metric-creation

name	metric-creation
description	Guides you step-by-step through defining a business metric (aggregation) on a Honeydew entity. Covers SQL expression building and pushes to Honeydew via the MCP tools.

Prerequisites

Before creating metrics, ensure you are on the correct workspace and branch. Use get_session_workspace_and_branch to check the current session context. For development work, create a branch with create_workspace_branch (the session switches automatically). See the workspace-branch skill for the full workspace/branch tool reference.

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Overview

A Honeydew metric is a named, reusable aggregation anchored to an entity. Unlike a calculated attribute (which is per-row), a metric collapses multiple rows into a single value. Metrics are context-sensitive: they automatically respond to whatever filters and groupings the consuming BI tool or analyst applies.

Your job is to build an aggregation function that users can later group by.

If a user asks for a group by ("sum sales by category"), ignore the group. You're building the aggregation to sum sales by ANYTHING. The user will later use it in a query with their chosen dimensions.

Use a metric when:

• The same aggregation logic is reused across dashboards, teams, or tools
• You need a governed, single source of truth for a KPI (e.g. "revenue", "active users")
• The calculation involves combining other metrics (ratios, deltas)

Do not use a metric when the value is per-row (use a calculated attribute instead).

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Building the SQL Expression

Core Rules

• MUST use an aggregation function — metrics collapse rows
• DO NOT use window functions — only aggregations allowed
• DO NOT use joins or subqueries — simple expressions only
• NEVER use COUNT(*) — use the entity's built-in count metric (e.g., entity.count) if available, or COUNT(entity.key_field) on the primary key. No DISTINCT needed — primary keys are unique by definition.
• Compose from existing metrics whenever possible. Honeydew accepts named metric references (entity.metric_name) anywhere you'd write a raw aggregation — inside FILTER (WHERE ...), inside GROUP BY (...) (both fixed GROUP BY (dim) and nested GROUP BY (*, dim)), and as operands in derived arithmetic. Always check whether an existing metric can be the building block before reaching for SUM(...), COUNT(...), etc. The named-metric form expresses business intent, inherits future changes to the base metric, and keeps the model DRY.
• For cross-entity counts, clarify intent first — filtered count or simple count. Use related_entity.count FILTER (WHERE source_entity.key IS NOT NULL) for a filtered count. See reference.md "Cross-entity counts" for mechanics.
• Reuse existing attributes — if a calculated attribute already exists (or you just created one), reference it by name (e.g., entity.attribute_name) rather than repeating its SQL logic.
• Use fully qualified column names — entity.attribute, not just attribute

See reference.md — particularly the "Composing from Existing Metrics" section — for the canonical patterns: filtered, fixed grouping, nested grouping, cross-entity counts, and derived metrics, each with named-metric and raw-fallback forms shown side by side.

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Creation Methods

create_object (Required)

Always use create_object with full YAML to ensure proper datatype and all properties are set.

Call create_object with yaml_text:

type: metric
entity: <entity_name>
name: <snake_case_name>
display_name: <Human Readable Name>
description: |-
  <business description>
owner: <owner_email_or_team>
datatype: float|number|string|date|timestamp
sql: |-
  <aggregation SQL expression>

Required fields:

• type: metric
• entity — the entity this metric belongs to
• name — snake_case identifier
• owner — CRITICAL: always set to current username (from workspace context)
• datatype — CRITICAL: always set explicitly (default to float for most metrics, number for counts)
• sql — the aggregation expression

Optional fields:

• display_name — human readable name
• description — business context for a non-technical user: WHY this metric matters, which business team owns the definition, known caveats, or governance notes. Do NOT describe the SQL or how it's calculated — that's visible in the sql field. Do NOT restate the metric name or display name. If there's nothing meaningful to add beyond the name, omit it entirely.
• format_string — display format (e.g., $#,##0.00)
• labels — categorization tags
• folder — organizational path

update_object (for updates)

To modify an existing metric:

1. Use get_entity with the entity name to find the metric and its details.
2. Use search_model (with search_mode: EXACT) to find the metric's object_key.
3. Call update_object with the full updated YAML (yaml_text) and the object_key.

Minimal diff rule: When updating, preserve the existing field order and formatting from the current YAML. Only change the fields you need to modify. Objects are versioned in git, so unnecessary reordering or reformatting creates noisy diffs.

After Creation/Update: Display the UI Link

After a successful create_object or update_object call, the response includes a ui_url field. Always display this URL to the user so they can quickly open the object in the Honeydew application.

delete_object (for deletion)

1. Use search_model (with search_mode: EXACT) to find the metric's object_key.
2. Call delete_object with that object_key.

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Examples

See examples.md for full worked examples covering: basic, derived, filtered, ratio, count, distinct count, fixed grouping, nested aggregation, text summary, update, and delete.

