Eventhouse Consumption CLI Agent Skill

> Update Check — ONCE PER SESSION (mandatory)

> The first time this skill is used in a session, run the check-updates skill before proceeding.

> - GitHub Copilot CLI / VS Code: invoke the check-updates skill.

> - Claude Code / Cowork / Cursor / Windsurf / Codex: compare local vs remote package.json version.

> - Skip if the check was already performed earlier in this session.

> CRITICAL NOTES

> 1. To find the workspace details (including its ID) from workspace name: list all workspaces and, then, use JMESPath filtering

> 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

eventhouse-consumption-cli — Read-Only KQL Queries via CLI

Table of Contents

| Task | Reference | Notes |

|---|---|---|

| Finding Workspaces and Items in Fabric | COMMON-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] |

| Fabric Topology & Key Concepts | COMMON-CORE.md § Fabric Topology & Key Concepts | |

| Environment URLs | COMMON-CORE.md § Environment URLs | KQL Cluster URI is per-item |

| Authentication & Token Acquisition | COMMON-CORE.md § Authentication & Token Acquisition | Wrong audience = 401; read before any auth issue |

| Core Control-Plane REST APIs | COMMON-CORE.md § Core Control-Plane REST APIs | |

| Pagination | COMMON-CORE.md § Pagination | |

| Long-Running Operations (LRO) | COMMON-CORE.md § Long-Running Operations (LRO) | |

| Rate Limiting & Throttling | COMMON-CORE.md § Rate Limiting & Throttling | |

| OneLake Data Access | COMMON-CORE.md § OneLake Data Access | Requires storage.azure.com token, not Fabric token |

| Job Execution | COMMON-CORE.md § Job Execution | |

| Capacity Management | COMMON-CORE.md § Capacity Management | |

| Gotchas & Troubleshooting | COMMON-CORE.md § Gotchas & Troubleshooting | |

| Best Practices | COMMON-CORE.md § Best Practices | |

| Tool Selection Rationale | COMMON-CLI.md § Tool Selection Rationale | |

| Authentication Recipes | COMMON-CLI.md § Authentication Recipes | az login flows and token acquisition |

| Fabric Control-Plane API via az rest | COMMON-CLI.md § Fabric Control-Plane API via az rest | Always pass --resource https://api.fabric.microsoft.com or az rest fails |

| Pagination Pattern | COMMON-CLI.md § Pagination Pattern | |

| Long-Running Operations (LRO) Pattern | COMMON-CLI.md § Long-Running Operations (LRO) Pattern | |

| OneLake Data Access via curl | COMMON-CLI.md § OneLake Data Access via curl | Use curl not az rest (different token audience) |

| Job Execution (CLI) | COMMON-CLI.md § Job Execution | |

| OneLake Shortcuts | COMMON-CLI.md § OneLake Shortcuts | |

| Capacity Management (CLI) | COMMON-CLI.md § Capacity Management | |

| Composite Recipes | COMMON-CLI.md § Composite Recipes | |

| Gotchas & Troubleshooting (CLI-Specific) | COMMON-CLI.md § Gotchas & Troubleshooting (CLI-Specific) | az rest audience, shell escaping, token expiry |

| Quick Reference: az rest Template | COMMON-CLI.md § Quick Reference: az rest Template | |

| Quick Reference: Token Audience / CLI Tool Matrix | COMMON-CLI.md § Quick Reference: Token Audience ↔ CLI Tool Matrix | Which --resource + tool for each service |

| Connection Fundamentals | EVENTHOUSE-CONSUMPTION-CORE.md § Connection Fundamentals | Cluster URI discovery, az rest, REST API |

| Schema Discovery and Security | EVENTHOUSE-CONSUMPTION-CORE.md § Schema Discovery and Security | Schema Discovery, Security — workspace roles + KQL DB roles |

| Monitoring and Diagnostics | EVENTHOUSE-CONSUMPTION-CORE.md § Monitoring and Diagnostics | |

