[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-microsoft-sqldw-consumption-cli":3,"mdc-cffj4e-key":37,"related-org-microsoft-sqldw-consumption-cli":3508,"related-repo-microsoft-sqldw-consumption-cli":3701},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":11,"stars":26,"repoUrl":27,"updatedAt":28,"license":29,"forks":30,"topics":31,"repo":32,"sourceUrl":35,"mdContent":36},"sqldw-consumption-cli","query Fabric Data Warehouse with T-SQL","Execute read-only T-SQL queries against Fabric Data Warehouse, Lakehouse SQL Endpoints, and Mirrored Databases via the MCP `fabric-sqlendpoint-execute_query` tool. Default skill for any lakehouse data query (row counts, SELECT, filtering, aggregation) unless the user explicitly requests PySpark or Spark DataFrames. Use when the user wants to: (1) query warehouse\u002Flakehouse data, (2) count rows or explore lakehouse tables, (3) discover schemas\u002Fcolumns, (4) generate T-SQL scripts, (5) monitor SQL performance, (6) export results to CSV\u002FJSON. For a Fabric notebook cell (%%sql or other notebook magics), use `spark-authoring-cli`, not this skill. Triggers: \"warehouse\", \"SQL query\", \"T-SQL\", \"query warehouse\", \"show warehouse tables\", \"show lakehouse tables\", \"query lakehouse\", \"lakehouse table\", \"how many rows\", \"count rows\", \"SQL endpoint\", \"describe warehouse schema\", \"generate T-SQL script\", \"warehouse performance\", \"export SQL data\", \"connect to warehouse\", \"lakehouse data\", \"explore lakehouse\".\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},"microsoft","Microsoft","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Fmicrosoft.png",[12,14,17,20,23],{"name":9,"slug":8,"type":13},"tag",{"name":15,"slug":16,"type":13},"Database","database",{"name":18,"slug":19,"type":13},"Microsoft Fabric","microsoft-fabric",{"name":21,"slug":22,"type":13},"Analytics","analytics",{"name":24,"slug":25,"type":13},"SQL","sql",858,"https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fskills-for-fabric","2026-07-31T05:55:04.037438",null,237,[],{"repoUrl":27,"stars":26,"forks":30,"topics":33,"description":34},[],"A collection of skills and MCP systems to enable users of CLI, VSCode, Claude to operate over Microsoft Fabric","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fskills-for-fabric\u002Ftree\u002FHEAD\u002Fskills\u002Fsqldw-consumption-cli","---\nname: sqldw-consumption-cli\ndescription: >\n  Execute read-only T-SQL queries against Fabric Data Warehouse, Lakehouse SQL Endpoints, and Mirrored Databases\n  via the MCP `fabric-sqlendpoint-execute_query` tool. Default skill for any lakehouse data query (row counts, SELECT, filtering,\n  aggregation) unless the user explicitly requests PySpark or Spark DataFrames. Use when the user wants to:\n  (1) query warehouse\u002Flakehouse data, (2) count rows or explore lakehouse tables, (3) discover schemas\u002Fcolumns,\n  (4) generate T-SQL scripts, (5) monitor SQL performance, (6) export results to CSV\u002FJSON. For a Fabric notebook cell (%%sql or\n  other notebook magics), use `spark-authoring-cli`, not this skill.\n  Triggers: \"warehouse\", \"SQL query\", \"T-SQL\", \"query warehouse\", \"show warehouse tables\",\n  \"show lakehouse tables\", \"query lakehouse\", \"lakehouse table\", \"how many rows\", \"count rows\",\n  \"SQL endpoint\", \"describe warehouse schema\", \"generate T-SQL script\", \"warehouse performance\",\n  \"export SQL data\", \"connect to warehouse\", \"lakehouse data\", \"explore lakehouse\".\n---\n\n> **Update Check — ONCE PER SESSION (mandatory)**\n> The first time this skill is used in a session, run the **check-updates** skill before proceeding.\n> - **GitHub Copilot CLI \u002F VS Code**: invoke the `check-updates` skill.\n> - **Claude Code \u002F Cowork \u002F Cursor \u002F Windsurf \u002F Codex**: compare local vs remote package.json version.\n> - Skip if the check was already performed earlier in this session.\n\n> **CRITICAL NOTES**\n> 1. To find the workspace details (including its ID) from workspace name: list all workspaces and, then, use JMESPath filtering\n> 2. To find the item details (including its ID) from workspace ID, item type, and item name: list all items of that type in that workspace and, then, use JMESPath filtering\n\n# SQL Endpoint Consumption — CLI Skill\n\n> **⚠️ SQL Execution Override:** For SQL data-plane execution, this skill supersedes COMMON-CLI SQL\u002FTDS guidance. Use MCP `fabric-sqlendpoint-execute_query` (see [Tool Stack](#tool-stack)) unless explicitly using Legacy CLI Fallback.\n\n## Table of Contents\n\n| Task | Reference | Notes |\n|---|---|---|\n| Finding Workspaces and Items in Fabric | [COMMON-CLI.md § Finding Workspaces and Items in Fabric](..\u002F..\u002Fcommon\u002FCOMMON-CLI.md#finding-workspaces-and-items-in-fabric) | **Mandatory** — *READ link first* [needed for finding workspace id by its name or item id by its name, item type, and workspace id]|\n| Fabric Topology & Key Concepts | [COMMON-CORE.md § Fabric Topology & Key Concepts](..\u002F..\u002Fcommon\u002FCOMMON-CORE.md#fabric-topology--key-concepts) ||\n| Environment URLs | [COMMON-CORE.md § Environment URLs](..\u002F..\u002Fcommon\u002FCOMMON-CORE.md#environment-urls) ||\n| Authentication & Token Acquisition | [COMMON-CORE.md § Authentication & Token Acquisition](..\u002F..\u002Fcommon\u002FCOMMON-CORE.md#authentication--token-acquisition) | Wrong audience = 401; read before any auth issue |\n| Core Control-Plane REST APIs | [COMMON-CORE.md § Core Control-Plane REST APIs](..