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Discovery Helpers

Use these MCP tools to explore existing metrics:

• get_entity — Get entity details including all its metrics, attributes, datasets, and relations
• get_field — Get detailed info about a specific metric by entity and field name
• search_model — Search for metrics across the model by name (use search_mode: EXACT for known names, OR for broad discovery)
• list_entities — List entities to identify where to anchor new metrics

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Clarify Ambiguous Requests (BEFORE creating)

Many metric requests are ambiguous. ALWAYS clarify before implementing:

Ambiguous Term	Possible Interpretations	Ask User
"per day/week/month"	A) Breakdown by period (multiple rows)	"Do you want revenue for each day (fixed grouping) or average daily revenue (single KPI)?"
	B) Average per period (single value)
"rate"	A) Ratio (X / Y)	"Is this a ratio (e.g., conversion rate = orders/visits) or velocity (e.g., orders per hour)?"
	B) Velocity (X per time unit)
"growth"	A) Absolute difference	"Do you want absolute growth ($100 → $150 = $50) or percentage growth (50%)?"
	B) Percentage change
"average"	A) Simple mean	"Simple average or weighted average? If weighted, by what?"
	B) Weighted mean

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Map User Choice → Implementation Pattern

After user clarifies intent, use the correct SQL pattern. Wherever a raw aggregation appears below, prefer the named-metric form when an existing metric covers the base aggregation — see reference.md for the full named-metric ↔ raw fallback table.

User Choice	SQL Pattern (preferred named-metric form / raw fallback)	Example
Breakdown by period	entity.metric GROUP BY (time_field) / AGG(field) GROUP BY (time_field)	order_header.total_revenue GROUP BY (order_header.order_date)
Average per period	AVG(entity.metric GROUP BY (*, time_field)) / AVG(AGG(field) GROUP BY (*, time_field))	AVG(order_header.total_revenue GROUP BY (*, order_header.order_date))
Ratio	entity.metric_a / NULLIF(entity.metric_b, 0)	orders.order_count / NULLIF(orders.customer_count, 0)
Velocity (per time)	entity.metric / COUNT(DISTINCT time_field) / AGG(field) / COUNT(DISTINCT time_field)	order_header.total_revenue / NULLIF(COUNT(DISTINCT order_header.order_date), 0)
Absolute growth	current - previous	Requires time comparison logic
Percentage growth	(current - previous) / NULLIF(previous, 0) * 100	Requires time comparison logic

CRITICAL: "Breakdown by X" or "for each X" = Fixed GROUP BY

• ✅ order_header.total_revenue GROUP BY (order_header.order_date)
• ❌ order_header.total_revenue ← requires manual grouping at query time

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Additional Grouping Clarifications

When the user's request contains phrases suggesting a specific granularity, ask before creating:

Phrase	Likely Intent	Clarifying Question
"in an order", "per order", "by order"	Fixed grouping by order_id	"Should this always be calculated per order (fixed grouping), or flexible to group by any dimension?"
"in a day", "per day", "daily"	Fixed grouping by date	"Should this always be at daily granularity, or flexible?"
"per customer", "by customer"	Fixed grouping by customer_id	"Should this always be per customer, or flexible?"

Rule: If the request mentions "per X" or "in an X", clarify whether they want:

1. Fixed Grouping — entity.metric GROUP BY (entity.x_id) (named-metric form preferred)
2. Flexible Aggregation — entity.metric that users can group by anything later

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Documentation Lookup

Use the search_docs and query_docs_filesystem tools from the honeydew MCP server to search the Honeydew documentation when:

• You need to understand metric types or advanced aggregation patterns beyond what reference.md covers
• The user asks about how metrics behave in BI tools or how context-sensitive aggregation works
• You need guidance on derived metrics, fixed groupings, or metric composition patterns
• The user needs time intelligence features — period-over-period comparisons, YTD/MTD/QTD calculations, trailing windows, or time-based growth metrics

Search for topics like: "metrics", "aggregation", "derived metrics", "fixed grouping", "time intelligence", "period over period", "YTD", "trailing window".