| Performance Best Practices | EVENTHOUSE-CONSUMPTION-CORE.md § Performance Best Practices | Read before writing KQL — time filters, has vs contains |

| Common Consumption Patterns | EVENTHOUSE-CONSUMPTION-CORE.md § Common Consumption Patterns | Time-series, Top-N, percentile, dynamic fields |

| Gotchas, Troubleshooting, and Quick Reference | EVENTHOUSE-CONSUMPTION-CORE.md § Gotchas, Troubleshooting, and Quick Reference | Gotchas and Troubleshooting (12 issues), Quick Reference: Consumption Capabilities by Scenario |

| Table and Column Discovery | discovery-queries.md § Table and Column Discovery | Table Discovery, Column Statistics |

| Function and View Discovery | discovery-queries.md § Function and View Discovery | Function Discovery, Materialized View Discovery |

| Policy Discovery | discovery-queries.md § Policy Discovery | |

| External Tables and Ingestion Mappings | discovery-queries.md § External Tables and Ingestion Mappings | External Table Discovery, Ingestion Mapping Discovery |

| Security Discovery | discovery-queries.md § Security Discovery | |

| Database Overview Script | discovery-queries.md § Database Overview Script | |

| Tool Stack | SKILL.md § Tool Stack | |

| Connection | SKILL.md § Connection | eventhouse-specific az rest connection steps |

| Agentic Exploration ("Chat With My Data") | SKILL.md § Agentic Exploration | Start here for data exploration |

| Running Queries | SKILL.md § Running Queries | az rest, output formatting, export |

| Monitoring | SKILL.md § Monitoring | |

| Must / Prefer / Avoid / Troubleshooting | SKILL.md § Must / Prefer / Avoid / Troubleshooting | MUST DO / AVOID / PREFER checklists |

| Examples | SKILL.md § Examples | |

| Agent Integration Notes | SKILL.md § Agent Integration Notes | |

Tool Stack

| Tool | Purpose | Install |

|---|---|---|

| az cli | KQL queries and management commands via Kusto REST API; Fabric control-plane discovery | winget install Microsoft.AzureCLI |

| jq | JSON processing and output formatting | winget install jqlang.jq |

Connection

Step 1 — Discover KQL Database Query URI

# Get workspace ID (if not known)
WS_ID=$(az rest --method GET \
  --url "https://api.fabric.microsoft.com/v1/workspaces" \
  --resource "https://api.fabric.microsoft.com" \
  | jq -r '.value[] | select(.displayName=="MyWorkspace") | .id')

# List KQL Databases and get connection properties
az rest --method GET \
  --url "https://api.fabric.microsoft.com/v1/workspaces/${WS_ID}/kqlDatabases" \
  --resource "https://api.fabric.microsoft.com" \
  | jq '.value[] | {name: .displayName, id: .id, queryUri: .properties.queryServiceUri, dbName: .properties.databaseName}'

Step 2 — Set Connection Variables

CLUSTER_URI="https://<cluster>.kusto.fabric.microsoft.com"
DB_NAME="MyKqlDatabase"

Step 3 — Verify Connection

> Important — body file pattern: KQL queries contain | (pipe) characters which break shell

> escaping in both bash and PowerShell. Always write the JSON body to a temp file and reference

> it with --body @<file>. This is the recommended approach for all az rest KQL calls.

> On PowerShell, use @{db="X";csl="..."} | ConvertTo-Json -Compress | Out-File $env:TEMP\kql_body.json -Encoding utf8NoBOM then --body "@$env:TEMP\kql_body.json".