\u002F..\u002Fcommon\u002FCOMMON-CORE.md#core-control-plane-rest-apis) | Includes pagination, LRO polling, and rate-limiting patterns |\n| OneLake Data Access | [COMMON-CORE.md § OneLake Data Access](..\u002F..\u002Fcommon\u002FCOMMON-CORE.md#onelake-data-access) | Requires `storage.azure.com` token, not Fabric token |\n| Job Execution | [COMMON-CORE.md § Job Execution](..\u002F..\u002Fcommon\u002FCOMMON-CORE.md#job-execution) ||\n| Capacity Management | [COMMON-CORE.md § Capacity Management](..\u002F..\u002Fcommon\u002FCOMMON-CORE.md#capacity-management) ||\n| Gotchas, Best Practices & Troubleshooting | [COMMON-CORE.md § Gotchas, Best Practices & Troubleshooting](..\u002F..\u002Fcommon\u002FCOMMON-CORE.md#gotchas-best-practices--troubleshooting) ||\n| Tool Selection Rationale | [COMMON-CLI.md § Tool Selection Rationale](..\u002F..\u002Fcommon\u002FCOMMON-CLI.md#tool-selection-rationale) ||\n| Authentication Recipes | [COMMON-CLI.md § Authentication Recipes](..\u002F..\u002Fcommon\u002FCOMMON-CLI.md#authentication-recipes) | `az login` flows and token acquisition |\n| Fabric Control-Plane API via `az rest` | [COMMON-CLI.md § Fabric Control-Plane API via az rest](..\u002F..\u002Fcommon\u002FCOMMON-CLI.md#fabric-control-plane-api-via-az-rest) | **Always pass `--resource`**; includes pagination and LRO helpers |\n| OneLake Data Access via `curl` | [COMMON-CLI.md § OneLake Data Access via curl](..\u002F..\u002Fcommon\u002FCOMMON-CLI.md#onelake-data-access-via-curl) | Use `curl` not `az rest` (different token audience) |\n| SQL \u002F TDS Data-Plane Access | [SKILL.md § Tool Stack](#tool-stack) | `fabric-sqlendpoint-execute_query` MCP tool — replaces sqlcmd |\n| Job Execution (CLI) | [COMMON-CLI.md § Job Execution](..\u002F..\u002Fcommon\u002FCOMMON-CLI.md#job-execution) ||\n| OneLake Shortcuts | [COMMON-CLI.md § OneLake Shortcuts](..\u002F..\u002Fcommon\u002FCOMMON-CLI.md#onelake-shortcuts) ||\n| Capacity Management (CLI) | [COMMON-CLI.md § Capacity Management](..\u002F..\u002Fcommon\u002FCOMMON-CLI.md#capacity-management) ||\n| Composite Recipes | [COMMON-CLI.md § Composite Recipes](..\u002F..\u002Fcommon\u002FCOMMON-CLI.md#composite-recipes) ||\n| Gotchas & Troubleshooting (CLI-Specific) | [COMMON-CLI.md § Gotchas & Troubleshooting (CLI-Specific)](..\u002F..\u002Fcommon\u002FCOMMON-CLI.md#gotchas--troubleshooting-cli-specific) | `az rest` audience, shell escaping, token expiry |\n| Quick Reference | [COMMON-CLI.md § Quick Reference](..\u002F..\u002Fcommon\u002FCOMMON-CLI.md#quick-reference) | `az rest` template + token audience\u002Ftool matrix |\n| Item-Type Capability Matrix | [SQLDW-CONSUMPTION-CORE.md § Item-Type Capability Matrix](..\u002F..\u002Fcommon\u002FSQLDW-CONSUMPTION-CORE.md#item-type-capability-matrix) | **Read first** — shows what's read-only (SQLEP) vs read-write (DW) |\n| Connection Fundamentals | [SQLDW-CONSUMPTION-CORE.md § Connection Fundamentals](..\u002F..\u002Fcommon\u002FSQLDW-CONSUMPTION-CORE.md#connection-fundamentals) | TDS, port 1433, Entra-only, no MARS |\n| Supported T-SQL Surface Area (Consumption Focus) | [SQLDW-CONSUMPTION-CORE.md § Supported T-SQL Surface Area](..\u002F..\u002Fcommon\u002FSQLDW-CONSUMPTION-CORE.md#supported-t-sql-surface-area-consumption-focus) | **Read before writing T-SQL** — includes data types (no `nvarchar`\u002F`datetime`\u002F`money`) |\n| Read-Side Objects You Can Create | [SQLDW-CONSUMPTION-CORE.md § Read-Side Objects You Can Create](..\u002F..\u002Fcommon\u002FSQLDW-CONSUMPTION-CORE.md#read-side-objects-you-can-create) | Views, TVFs, scalar UDFs, procedures |\n| Temporary Tables | [SQLDW-CONSUMPTION-CORE.md § Temporary Tables](..\u002F..\u002Fcommon\u002FSQLDW-CONSUMPTION-CORE.md#temporary-tables) | Use `DISTRIBUTION = ROUND_ROBIN` for INSERT INTO SELECT support |\n| Cross-Database Queries | [SQLDW-CONSUMPTION-CORE.md § Cross-Database Queries](..\u002F..\u002Fcommon\u002FSQLDW-CONSUMPTION-CORE.md#cross-database-queries) | 3-part naming, same workspace |\n| Security for Consumption | [SQLDW-CONSUMPTION-CORE.md § Security for Consumption](..\u002F..\u002Fcommon\u002FSQLDW-CONSUMPTION-CORE.md#security-for-consumption) | GRANT\u002FDENY, RLS, CLS, DDM |\n| Monitoring and Diagnostics | [SQLDW-CONSUMPTION-CORE.md § Monitoring and Diagnostics](..\u002F..\u002Fcommon\u002FSQLDW-CONSUMPTION-CORE.md#monitoring-and-diagnostics) | Includes query labels; DMVs (live) + `queryinsights.*` (30-day history) |\n| Performance: Best Practices and Troubleshooting | [SQLDW-CONSUMPTION-CORE.md § Performance: Best Practices and Troubleshooting](..\u002F..\u002Fcommon\u002FSQLDW-CONSUMPTION-CORE.md#performance-best-practices-and-troubleshooting) | Statistics, caching, clustering, query tips |\n| REST API: Refresh SQL Endpoint Metadata | [SQLDW-CONSUMPTION-CORE.md § REST API: Refresh SQL Endpoint Metadata](..\u002F..\u002Fcommon\u002FSQLDW-CONSUMPTION-CORE.md#rest-api-refresh-sql-endpoint-metadata) | Force metadata sync when SQLEP data is stale after ETL |\n| System Catalog Queries (Metadata Exploration) | [SQLDW-CONSUMPTION-CORE.md § System Catalog Queries](..\u002F..\u002Fcommon\u002FSQLDW-CONSUMPTION-CORE.md#system-catalog-queries-metadata-exploration) | `sys.tables`, `sys.columns`, `sys.views`, `sys.stats` |\n| Common Consumption Patterns (End-to-End Examples) | [SQLDW-CONSUMPTION-CORE.md § Common Consumption Patterns](..\u002F..