }. - If no model is connected — Desktop:
{ "operation": "list" }, present the choices, then{ "operation": "select", "model_id": "<model_id>" }with the user's pick. Service:{ "operation": "authenticate", "source": "service" }first when needed, then{ "operation": "list_workspaces" }for the workspace choice, then{ "operation": "list", "source": "service", "workspace": "<workspace>" }to list its datasets, and finallyselectwith the returnedmodel_id(workspaces themselves are not selectable). - Load saved preferences with
manage_preferences{ "action": "list", "resource": "rendered" }and apply them within their stated trust boundary. - Read tool-invocation-conventions before composing any non-trivial write or bulk request.
- If a tool returns empty or stale results, switch to troubleshooting-guide before retrying.
Route to the right tool family
list_model(list,search,analyze,info,report) for discovery, search, previews, and metadata inspection.run_query(execute,analyze,vertipaq,test_access) for DAX execution, performance analysis, storage inspection, and RLS checks.- Write tools (
manage_schema,manage_semantic,manage_security) only after the target object and operation are explicit. - Once connected and healthy, hand off to the task skill: query authoring →
mcp-engine-query; slow queries →mcp-engine-dax-performance; wrong values →mcp-engine-dax-debugging; tables, columns, relationships, partitions →mcp-engine-schema-authoring; measures and calc groups →mcp-engine-semantic-authoring; multi-object refactors →mcp-engine-refactoring; tests, checkpoints, rollback →mcp-engine-testing-changes; RLS, policy, masking, audit →mcp-engine-security-governance. If a named skill is not installed, continue with the tool'sinputSchemaor ask the user to add it.
Guard bulk and write requests
- Keep
transaction,dry_run,include_items, andinclude_detailsas top-level request controls. - Put per-item identifiers and
specvalues inside each item; items do not inherit from the request. - Confirm destructive intent before deletes, renames, broad refreshes, or model-wide rewrites.
Recover from common failures
- Wrong or stale connection state: re-check with
{ "operation": "get_current" }, then{ "operation": "reload" }after external model changes. - Object seems missing: broaden discovery with
list_model{ "operation": "search", "spec": { "query": "<object name>", "mode": "name" } }(spec.queryis required) before concluding it does not exist. - Argument validation errors: fix key names and payload shape per tool-invocation-conventions before changing business logic.
- Full recovery flows: troubleshooting-guide.
Signals
- GitHub stars
- 256
- Forks
- 65
- Last commit
- Aug 2026
Advanced
- Catalog kind
- skill
- Gateway key
mcp-engine-bootstrap- Source
- github.com/maxanatsko/mcp-engine-public