# Write body to temp file (avoids pipe escaping issues)
cat > /tmp/kql_body.json << 'EOF'
{"db":"MyKqlDatabase","csl":"print Message = 'Connected successfully', Cluster = current_cluster_endpoint(), Timestamp = now()"}
EOF

az rest --method POST \
  --url "${CLUSTER_URI}/v1/rest/query" \
  --resource "https://kusto.kusto.windows.net" \
  --headers "Content-Type=application/json" \
  --body @/tmp/kql_body.json \
  | jq '.Tables[0].Rows'

Agentic Exploration

"Chat With My Data" — Discovery Sequence

When the user asks to explore or query an Eventhouse without specifying tables:

Step 1 → .show tables                                    // discover tables
Step 2 → .show table <TABLE> schema as json              // understand columns + types
Step 3 → <TABLE> | take 10                               // see sample data
Step 4 → <TABLE> | summarize count() by bin(Timestamp, 1h) | render timechart  // shape of data
Step 5 → Formulate targeted query based on user's question

Schema-Aware Query Generation

After schema discovery, generate queries using actual column names and types:

// Example: user asks "show me errors in the last hour"
// After discovering table "AppEvents" with columns: Timestamp, Level, Message, Source
AppEvents
| where Timestamp > ago(1h)
| where Level == "Error"
| summarize ErrorCount = count() by Source, bin(Timestamp, 5m)
| order by ErrorCount desc

Running Queries

Via az rest

> Always use the temp-file pattern for --body — KQL pipes (|) break inline shell escaping.

# Run a KQL query
cat > /tmp/kql_body.json << 'EOF'
{"db":"MyDB","csl":"Events | where Timestamp > ago(1h) | count"}
EOF

az rest --method POST \
  --url "${CLUSTER_URI}/v1/rest/query" \
  --resource "https://kusto.kusto.windows.net" \
  --headers "Content-Type=application/json" \
  --body @/tmp/kql_body.json \
  | jq '.Tables[0].Rows'

Output Formatting

# Pretty-print results as a table with jq
cat > /tmp/kql_body.json << 'EOF'
{"db":"MyDB","csl":".show tables"}
EOF

az rest --method POST \
  --url "${CLUSTER_URI}/v1/rest/query" \
  --resource "https://kusto.kusto.windows.net" \
  --headers "Content-Type=application/json" \
  --body @/tmp/kql_body.json \
  | jq '.Tables[0] | [.Columns[].ColumnName] as $cols | .Rows[] | [$cols, .] | transpose | map({(.[0]): .[1]}) | add'

# Save results to file
cat > /tmp/kql_body.json << 'EOF'
{"db":"MyDB","csl":"Events | where Timestamp > ago(1h) | summarize count() by EventType"}
EOF

az rest --method POST \
  --url "${CLUSTER_URI}/v1/rest/query" \
  --resource "https://kusto.kusto.windows.net" \
  --headers "Content-Type=application/json" \
  --body @/tmp/kql_body.json \
  --output-file results.json

Monitoring

// Active queries
.show queries

// Recent commands (last hour)
.show commands
| where StartedOn > ago(1h)
| project StartedOn, CommandType, Text = substring(Text, 0, 80), Duration, State
| order by StartedOn desc

// Ingestion failures (for context when data seems stale)
.show ingestion failures
| where FailedOn > ago(24h)
| summarize count() by ErrorCode
| top 5 by count_

Must / Prefer / Avoid / Troubleshooting

Must

• Always include time filters — where Timestamp > ago(...) must be present on time-series tables.
• Discover schema before querying — run .show tables and .show table T schema as json first.
• Use has for term search — indexed and fast; only fall back to contains for substring needs.
• Verify cluster URI — KQL Database URIs are per-item; always resolve via Fabric REST API.

Prefer

• az rest for CLI query sessions; Fabric KQL MCP server for agent-integrated workflows.
• project early to drop unneeded columns before aggregation.
• materialize() when a sub-expression is used multiple times.
• take 100 for initial exploration; avoid full table scans.
• render timechart for time-series; render piechart for distribution.

Avoid

• contains on large tables — full scan, not indexed. Use has or has_cs.
• join without filtering both sides first — causes memory explosion.
• SELECT * equivalent (project all columns) on wide tables.
• Missing bin() in time-series summarize — produces one row per unique timestamp.
• Hardcoded cluster URIs — always resolve from Fabric REST API or environment variables.