\u002Fcommon\u002FSQLDW-CONSUMPTION-CORE.md#common-consumption-patterns-end-to-end-examples) | Reporting views, cross-DB analytics, temp table staging |\n| Gotchas and Troubleshooting Reference | [SQLDW-CONSUMPTION-CORE.md § Gotchas and Troubleshooting Reference](..\u002F..\u002Fcommon\u002FSQLDW-CONSUMPTION-CORE.md#gotchas-and-troubleshooting-reference) | 18 numbered issues with cause + resolution |\n| Quick Reference: Consumption Capabilities by Scenario | [SQLDW-CONSUMPTION-CORE.md § Quick Reference: Consumption Capabilities](..\u002F..\u002Fcommon\u002FSQLDW-CONSUMPTION-CORE.md#quick-reference-consumption-capabilities-by-scenario) | Scenario → approach lookup |\n| Schema and Object Discovery | [discovery-queries.md § Schema and Object Discovery](references\u002Fdiscovery-queries.md#schema-and-object-discovery) | Tables, columns, views, functions, procedures, cross-DB |\n| Security Discovery | [discovery-queries.md § Security Discovery](references\u002Fdiscovery-queries.md#security-discovery) ||\n| Statistics and Performance Metadata | [discovery-queries.md § Statistics and Performance Metadata](references\u002Fdiscovery-queries.md#statistics-and-performance-metadata) ||\n| Data Export Workflow | [script-templates.md § Data Export Workflow](references\u002Fscript-templates.md#data-export-workflow) | Query to CSV + parameterized date range export |\n| Schema Discovery Workflow | [script-templates.md § Schema Discovery Workflow](references\u002Fscript-templates.md#schema-discovery-workflow) | Full schema report via MCP |\n| Performance Investigation Workflow | [script-templates.md § Performance Investigation Workflow](references\u002Fscript-templates.md#performance-investigation-workflow) | Active queries, slow query analysis |\n| Tool Stack | [SKILL.md § Tool Stack](#tool-stack) | `fabric-sqlendpoint-execute_query` MCP tool + `az` CLI |\n| Connection | [SKILL.md § Connection](#connection) ||\n| Agentic Exploration (\"Chat With My Data\") | [SKILL.md § Agentic Exploration](#agentic-exploration-chat-with-my-data) | **Start here** for data exploration |\n| Script Generation | [consumption-cli-quickref.md § Script Generation](references\u002Fconsumption-cli-quickref.md#script-generation) | When to emit a standalone bash\u002FPowerShell script; `az rest` discovery + Legacy CLI Fallback |\n| Monitoring and Performance | [consumption-cli-quickref.md § Monitoring and Performance](references\u002Fconsumption-cli-quickref.md#monitoring-and-performance) | Active queries DMV (read-only; session termination is out of scope) |\n| Gotchas, Rules, Troubleshooting | [SKILL.md § Gotchas, Rules, Troubleshooting](#gotchas-rules-troubleshooting) | **MUST DO \u002F AVOID \u002F PREFER** checklists |\n| Agent Integration Notes | [consumption-cli-quickref.md § Agent Integration Notes](references\u002Fconsumption-cli-quickref.md#agent-integration-notes) | Per-agent CLI tips |\n\n---\n\n## Tool Stack\n\n| Tool | Role | Install |\n|---|---|---|\n| `fabric-sqlendpoint-execute_query` MCP tool | **Primary**: Execute T-SQL queries against Fabric SQL Endpoints. Returns CSV results. Auth handled by MCP protocol. | No install — server-side. Requires MCP server registration (see below). |\n| `az` CLI | Auth (`az login`), Fabric REST for workspace\u002Fitem discovery. | Pre-installed in most dev environments |\n| `jq` | Parse JSON from `az rest` | Pre-installed or trivial |\n\n> **IMPORTANT — MCP vs sqlcmd:**\n> This skill uses the `fabric-sqlendpoint-execute_query` MCP tool for all T-SQL execution. Do **not** use COMMON-CLI SQL\u002FTDS\u002Fsqlcmd sections for query execution. Those references apply only for `az rest` control-plane patterns.\n\n> **Agent preflight** — verify before first SQL operation:\n> 1. Confirm the `fabric-sqlendpoint-execute_query` tool is available in your tool list. This tool is provided by the `fabric-sqlendpoint` MCP server, which is registered either by installing a Fabric skills **plugin** (the path for end users) or via this repo's `.mcp.json` — other MCP clients may register it through their own configuration.\n> 2. If no matching tool is found, the user must register the Fabric SQL Endpoint MCP server. See [mcp-setup\u002F](..\u002F..\u002Fmcp-setup\u002F) for registration instructions.\n>    - **Global URL**: `https:\u002F\u002Fapi.fabric.microsoft.com\u002Fv1\u002Fmcp\u002FdataPlane\u002FsqlEndpoint`\n>    - **Item-scoped URL**: `https:\u002F\u002Fapi.fabric.microsoft.com\u002Fv1\u002Fmcp\u002FdataPlane\u002Fworkspaces\u002F{workspaceId}\u002Fitems\u002F{itemId}\u002FsqlEndpoint`\n\n### MCP Tool Signature\n\n```text\nfabric-sqlendpoint-execute_query(workspaceId, itemId, query)\n```\n\n> **Tool name may differ:** `execute_query` is the logical operation. Depending on how the server is\n> registered, the concrete tool name in your tool list may be prefixed (e.g.\n> `fabric-sqlendpoint-execute_query` or `sqlendpoint-global-execute_query`). Invoke the concrete name\n> shown in your tool list, always passing `workspaceId`, `itemId`, and `query`.\n\n| Parameter | Type | Description |\n|-----------|------|-------------|\n| `workspaceId` | string (UUID) | The workspace GUID containing the target item |\n| `itemId` | string (UUID) | The Fabric item GUID to query. For a **Warehouse** or **Mirrored Database**, use the item id. For a **Lakehouse**, use its **SQL analytics endpoint** id (`properties.sqlEndpointProperties.id`) — **not** the Lakehouse item id. |\n| `query` | string | T-SQL query text (single batch — no `GO` separators or sqlcmd meta-commands) |\n\n**Returns:** CSV resource (RFC 4180) with tabular results + metadata text (\"Query returned N rows.