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Best Practices

• Use FILTER (WHERE ...) for filtered aggregations — NOT CASE WHEN. The FILTER syntax is cleaner, more readable, and the native Honeydew pattern.
  • ✅ orders.amount FILTER (WHERE orders.is_promotional) (named-metric form preferred)
  • ✅ SUM(orders.amount) FILTER (WHERE orders.is_promotional) (raw fallback)
  • ❌ SUM(CASE WHEN orders.is_promotional THEN orders.amount ELSE 0 END)
• Compose from existing metrics universally — not just for FILTER. Honeydew accepts named-metric references inside FILTER (WHERE ...), inside GROUP BY (dim) and GROUP BY (*, dim), and as operands in derived arithmetic. Whenever an existing metric covers the base aggregation, use the named-metric form rather than re-deriving from raw aggregation functions. Examples:
  • Filtered: orders.revenue FILTER (WHERE orders.region='US') instead of SUM(orders.amount) FILTER (...)
  • Fixed grouping: orders.revenue GROUP BY (orders.region) instead of SUM(orders.amount) GROUP BY (...)
  • Nested grouping: orders.revenue GROUP BY (*, orders.order_date) instead of SUM(...) GROUP BY (*, ...)
  • Derived arithmetic: orders.revenue - orders.cost instead of SUM(price) - SUM(cost) This keeps definitions DRY and ensures changes propagate automatically.
• For cross-entity counts, clarify intent before choosing a pattern. When a user asks for "number of users who made a booking" or similar, confirm whether they want:
  1. Filtered count — count of related-entity members that appear in the source (e.g. users who have at least one booking). Use related_entity.count FILTER (WHERE source_entity.entity_key IS NOT NULL). Example: users.count FILTER (WHERE bookings.booking_id IS NOT NULL)
  2. Simple count — just the total count of the related entity with no filtering. Use related_entity.count directly, or COUNT(DISTINCT source_entity.fk_column) as a raw fallback. See reference.md "Cross-entity counts" for why the filter matters.
• Compose from existing model objects — attributes and metrics alike. Honeydew's semantic layer is object-oriented: attributes and metrics are named, reusable building blocks. When building a new metric, prefer referencing existing objects over inlining raw SQL. This applies everywhere:
  • As an aggregation input: SUM(orders.net_price) instead of SUM(orders.price - orders.discount) when orders.net_price is a calculated attribute that already encodes that subtraction.
  • As a FILTER predicate: bookings.count FILTER (WHERE bookings.is_cancelled) instead of bookings.count FILTER (WHERE bookings.status = 'cancelled') when bookings.is_cancelled is a boolean attribute. Likewise, orders.revenue FILTER (WHERE orders.is_promotional) reuses the is_promotional attribute rather than repeating its SQL condition.
  • As a metric operand: orders.revenue - orders.cost reuses two existing metrics rather than re-deriving SUM(price) - SUM(cost) from raw columns. Referenced objects propagate definition changes automatically and express business concepts rather than SQL implementation details.
• Never use COUNT(*). Use the entity's built-in count metric (e.g., entity.count) when available. Otherwise, use COUNT(entity.key_field) on the primary key. No DISTINCT needed — primary keys are unique by definition.
• Name metrics after the business concept, not the SQL. gross_margin is better than revenue_minus_cogs_divided_by_revenue.
• Description = business context only, or omit. Never use it to explain how the metric is calculated or what aggregation it uses — that's visible in the sql field. A good description answers "why does this metric exist?" or "what should a business user know about this number?" (e.g., ownership, caveats, governance). If there's nothing to add beyond the name, leave the field out entirely.
• Use Derived metrics for ratios. Build numerator and denominator as separate metrics first.
• Use fully qualified column names. orders.amount, not just amount.
• Ignore grouping requests. Build the aggregation; users add dimensions later.

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MANDATORY: Validate After Creating

After creating ANY metric, you MUST invoke the validation skill to test and validate results.

See validation skill for:

• How to execute metrics via get_data_from_fields
• Sanity checks (magnitude, sign, consistency)
• When to alert the user about suspicious results
• Cross-validation with related metrics

Quick validation:

Call get_data_from_fields with:

• metrics: ["<entity>.<metric_name>"]

For filtered cross-entity count metrics — run get_data_from_fields with just the new metric and compare it against related_entity.count standalone. If you built a filtered count (e.g. users who have at least one booking), the result should be less than or equal to the related entity's total. If the two values are equal, the join-forcing filter isn't working as intended — revisit the filter expression.

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Common Pitfalls to Avoid

• Using COUNT(*) — use the entity's built-in count metric if available, or COUNT(entity.key_field) on the primary key. No DISTINCT needed — primary keys are unique.
• Re-deriving from raw aggregations when a named metric exists. Whether the new metric wraps a FILTER, a fixed GROUP BY (dim), a nested GROUP BY (*, dim), or arithmetic, the named-metric form is preferred over the raw aggregation. The raw form is only correct when no suitable named metric exists.
• Cross-entity filtered count missing the filter. Always pair with FILTER (WHERE source_entity.entity_key IS NOT NULL). See reference.md for mechanics.
• Using COUNT(DISTINCT entity.fk_column) without checking if a related entity has a count metric first. Entities auto-generate a count metric. If a relation exists, prefer the related_entity.count FILTER (...) form. COUNT(DISTINCT) is the fallback when there's no relation or no count metric.
• Repeating attribute logic that already exists — if a calculated attribute already exists, reference it by name instead of duplicating its SQL.
• Using window functions — only aggregations allowed in metrics.
• Using joins or subqueries — simple expressions only.
• Unqualified column references — always prefix with entity name.