How do I install the MCP Engine AI Readiness skill?

Run npx skillmds@latest add maxanatsko/mcp-engine-ai-readiness 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.

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Which AI agents work with MCP Engine AI Readiness?

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Is MCP Engine AI Readiness free to use?

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

Who published MCP Engine AI Readiness?

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

MCP Engine AI Readiness by maxanatsko

Name: MCP Engine AI Readiness
Rating: 4.666666666666667 (3 reviews)
Author: maxanatsko

MCP Engine AI Readiness

Use when preparing or assessing a Power BI semantic model for Copilot, Fabric data agents, or natural-language Q&A — clear business terminology, unambiguous metrics, usable date defaults, focused field exposure, descriptions, AI instructions, AI data schema recommendations, verified-answer candidates, or natural-language validation tests. For general modeling quality unrelated to AI consumption, use mcp-engine-model-quality.

maxanatsko 24fd6fb 6 files · 20.8 KB Updated Sep 10, 2026

File contents

PBI AI Readiness

Use this skill to turn an existing Power BI semantic model into a Copilot-ready assessment and artifact pack. This is an authoring and review workflow, not a runtime MCP tool.

Start Here

Confirm the user wants a readiness assessment, artifact drafts, or both.
Prefer metadata-level inspection before querying data values.
Use existing SemanticOps MCP tools when available: list_model, manage_dependencies, run_query, manage_tests, and manage_model_properties.
Keep unsupported Prep data for AI actions as drafts for Power BI Desktop, Power BI service, PBIP, Git, or manual review.
Separate recommendations into:
- can apply through MCP/model metadata now
- draft/export for Prep data for AI UI or PBIP/Git workflow
- validate manually in Copilot

Workflow

Read copilot-readiness-workflow for the assessment sequence, tool usage, privacy guardrails, and output order.
Read readiness-scorecard when producing severity, score, business impact, and remediation priority.
Read ai-artifact-templates when drafting AI instructions, AI data schema recommendations, verified answers, or manage_tests candidates.
Read domain-examples when the model is sales, finance, support, or operational and the user wants concrete starting examples.

Guardrails

Do not claim SemanticOps MCP can directly configure all Power BI Prep data for AI settings over live TOM/XMLA.
Treat AI instructions, AI data schemas, and verified answers as draft artifacts unless the user provides an explicit supported PBIP/Git path or asks for manual-application guidance.
Do not expose sensitive values from data previews. Prefer names, descriptions, expressions, relationships, dependencies, and aggregate-only validation queries.
Respect SemanticOps MCP mode, policy, confirmation, license, and audit gates for any suggested or requested model change.
Make nondeterminism explicit: readiness work can improve Copilot behavior, but it cannot guarantee identical answers for every prompt.

Output Standard

Return a compact readiness pack unless the user asks for raw details:

Executive summary with readiness level.
Scorecard grouped by critical, high, medium, and low findings.
Recommended MCP-applicable model metadata fixes.
Draft AI instructions.
Draft AI data schema recommendation.
Verified-answer backlog.
Optional natural-language test suggestions.
Manual validation checklist for Power BI Desktop or service.

---name: mcp-engine-ai-readinessdescription: Use when preparing or assessing a Power BI semantic model for Copilot, Fabric data agents, or natural-language Q&A — clear business terminology, unambiguous metrics, usable date defaults, focused field exposure, descriptions, AI instructions, AI data schema recommendations, verified-answer candidates, or natural-language validation tests. For general modeling quality unrelated to AI consumption, use mcp-engine-model-quality.---# PBI AI ReadinessUse this skill to turn an existing Power BI semantic model into a Copilot-ready assessment and artifact pack. This is an authoring and review workflow, not a runtime MCP tool.## Start Here1. Confirm the user wants a readiness assessment, artifact drafts, or both.2. Prefer metadata-level inspection before querying data values.3. Use existing SemanticOps MCP tools when available: `list_model`, `manage_dependencies`, `run_query`, `manage_tests`, and `manage_model_properties`.4. Keep unsupported Prep data for AI actions as drafts for Power BI Desktop, Power BI service, PBIP, Git, or manual review.5. Separate recommendations into: - can apply through MCP/model metadata now - draft/export for Prep data for AI UI or PBIP/Git workflow - validate manually in Copilot## Workflow- Read [copilot-readiness-workflow](references/copilot-readiness-workflow.md) for the assessment sequence, tool usage, privacy guardrails, and output order.- Read [readiness-scorecard](references/readiness-scorecard.md) when producing severity, score, business impact, and remediation priority.- Read [ai-artifact-templates](references/ai-artifact-templates.md) when drafting AI instructions, AI data schema recommendations, verified answers, or `manage_tests` candidates.- Read [domain-examples](references/domain-examples.md) when the model is sales, finance, support, or operational and the user wants concrete starting examples.## Guardrails- Do not claim SemanticOps MCP can directly configure all Power BI Prep data for AI settings over live TOM/XMLA.- Treat AI instructions, AI data schemas, and verified answers as draft artifacts unless the user provides an explicit supported PBIP/Git path or asks for manual-application guidance.- Do not expose sensitive values from data previews. Prefer names, descriptions, expressions, relationships, dependencies, and aggregate-only validation queries.- Respect SemanticOps MCP mode, policy, confirmation, license, and audit gates for any suggested or requested model change.- Make nondeterminism explicit: readiness work can improve Copilot behavior, but it cannot guarantee identical answers for every prompt.## Output StandardReturn a compact readiness pack unless the user asks for raw details:1. Executive summary with readiness level.2. Scorecard grouped by critical, high, medium, and low findings.3. Recommended MCP-applicable model metadata fixes.4. Draft AI instructions.5. Draft AI data schema recommendation.6. Verified-answer backlog.7. Optional natural-language test suggestions.8. Manual validation checklist for Power BI Desktop or service.

Frequently asked questions