\").\n\n> **Batch guidance:** Multiple statements (e.g., `SET NOCOUNT ON; SELECT ...`) are allowed in a single call as long as there are no `GO` separators. Only the last result set is returned. For independent read queries, prefer separate `fabric-sqlendpoint-execute_query` calls for clearer error handling.\n\n### MCP Limits\n\n| Limit | Value | Notes |\n|-------|-------|-------|\n| Max rows | 10,000 | Results are truncated beyond this. Use `TOP`, filters, or aggregations. |\n| Query timeout | 300 seconds | Long-running queries fail with timeout error. |\n| Rate limit | 20 requests\u002Fmin per identity | HTTP 429 returned when exceeded. Retry after backoff. |\n\n> These values are **observed defaults, not a documented contract** — the MCP service can change them. Treat them as guidance and confirm the current behavior from live `429` \u002F timeout \u002F truncation responses (or Microsoft Learn, if\u002Fwhen published) rather than relying on the exact numbers.\n\n### Supported Item Types\n\n| Item Type | itemId Source | Read Queries | DML (INSERT\u002FUPDATE\u002FDELETE) |\n|-----------|--------------|--------------|---------------------------|\n| **Warehouse** | `GET \u002Fv1\u002Fworkspaces\u002F{wId}\u002Fwarehouses` → item `id` | ✅ | ✅ |\n| **Lakehouse SQL Endpoint** | `GET \u002Fv1\u002Fworkspaces\u002F{wId}\u002Flakehouses` → `properties.sqlEndpointProperties.id` (**not** the lakehouse `id`) | ✅ | ❌ (read-only) |\n| **Mirrored Database** | `GET \u002Fv1\u002Fworkspaces\u002F{wId}\u002FmirroredDatabases` → item `id` | ✅ | ❌ (read-only) |\n\n---\n\n## Connection\n\n### Discover workspaceId and itemId\n\nYou need the workspace GUID and item GUID to call `fabric-sqlendpoint-execute_query`. Discover them via the Fabric REST API:\n\n```bash\n# 1. Find workspace ID by name (capture into WS_ID for the next calls)\nWS_ID=$(az rest --method get \\\n  --resource \"https:\u002F\u002Fapi.fabric.microsoft.com\" \\\n  --url \"https:\u002F\u002Fapi.fabric.microsoft.com\u002Fv1\u002Fworkspaces\" \\\n  --query \"value[?displayName=='MyWorkspace'].id\" --output tsv)\necho \"Workspace ID: $WS_ID\"\n\n# 2. Find warehouse item ID by name\naz rest --method get \\\n  --resource \"https:\u002F\u002Fapi.fabric.microsoft.com\" \\\n  --url \"https:\u002F\u002Fapi.fabric.microsoft.com\u002Fv1\u002Fworkspaces\u002F$WS_ID\u002Fwarehouses\" \\\n  --query \"value[?displayName=='MyWarehouse'].id\" --output tsv\n# For a Lakehouse, pass its SQL analytics endpoint id — NOT the lakehouse item id\naz rest --method get \\\n  --resource \"https:\u002F\u002Fapi.fabric.microsoft.com\" \\\n  --url \"https:\u002F\u002Fapi.fabric.microsoft.com\u002Fv1\u002Fworkspaces\u002F$WS_ID\u002Flakehouses\" \\\n  --query \"value[?displayName=='MyLakehouse'].properties.sqlEndpointProperties.id\" --output tsv\n```\n\n### Execute a Query\n\nOnce you have `workspaceId` and `itemId`, call the MCP tool:\n\n```text\nfabric-sqlendpoint-execute_query(\n  workspaceId: \"aaaaaaaa-bbbb-cccc-dddd-eeeeeeeeeeee\",\n  itemId: \"11111111-2222-3333-4444-555555555555\",\n  query: \"SELECT TOP 10 * FROM dbo.FactSales\"\n)\n```\n\n**No additional connection setup needed** — authentication is handled transparently by the MCP protocol.\n\n---\n\n## Agentic Exploration (\"Chat With My Data\")\n\n### Schema Discovery Sequence\n\nRun these in order to understand what's in the endpoint. See [references\u002Fdiscovery-queries.md](references\u002Fdiscovery-queries.md) for extended discovery queries.\n\n```text\n# 1. List schemas\nfabric-sqlendpoint-execute_query(workspaceId, itemId, \"SELECT schema_name FROM INFORMATION_SCHEMA.SCHEMATA ORDER BY schema_name\")\n\n# 2. List tables and views\nfabric-sqlendpoint-execute_query(workspaceId, itemId, \"SELECT table_schema, table_name, table_type FROM INFORMATION_SCHEMA.TABLES ORDER BY table_schema, table_name\")\n\n# 3. Columns for a table\nfabric-sqlendpoint-execute_query(workspaceId, itemId, \"SELECT column_name, data_type, character_maximum_length, is_nullable FROM INFORMATION_SCHEMA.COLUMNS WHERE table_schema='dbo' AND table_name='FactSales' ORDER BY ordinal_position\")\n\n# 4. Preview rows\nfabric-sqlendpoint-execute_query(workspaceId, itemId, \"SELECT TOP 5 * FROM dbo.FactSales\")\n\n# 5. Row counts\nfabric-sqlendpoint-execute_query(workspaceId, itemId, \"SELECT s.name AS [schema], t.name AS [table], SUM(p.rows) AS row_count FROM sys.tables t JOIN sys.schemas s ON t.schema_id=s.schema_id JOIN sys.partitions p ON t.object_id=p.object_id AND p.index_id IN (0,1) GROUP BY s.name, t.name ORDER BY row_count DESC\")\n\n# 6. Programmability objects (views, functions, procedures)\nfabric-sqlendpoint-execute_query(workspaceId, itemId, \"SELECT name, type_desc FROM sys.objects WHERE type IN ('V','FN','IF','P','TF') ORDER BY type_desc, name\")\n```\n\n### Agentic Workflow\n\n1. **Discover** → Run Steps 1–3 to understand available tables\u002Fcolumns.\n2. **Sample** → `SELECT TOP 5` on relevant tables.\n3. **Formulate** → Write T-SQL using [SQLDW-CONSUMPTION-CORE.md](..\u002F..\u002Fcommon\u002FSQLDW-CONSUMPTION-CORE.md) Supported T-SQL Surface Area.\n4. **Execute** → Call `fabric-sqlendpoint-execute_query(workspaceId, itemId, query)`.\n5. **Iterate** → Refine based on results.\n6. **Present** → Show results or generate follow-up queries.\n\n---\n\n## Gotchas, Rules, Troubleshooting\n\nFor full T-SQL\u002Fplatform gotchas: [SQLDW-CONSUMPTION-CORE.md](..