Troubleshooting

| Symptom | Fix |

|---|---|

| az rest auth fails | Run az login first; ensure --resource "https://kusto.kusto.windows.net" is set |

| Empty results on valid table | Check database context; may need database("name").table |

| Query timeout | Add tighter time filter; check .show queries for competing queries |

| Forbidden (403) | Request viewer role on the KQL Database |

| Results truncated | Default limit is 500K rows; add set truncationmaxrecords = N; before query |

| KQL pipe \| breaks PowerShell or bash | Never inline KQL in --body. Write JSON to a temp file and use --body @file.json (see Running Queries) |

Examples

Example 1: Discover and Query

# 1. Set connection variables (after discovering URI via Step 1)
CLUSTER_URI="https://<your-cluster>.kusto.fabric.microsoft.com"
DB_NAME="SalesDB"

# 2. Discover tables
cat > /tmp/kql_body.json << EOF
{"db":"${DB_NAME}","csl":".show tables"}
EOF
az rest --method POST \
  --url "${CLUSTER_URI}/v1/rest/query" \
  --resource "https://kusto.kusto.windows.net" \
  --headers "Content-Type=application/json" \
  --body @/tmp/kql_body.json \
  | jq '.Tables[0].Rows'

# 3. Explore schema
cat > /tmp/kql_body.json << EOF
{"db":"${DB_NAME}","csl":".show table Orders schema as json"}
EOF
az rest --method POST \
  --url "${CLUSTER_URI}/v1/rest/query" \
  --resource "https://kusto.kusto.windows.net" \
  --headers "Content-Type=application/json" \
  --body @/tmp/kql_body.json \
  | jq '.Tables[0].Rows'

# 4. Sample data
cat > /tmp/kql_body.json << EOF
{"db":"${DB_NAME}","csl":"Orders | take 10"}
EOF
az rest --method POST \
  --url "${CLUSTER_URI}/v1/rest/query" \
  --resource "https://kusto.kusto.windows.net" \
  --headers "Content-Type=application/json" \
  --body @/tmp/kql_body.json \
  | jq '.Tables[0].Rows'

// 5. Analytical query (via az rest --body @file)
Orders
| where OrderDate > ago(30d)
| summarize
    TotalOrders = count(),
    TotalRevenue = sum(Amount)
    by bin(OrderDate, 1d)
| render timechart

Example 2: Cross-Database Query

// Query across KQL databases in the same Eventhouse
let orders = database("SalesDB").Orders | where OrderDate > ago(7d);
let products = database("CatalogDB").Products;
orders
| join kind=inner (products) on ProductId
| summarize Revenue = sum(Amount) by ProductName
| top 10 by Revenue desc

Example 3: Export Results to File

# Run query and save results to JSON
cat > /tmp/kql_body.json << 'EOF'
{"db":"MyDB","csl":"Events | where Timestamp > ago(1d) | summarize count() by EventType"}
EOF

az rest --method POST \
  --url "${CLUSTER_URI}/v1/rest/query" \
  --resource "https://kusto.kusto.windows.net" \
  --headers "Content-Type=application/json" \
  --body @/tmp/kql_body.json \
  --output-file results.json

# Convert to CSV with jq
cat results.json \
  | jq -r '.Tables[0] | (.Columns | map(.ColumnName)), (.Rows[]) | @csv' > results.csv

Agent Integration Notes

• This skill is read-only — it does not create, alter, or drop database objects.
• For authoring operations (table management, ingestion, policies), delegate to eventhouse-authoring-cli.
• For cross-workload orchestration (Spark + SQL + KQL), delegate to the FabricDataEngineer agent.
• The Fabric KQL MCP server (fabric-kql in mcp-setup/mcp-config-template.json) can be used as an alternative to az rest for agent-integrated query execution.