--- name: semantic-model-1901f540a764 description: List, read, and interpret Microsoft Fabric Semantic Models (Power BI datasets). Use when an agent needs to understand available business metrics, DAX measures, table relationships, or column definitions before writing DAX queries, building reports, or validating Gold-layer outputs against business definitions. --- # semantic-model ## When to use Invoke before any task that depends on understanding business metric definitions: - Writing or reviewing a DAX query or measure - Checking whether a Gold-layer table covers a KPI already defined in a semantic model - Understanding join paths (relationships) before recommending aggregation logic - Validating that a Silver → Gold promotion matches the expected column names and types - Answering questions like "what does the Revenue measure include?" or "how is Churn Rate calculated?" ## Tools — `semantic_model_list` / `semantic_model_show` (MCP) Inspection runs server-side via the `fabric-server` MCP tools (backed by `sempy.fabric` in the container). There is no target-side script. Auth is handled by the container's service-principal credentials. | Tool | Arguments | Purpose | |---|---|---| | `semantic_model_list` | _(none — uses the configured `FABRIC_WORKSPACE_ID`)_ | List all semantic models in the workspace | | `semantic_model_show` | `model` (name or id); `include_hidden` (default off); `include_expressions` (default off — DAX may carry sensitive logic) | Show tables, columns, measures, and relationships for one model. Always returns JSON. | Call `semantic_model_list` first when the model name is unknown — never guess an ID. `semantic_model_show` hides DAX expressions and hidden objects by default; opt in with `include_expressions` / `include_hidden` only when needed. ## Output format ### list ``` Name ID Description --------------------------------------------------------------------------------------------------------- Sales Model a1b2c3d4-... Core revenue and margin KPIs ``` ### show ``` Semantic Model : Sales Model ID : a1b2c3d4-... Description : Core revenue and margin KPIs Table: Date col Date DateTime col Year Int64 col Month Int64 col MonthName String [calc] = FORMAT([Date], "MMMM") Table: Sales col OrderID Int64 col CustomerKey Int64 col Amount Decimal mea Total Revenue SUM(Sales[Amount]) [#,##0.00] // Gross before returns mea Net Revenue [Total Revenue] - [Returns] Relationships: Sales[CustomerKey] → Customer[CustomerKey] [BothDirections] Sales[DateKey] → Date[DateKey] ``` ## How to interpret | Symbol | Meaning | |---|---| | `col` | Regular or calculated column | | `mea` | DAX measure | | `[calc]` | Calculated column (DAX expression shown after `=`) | | `[hidden]` | Hidden from report view — internal use only | | `[inactive]` | Relationship exists but is not the active join path; use `USERELATIONSHIP()` in DAX | | `[BothDirections]` | Cross-filter propagates both ways — watch for ambiguous paths | ## What to derive from the output - **Measure expressions** are the authoritative business definitions. When a Gold table computes the same metric, the DAX and the PySpark logic must agree. - **Relationships** define valid join paths. A missing relationship is a schema gap; an inactive relationship requires explicit `USERELATIONSHIP()`. - **Hidden columns** are implementation details. Do not surface them in reports or Silver/Gold schemas. - **Data types** on columns must match what Bronze/Silver produce. A `Decimal` column fed an `Int64` source will silently truncate. ## MUST - Run `list` first when the model name is unknown — never guess an ID - Always confirm the model exists in this workspace before referencing its measures in code or documentation - Treat measure expressions as read-only — the semantic model is owned by the BI team, not the data engineering agent - For new Direct Lake models authored in this repo, keep TMDL under `workspace//semantic-model/.tmdl` as the source of truth and a sibling `README.md` with the Fabric UI creation walkthrough - Author TMDL with **only** columns that map to real Delta columns via `sourceColumn` — no calculated columns, no calculated tables (Direct Lake constraint) - Move any per-row derivation (`is_weekend`, `hour_of_day`, `price_date_lu`) **upstream into the silver or features notebook** so the model only consumes existing Delta columns ## AVOID - Modifying semantic models via REST API — use the Fabric UI or XMLA endpoint under human supervision - Assuming a measure in one model is defined identically in another — always inspect the target model directly - Declaring a calculated column in Direct Lake TMDL (`column 'X' = `) — it silently degrades the model to DirectQuery or fails the TMDL save - Calculated tables in Direct Lake — not supported - Row-level security using calculated-column predicates in Direct Lake — not supported ## Direct Lake authoring constraints When authoring a Direct Lake semantic model from a forecast / silver / gold Delta table: | Allowed in Direct Lake | Use for | |---|---| | Measures (pure DAX over the table) | All per-aggregation logic — `SUM`, `AVERAGE`, `CALCULATE(..., )` | | `sourceColumn` → `name` mapping | Renaming a Delta column to a friendly label without altering the underlying schema | | `formatString`, `summarizeBy`, `isHidden` | Display, summarization defaults, hiding lineage/tracking columns | | NOT allowed in Direct Lake | Workaround | |---|---| | Calculated columns (`column 'X' = `) | Add the column to the upstream Delta table in the silver or features notebook | | Calculated tables | Materialize the table upstream in a notebook and point the model at the resulting Delta table | | Calculated-column-based RLS | Move the predicate column into the Delta table; use measure-level filters in DAX | If the model needs `is_weekend`, `hour_of_day`, `price_date_lu`, etc., produce that column upstream and expose it through `sourceColumn` — never derive it inside the TMDL. See `memory/skill-fixes/directlake-no-calculated-columns.md` for the incident this rule comes from. ## TMDL repo workflow The `semantic-model` skill does NOT create or modify semantic models via REST API. The workflow is: 1. Author `.tmdl` and `README.md` under `workspace//semantic-model/`. 2. Human creates the Direct Lake model in the Fabric UI ("New semantic model" from the underlying lakehouse). 3. Human pastes the TMDL into the editor's TMDL view to apply column mappings and measures. 4. Subsequent runs of the upstream notebooks update the model automatically via Direct Lake — no re-paste needed unless TMDL changes. --- Source attribution: scardoso-lu Original project: https://github.com/scardoso-lu/fabric-skills-settings/blob/33534eb185fda573125308a0129457bd65419d13/server/skills/semantic-model/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2026 Silvio Cardoso Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: scardoso-lu Original project: https://github.com/scardoso-lu/fabric-skills-settings/blob/33534eb185fda573125308a0129457bd65419d13/server/skills/semantic-model/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Marketing, Product Management Roles: Data Analyst, Marketing Analyst, Product Manager Specialties: API Tools: Power Bi Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: a87e384888ea97999f9f3916e38ed379b794eaeb45ed546ac4d75dc30e636adf