\u002F..\u002Fcommon\u002FSQLDW-CONSUMPTION-CORE.md) Gotchas and Troubleshooting Reference.\n\n### MUST DO\n\n- **Verify `fabric-sqlendpoint-execute_query` MCP tool is available** — check tool list before first operation. If unavailable, instruct user to register the MCP server.\n- **Always use `TOP` or `WHERE` filters** — the MCP tool returns a maximum of 10,000 rows. If exactly 10,000 rows are returned, results are likely truncated.\n- **Use `COUNT(*)` first for large tables** — check row counts before running unbounded SELECTs.\n- **`SET NOCOUNT ON;`** at the start of multi-statement queries — suppresses row-count messages.\n- **Label queries** with `OPTION (LABEL = 'AGENTCLI_...')` for Query Insights tracing.\n- **Send valid T-SQL only** — no `GO` batch separators, no `:setvar`, no sqlcmd meta-commands. Each `fabric-sqlendpoint-execute_query` call is a single T-SQL batch.\n- **Use multiple tool calls for multi-batch operations** — if you need `GO` separators, split into separate `fabric-sqlendpoint-execute_query` calls.\n\n### AVOID\n\n- **`sqlcmd`** — use the `fabric-sqlendpoint-execute_query` MCP tool instead. Do not shell out to sqlcmd for query execution.\n- **Unbounded `SELECT *`** — will hit the 10,000 row cap. Always use `TOP N` or `WHERE` filters.\n- **Rapid-fire sequential queries** — rate limit is 20 req\u002Fmin per identity. Space out calls or consolidate with JOINs\u002FUNION ALL.\n- **DML on Lakehouse\u002FMirrored DB** — these are read-only. DML only works on Warehouse items.\n- **`GO` separators in query text** — not supported. Use separate tool calls for each batch.\n- **MARS** — not supported. Each query runs independently.\n- **Hardcoded item IDs** — discover via REST API (Connection section).\n\n### PREFER\n\n- **`fabric-sqlendpoint-execute_query` MCP tool** over any CLI tool for T-SQL execution.\n- **`TOP N`** on exploration queries — avoid hitting row limits.\n- **Consolidating related queries** into single SELECTs with JOINs to reduce rate-limit pressure.\n- **`az rest`** for Fabric REST API operations — workspace\u002Fitem discovery, capacity management.\n- **Aggregate queries** (`COUNT`, `SUM`, `AVG`, `GROUP BY`) over full table scans.\n- **`ORDER BY` with `TOP`** for deterministic results.\n\n### TROUBLESHOOTING\n\n| Symptom | Cause | Fix |\n|---|---|---|\n| MCP tool not available | MCP server not registered | Register `https:\u002F\u002Fapi.fabric.microsoft.com\u002Fv1\u002Fmcp\u002FdataPlane\u002FsqlEndpoint` in MCP client config |\n| HTTP 401 \u002F Unauthorized | Auth token expired or invalid | Re-authenticate (depends on MCP client — may need `az login` refresh) |\n| HTTP 403 \u002F Forbidden | Insufficient permissions on workspace\u002Fitem | Verify user has Viewer+ role on the workspace\u002Fitem |\n| HTTP 404 \u002F Not Found | Wrong workspaceId\u002FitemId, or feature not enabled | Verify IDs via REST API; 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Use when the user is working in a Rush-based repository, asks about Rush commands (install, update, build, rebuild), needs help with project selection, dependency management, build caching, subspace configuration, or troubleshooting Rush-specific issues.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3516,3519,3522,3523,3526],{"name":3517,"slug":3518,"type":13},"Engineering","engineering",{"name":3520,"slug":3521,"type":13},"Local Development","local-development",{"name":9,"slug":8,"type":13},{"name":3524,"slug":3525,"type":13},"Project Management","project-management",{"name":3527,"slug":3528,"type":13},"Rush","rush",6484,"https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Frushstack","2026-04-06T18:34:44.965032",{"slug":3533,"name":3533,"fn":3534,"description":3535,"org":3536,"tags":3537,"stars":3550,"repoUrl":3551,"updatedAt":3552},"azure-ai-agents-persistent-dotnet","build AI agents with Azure .NET SDK","Azure AI Agents Persistent SDK for .NET. Low-level SDK for creating and managing AI agents with threads, messages, runs, and tools. Use for agent CRUD, conversation threads, streaming responses, function calling, file search, and code interpreter. Triggers: \"PersistentAgentsClient\", \"persistent agents\", \"agent threads\", \"agent runs\", \"streaming agents\", \"function calling agents .NET\".\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3538,3541,3544,3547],{"name":3539,"slug":3540,"type":13},".NET","net",{"name":3542,"slug":3543,"type":13},"Agents","agents",{"name":3545,"slug":3546,"type":13},"Azure","azure",{"name":3548,"slug":3549,"type":13},"LLM","llm",2804,"https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fskills","2026-07-03T16:32:10.297433",{"slug":3554,"name":3554,"fn":3555,"description":3556,"org":3557,"tags":3558,"stars":3550,"repoUrl":3551,"updatedAt":3571},"azure-ai-anomalydetector-java","build anomaly detection applications with Java","Build anomaly detection applications with Azure AI Anomaly Detector SDK for Java. Use when implementing univariate\u002Fmultivariate anomaly detection, time-series analysis, or AI-powered monitoring.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3559,3560,3561,3564,3567,3568],{"name":21,"slug":22,"type":13},{"name":3545,"slug":3546,"type":13},{"name":3562,"slug":3563,"type":13},"Data Analysis","data-analysis",{"name":3565,"slug":3566,"type":13},"Java","java",{"name":9,"slug":8,"type":13},{"name":3569,"slug":3570,"type":13},"Monitoring","monitoring","2026-05-13T06:14:16.261754",{"slug":3573,"name":3573,"fn":3574,"description":3575,"org":3576,"tags":3577,"stars":3550,"repoUrl":3551,"updatedAt":3586},"azure-ai-contentsafety-java","build content moderation applications with Azure AI","Build content moderation applications with Azure AI Content Safety SDK for Java. Use when implementing text\u002Fimage analysis, blocklist management, or harm detection for hate, violence, sexual content, and self-harm.