How do I install the MCP Engine Bootstrap skill?

Run npx skillmds@latest add maxanatsko/mcp-engine-bootstrap in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is MCP Engine Bootstrap safe to use?

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Which AI agents work with MCP Engine Bootstrap?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is MCP Engine Bootstrap free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published MCP Engine Bootstrap?

maxanatsko (@maxanatsko) published this skill. Their other Agent Skills are listed on their SkillMD profile.

MCP Engine Bootstrap by maxanatsko — Agent Skill

Name: MCP Engine Bootstrap
Rating: 4.2 (5 reviews)
Author: maxanatsko

MCP Engine Bootstrap

Use 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).

maxanatsko 2b7ddd1 4 files · 39.7 KB Updated Sep 10, 2026

File contents

PBI Bootstrap

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 finally select with the returned model_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's inputSchema or ask the user to add it.

Guard bulk and write requests

Keep transaction, dry_run, include_items, and include_details as top-level request controls.
Put per-item identifiers and spec values 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.query is 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.

---name: mcp-engine-bootstrapdescription: Use 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).---# PBI BootstrapSession setup and recovery for SemanticOps MCP. Use the bundled references as the working documentation.## Quick Start1. Check the connection: `manage_model_connection` `{ "operation": "get_current" }`.2. 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 finally `select` with the returned `model_id` (workspaces themselves are not selectable).3. Load saved preferences with `manage_preferences` `{ "action": "list", "resource": "rendered" }` and apply them within their stated trust boundary.4. Read [tool-invocation-conventions](references/tool-invocation-conventions.md) before composing any non-trivial write or bulk request.5. If a tool returns empty or stale results, switch to [troubleshooting-guide](references/troubleshooting-guide.md) 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's `inputSchema` or ask the user to add it.## Guard bulk and write requests- Keep `transaction`, `dry_run`, `include_items`, and `include_details` as top-level request controls.- Put per-item identifiers and `spec` values 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.query` is required) before concluding it does not exist.- Argument validation errors: fix key names and payload shape per [tool-invocation-conventions](references/tool-invocation-conventions.md) before changing business logic.- Full recovery flows: [troubleshooting-guide](references/troubleshooting-guide.md).

Frequently asked questions