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3578,3581,3582,3583],{"name":3579,"slug":3580,"type":13},"AI Infrastructure","ai-infrastructure",{"name":3545,"slug":3546,"type":13},{"name":3565,"slug":3566,"type":13},{"name":3584,"slug":3585,"type":13},"Security","security","2026-07-07T06:53:31.293235",{"slug":3588,"name":3588,"fn":3589,"description":3590,"org":3591,"tags":3592,"stars":3550,"repoUrl":3551,"updatedAt":3603},"azure-ai-contentsafety-py","detect harmful content with Azure AI Content Safety","Azure AI Content Safety SDK for Python. Use for detecting harmful content in text and images with multi-severity classification.\nTriggers: \"azure-ai-contentsafety\", \"ContentSafetyClient\", \"content moderation\", \"harmful content\", \"text analysis\", \"image analysis\".\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3593,3594,3597,3598,3599,3602],{"name":3545,"slug":3546,"type":13},{"name":3595,"slug":3596,"type":13},"Compliance","compliance",{"name":3548,"slug":3549,"type":13},{"name":9,"slug":8,"type":13},{"name":3600,"slug":3601,"type":13},"Python","python",{"name":3584,"slug":3585,"type":13},"2026-07-18T05:14:23.017504",{"slug":3605,"name":3605,"fn":3606,"description":3607,"org":3608,"tags":3609,"stars":3550,"repoUrl":3551,"updatedAt":3614},"azure-ai-language-conversations-py","implement conversational language understanding with Python","Implement Conversational Language Understanding (CLU) using the azure-ai-language-conversations Python SDK. Use when working with ConversationAnalysisClient to analyze conversation intent and entities, building NLP features, or integrating language understanding into applications.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3610,3611,3612,3613],{"name":21,"slug":22,"type":13},{"name":3545,"slug":3546,"type":13},{"name":3548,"slug":3549,"type":13},{"name":3600,"slug":3601,"type":13},"2026-07-31T05:54:29.068751",{"slug":3616,"name":3616,"fn":3617,"description":3618,"org":3619,"tags":3620,"stars":3550,"repoUrl":3551,"updatedAt":3627},"azure-ai-translation-text-py","translate text using Azure AI services","Azure AI Text Translation SDK for real-time text translation, transliteration, language detection, and dictionary lookup. Use for translating text content in applications.\nTriggers: \"text translation\", \"translator\", \"translate text\", \"transliterate\", \"TextTranslationClient\".\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3621,3624,3625,3626],{"name":3622,"slug":3623,"type":13},"API Development","api-development",{"name":3545,"slug":3546,"type":13},{"name":9,"slug":8,"type":13},{"name":3600,"slug":3601,"type":13},"2026-07-18T05:14:16.988376",{"slug":3629,"name":3629,"fn":3630,"description":3631,"org":3632,"tags":3633,"stars":3550,"repoUrl":3551,"updatedAt":3642},"azure-ai-vision-imageanalysis-py","analyze images with Azure AI Vision","Azure AI Vision Image Analysis SDK for captions, tags, objects, OCR, people detection, and smart cropping. Use for computer vision and image understanding tasks.\nTriggers: \"image analysis\", \"computer vision\", \"OCR\", \"object detection\", \"ImageAnalysisClient\", \"image caption\".\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3634,3635,3638,3641],{"name":3545,"slug":3546,"type":13},{"name":3636,"slug":3637,"type":13},"Computer Vision","computer-vision",{"name":3639,"slug":3640,"type":13},"Images","images",{"name":3600,"slug":3601,"type":13},"2026-07-18T05:14:18.007737",{"slug":3644,"name":3644,"fn":3645,"description":3646,"org":3647,"tags":3648,"stars":3550,"repoUrl":3551,"updatedAt":3657},"azure-appconfiguration-java","manage configuration with Azure App Configuration","Azure App Configuration SDK for Java. Centralized application configuration management with key-value settings, feature flags, and snapshots.\nTriggers: \"ConfigurationClient java\", \"app configuration java\", \"feature flag java\", \"configuration setting java\", \"azure config java\".\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3649,3650,3653,3656],{"name":3545,"slug":3546,"type":13},{"name":3651,"slug":3652,"type":13},"Configuration","configuration",{"name":3654,"slug":3655,"type":13},"Feature Flags","feature-flags",{"name":3565,"slug":3566,"type":13},"2026-07-03T16:32:01.278468",{"slug":3659,"name":3659,"fn":3660,"description":3661,"org":3662,"tags":3663,"stars":3550,"repoUrl":3551,"updatedAt":3674},"azure-cosmos-rust","build applications with Azure Cosmos DB","Azure Cosmos DB library for Rust (NoSQL API). Document CRUD, containers, and globally distributed data.\nTriggers: \"cosmos db rust\", \"CosmosClient rust\", \"document crud rust\", \"NoSQL rust\", \"partition key rust\".\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3664,3667,3668,3671],{"name":3665,"slug":3666,"type":13},"Cosmos DB","cosmos-db",{"name":15,"slug":16,"type":13},{"name":3669,"slug":3670,"type":13},"NoSQL","nosql",{"name":3672,"slug":3673,"type":13},"Rust","rust","2026-07-31T05:54:27.021432",{"slug":3676,"name":3676,"fn":3660,"description":3677,"org":3678,"tags":3679,"stars":3550,"repoUrl":3551,"updatedAt":3687},"azure-cosmos-ts","Azure Cosmos DB JavaScript\u002FTypeScript SDK (@azure\u002Fcosmos) for data plane operations. Use for CRUD operations on documents, queries, bulk operations, and container management. Triggers: \"Cosmos DB\", \"@azure\u002Fcosmos\", \"CosmosClient\", \"document CRUD\", \"NoSQL queries\", \"bulk operations\", \"partition key\", \"container.items\".\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3680,3681,3682,3683,3684],{"name":3665,"slug":3666,"type":13},{"name":15,"slug":16,"type":13},{"name":9,"slug":8,"type":13},{"name":3669,"slug":3670,"type":13},{"name":3685,"slug":3686,"type":13},"TypeScript","typescript","2026-07-03T16:31:19.368382",{"slug":3689,"name":3689,"fn":3690,"description":3691,"org":3692,"tags":3693,"stars":3550,"repoUrl":3551,"updatedAt":3699},"azure-data-tables-java","build table storage applications with Java","Build table storage applications with Azure Tables SDK for Java. Use when working with Azure Table Storage or Cosmos DB Table API for NoSQL key-value data, schemaless storage, or structured data at scale.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3694,3695,3696,3697,3698],{"name":3545,"slug":3546,"type":13},{"name":3665,"slug":3666,"type":13},{"name":15,"slug":16,"type":13},{"name":3565,"slug":3566,"type":13},{"name":3669,"slug":3670,"type":13},"2026-05-13T06:14:17.582229",267,{"items":3702,"total":3808},[3703,3714,3735,3753,3769,3783,3797],{"slug":64,"name":64,"fn":3704,"description":3705,"org":3706,"tags":3707,"stars":26,"repoUrl":27,"updatedAt":3713},"check for Fabric skill marketplace updates","Check an installed skills-for-fabric plugin bundle or git clone for updates, show the matching changelog, and provide host-appropriate update guidance. Use when the user wants to: (1) check for skill updates, (2) see what changed, (3) verify the installed version. Triggers: \"check for updates\", \"am I up to date\", \"what version\", \"update skills\", \"show changelog\".\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3708,3709,3710],{"name":9,"slug":8,"type":13},{"name":18,"slug":19,"type":13},{"name":3711,"slug":3712,"type":13},"Operations","operations","2026-07-24T05:41:25.892114",{"slug":3715,"name":3715,"fn":3716,"description":3717,"org":3718,"tags":3719,"stars":26,"repoUrl":27,"updatedAt":3734},"databricks-migration","migrate Databricks notebooks to Microsoft Fabric","Port Databricks notebooks and jobs to Microsoft Fabric. Provides an exhaustive dbutils to notebookutils substitution table: fs operations (runtime mounts or OneLake Shortcuts), secret scope to Key Vault URL conversion, notebook run and exit, widget replacement with parameter-tagged cells, and library install replacement with Fabric Environments. Covers Unity Catalog three-level namespace reduction to Lakehouse two-level schemas, DBFS path conversion to OneLake, Databricks Jobs to Spark Job Definitions, MLflow tracking URI removal, and Photon to Native Execution Engine substitution. Use when the user wants to: (1) replace dbutils with notebookutils, (2) collapse Unity Catalog namespaces to Lakehouse schemas, (3) convert Databricks Jobs or Delta Live Tables. Triggers: \"migrate from databricks\", \"databricks to fabric\", \"dbutils to notebookutils\", \"dbutils fabric\", \"unity catalog migration\", \"dbfs to onelake\", \"databricks notebook migration\", \"delta live tables fabric\", \"photon native execution\".\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3720,3723,3726,3727,3728,3731],{"name":3721,"slug":3722,"type":13},"Data Engineering","data-engineering",{"name":3724,"slug":3725,"type":13},"Databricks","databricks",{"name":9,"slug":8,"type":13},{"name":18,"slug":19,"type":13},{"name":3729,"slug":3730,"type":13},"Migration","migration",{"name":3732,"slug":3733,"type":13},"Notebooks","notebooks","2026-07-24T05:41:22.914341",{"slug":3736,"name":3736,"fn":3737,"description":3738,"org":3739,"tags":3740,"stars":26,"repoUrl":27,"updatedAt":3752},"dataflows-authoring-cli","manage Microsoft Fabric Dataflows via CLI","Create, update, delete, and refresh Fabric Dataflows Gen2 with write-side CLI via Fabric APIs. Build mashup.pq and queryMetadata.json, preview candidate M with executeQuery\u002FcustomMashupDocument, bind connections, and configure output destinations. For saved query execution or refresh-status reads, use `dataflows-consumption-cli`. If a request explicitly insists on the Dataflows consumption or read-only path for a mutation, do not route here; let consumption refuse before any separately confirmed authoring handoff. Triggers: \"create dataflow\", \"update dataflow\", \"delete dataflow\", \"trigger dataflow refresh\", \"preview Power Query M\", \"preview before save\", \"customMashupDocument\", \"create Fabric data source connection\", \"create SQL Server source REST\", \"POST \u002Fv1\u002Fconnections\", \"supportedConnectionTypes\", \"passwordReference\", \"bind connection\", \"dataflow output destination\", \"dataflow write to lakehouse\", \"dataflow write to warehouse\", \"dataflow write to ADX\", \"DataDestinations annotation\".\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3741,3744,3745,3748,3749],{"name":3742,"slug":3743,"type":13},"CLI","cli",{"name":3721,"slug":3722,"type":13},{"name":3746,"slug":3747,"type":13},"Data Pipeline","data-pipeline",{"name":18,"slug":19,"type":13},{"name":3750,"slug":3751,"type":13},"REST API","rest-api","2026-07-31T05:54:59.059517",{"slug":3754,"name":3754,"fn":3755,"description":3756,"org":3757,"tags":3758,"stars":26,"repoUrl":27,"updatedAt":3768},"e2e-medallion-architecture","implement Medallion Architecture in Microsoft Fabric","Plan and implement end-to-end Microsoft Fabric data platforms and Medallion Architecture (Bronze\u002FSilver\u002FGold) lakehouse patterns using PySpark, Delta Lake, Lakehouse\u002FWarehouse items, Fabric Pipelines, and semantic-model handoff. Use when the user wants to: (1) design a Bronze\u002FSilver\u002FGold data lakehouse, (2) plan an ingestion-to-semantic-model Fabric data platform, (3) set up multi-layer workspaces with lakehouses for each tier, (4) build ingestion-to-analytics pipelines with data quality enforcement, (5) orchestrate Bronze-to-Silver-to-Gold flows via notebooks. For natural-language business questions over existing Power BI report data, use `fabriciq`. Triggers: \"medallion architecture\", \"bronze silver gold\", \"lakehouse layers\", \"e2e data pipeline\", \"end-to-end lakehouse\", \"data lakehouse pattern\", \"multi-layer lakehouse\", \"build medallion\", \"setup medallion\", \"end-to-end Fabric data platform\", \"bronze silver gold platform\", \"ingestion semantic model platform\".\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3759,3762,3763,3764,3765],{"name":3760,"slug":3761,"type":13},"Architecture","architecture",{"name":3721,"slug":3722,"type":13},{"name":9,"slug":8,"type":13},{"name":18,"slug":19,"type":13},{"name":3766,"slug":3767,"type":13},"PySpark","pyspark","2026-07-31T05:54:57.038396",{"slug":3770,"name":3770,"fn":3771,"description":3772,"org":3773,"tags":3774,"stars":26,"repoUrl":27,"updatedAt":3782},"eventhouse-authoring-cli","manage Fabric Eventhouse and KQL databases","Execute KQL management commands (table management, ingestion, policies, functions, materialized views) against Fabric Eventhouse and KQL Databases via CLI. Use when the user wants to:\n  1. Create or alter KQL tables, columns, or functions\n  2. Ingest data into an Eventhouse (inline, from storage, streaming)\n  3. Configure retention, caching, or partitioning policies\n  4. Create or manage materialized views and update policies\n  5. Manage data mappings for ingestion pipelines\n  6. Deploy KQL schema via scripts\nTriggers: \"create kql table\", \"kql ingestion\", \"ingest into eventhouse\", \"kql function\", \"materialized view\", \"kql retention policy\", \"eventhouse schema\", \"kql authoring\", \"create eventhouse table\", \"kql mapping\"\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3775,3776,3779,3780,3781],{"name":15,"slug":16,"type":13},{"name":3777,"slug":3778,"type":13},"KQL","kql",{"name":9,"slug":8,"type":13},{"name":18,"slug":19,"type":13},{"name":3711,"slug":3712,"type":13},"2026-04-06T18:36:51.340409",{"slug":3784,"name":3784,"fn":3785,"description":3786,"org":3787,"tags":3788,"stars":26,"repoUrl":27,"updatedAt":3796},"eventhouse-consumption-cli","query Fabric Eventhouse with KQL","Run KQL queries against Fabric Eventhouse for real-time intelligence and time-series analytics using `az rest` against the Kusto REST API. Covers KQL operators (where, summarize, join, render), Eventhouse schema discovery (.show tables), time-series patterns with bin(), and ingestion monitoring. Use when the user wants to:\n  1. Run read-only KQL queries against an Eventhouse or KQL Database\n  2. Discover Eventhouse table schema and metadata\n  3. Analyse real-time or time-series data with KQL operators\n  4. Monitor ingestion health and active KQL queries\n  5. Export KQL results to JSON\nTriggers: \"kql query\", \"kusto query\", \"eventhouse query\", \"kql database\", \"real-time intelligence\", \"time-series kql\", \"query eventhouse\", \"explore eventhouse\", \"show tables kql\"\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3789,3790,3791,3792,3793],{"name":21,"slug":22,"type":13},{"name":3777,"slug":3778,"type":13},{"name":9,"slug":8,"type":13},{"name":18,"slug":19,"type":13},{"name":3794,"slug":3795,"type":13},"Real-Time Intelligence","real-time-intelligence","2026-04-06T18:36:45.018807",{"slug":3798,"name":3798,"fn":3799,"description":3800,"org":3801,"tags":3802,"stars":26,"repoUrl":27,"updatedAt":3807},"eventstream-consumption-cli","monitor Fabric Eventstream ingestion pipelines","List, inspect, and monitor Fabric Eventstream real-time ingestion pipelines via the Items REST API. Discover Eventstreams across workspaces, decode base64 graph topologies tracing event flow from source through operators to destination nodes. Validate connection IDs, wiring, retention policies (1-90 days), and throughput levels. Retrieve Custom Endpoint Kafka credentials via Topology API. **Invoke this skill** to: (1) list Eventstreams, (2) inspect Eventstream topology showing sources and destinations, (3) validate Eventstream configurations, (4) check Eventstream retention policy and throughput level, (5) get connection strings. Triggers: \"list eventstreams\", \"inspect eventstream\", \"describe eventstream topology\", \"eventstream operator nodes\", \"eventstream sources and destinations\", \"eventstream health\", \"eventstream status\", \"eventstream configuration\", \"eventstream retention\", \"eventstream throughput level\", \"eventstream connection string\", \"custom endpoint credentials\", \"check eventstream\".\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3803,3804,3805,3806],{"name":3742,"slug":3743,"type":13},{"name":3721,"slug":3722,"type":13},{"name":18,"slug":19,"type":13},{"name":3569,"slug":3570,"type":13},"2026-07-31T05:55:03.045486",19]