query

name	query
description	Use when the user wants to query, analyze, or explore data through the Honeydew semantic layer. Covers structured queries and multi-step deep analysis.

Prerequisites

Queries run against the workspace and branch set for the current session. Use get_session_workspace_and_branch to check the current context. If no workspace/branch is set, use list_workspaces, list_workspace_branches, and set_session_workspace_and_branch to select one. See the model-exploration skill for the full workspace/branch tool reference.

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Overview

Honeydew provides three ways to query data through the semantic layer. Each method suits a different situation — pick the right one based on how well you understand the model and how complex the question is.

Method	Tool	Best For
Structured query	get_data_from_fields / get_sql_from_fields	You know the exact fields. Deterministic, full control.
Deep analysis	ask_deep_analysis_question	Any natural language question — simple or complex, "why", multi-step, agentic.

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When to Use Each Method

1. Structured Query (get_data_from_fields / get_sql_from_fields)

Use when:

• You know the entity, attribute, and metric names (or can discover them via list_entities / get_entity)
• You need precise control over filters, ordering, and field selection
• You want deterministic, reproducible results
• You need to validate a newly created metric or attribute
• The user specifies exact fields like "show me detailed_listings.price by detailed_listings.room_type"

Do NOT use when:

• The question requires multi-step reasoning or investigation

How it works:

• get_data_from_fields — executes the query and returns data rows
• get_sql_from_fields — returns the generated SQL without executing (useful for review, debugging, or handing off to other tools)

Both take the same field parameters.

2. Deep Analysis (ask_deep_analysis_question)

Use when:

• The user asks a question in plain English and you don't know the exact field names
• The user wants a quick answer without worrying about model details
• The question requires multiple steps or investigative reasoning
• The user asks "why" something happened (e.g., "why did revenue drop in Q3?")
• The user wants trend analysis, anomaly detection, or root cause investigation
• The question is open-ended and may require looking at the data from multiple angles
• Follow-up questions build on prior analysis (use conversation_id)

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Decision Flow

User asks a data question
    │
    ├─► Do you know the exact field names?
    │       │
    │       ├─► YES → get_data_from_fields (structured, deterministic)
    │       │         (or get_sql_from_fields to preview SQL without executing)
    │       │
    │       └─► NO → ask_deep_analysis_question (plain English, any complexity)
    │
    └─► Plain English question / investigation / "why" / trends?
            └─► ask_deep_analysis_question

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Method 1: Structured Query

Field Parameters

A structured query uses flat field parameters to define what data to retrieve:

• attributes — dimensions to group by (columns in the output), e.g. ["entity.attribute_name"]
• metrics — aggregated measures (SUM, COUNT, AVG, etc.), e.g. ["entity.metric_name"]
• filters — row-level filters applied before aggregation, e.g. ["entity.field = 'value'"]
• order_by — sort order for results. Each entry MUST be a quoted string, as if it were a SQL identifier — e.g. ["\"entity.field\" ASC"]. Always wrap the field reference in double quotes inside the string.
• domain — optional domain name for query context
• limit — max rows to return (default: 100)
• offset — rows to skip (for pagination)

All fields use entity.field_name syntax. Cross-entity fields are supported when relations exist.

Discovering Fields

Before building a query, discover the available fields:

1. list_entities — see all entities
2. get_entity with entity name — see its attributes, metrics, and relations
3. get_field with entity and field name — get detailed info about a specific field
4. list_domains — see all available domains (useful before passing domain parameter)
5. search_model with a keyword and search_mode (OR for broad discovery, EXACT for known names) — find fields across the model

Examples

Simple metric query — total count:

Call get_data_from_fields with:

• metrics: ["detailed_listings.count"]

Dimension breakdown — listings by room type:

Call get_data_from_fields with:

• attributes: ["detailed_listings.room_type"]
• metrics: ["detailed_listings.count"]
• order_by: ["\"detailed_listings.count\" DESC"]

Filtered query — only entire homes:

Call get_data_from_fields with:

• attributes: ["detailed_listings.neighbourhood_cleansed"]
• metrics: ["detailed_listings.count"]
• filters: ["detailed_listings.room_type = 'Entire home/apt'"]
• order_by: ["\"detailed_listings.count\" DESC"]

Cross-entity query — listings with host info:

Call get_data_from_fields with:

• attributes: ["detailed_listings.room_type", "dim_host.host_is_superhost"]
• metrics: ["detailed_listings.count"]
• order_by: ["\"detailed_listings.count\" DESC"]

Using aliases — rename fields or ad-hoc expressions:

You can alias any field or ad-hoc expression using AS "alias_name". This controls the column name in the output.

Call get_data_from_fields with:

• attributes: ["detailed_listings.room_type"]
• metrics: ["detailed_listings.count AS \"total_listings\"", "AVG(detailed_listings.price) AS \"avg_price\""]
• order_by: ["\"total_listings\" DESC"]

Once aliased, use the alias (not the original expression) in order_by.

Pagination — large result sets:

Call get_data_from_fields with:

• attributes: (your fields)
• metrics: (your metrics)
• limit: 50 (max rows to return)
• offset: 100 (skip first 100 rows)

Finding duplicate values:

Call get_data_from_fields with:

• attributes: ["detailed_listings.host_name"]
• metrics: ["COUNT(detailed_listings.host_name)"]
• filters: ["COUNT(detailed_listings.host_name) > 1"]
• order_by: ["\"COUNT(detailed_listings.host_name)\" DESC"]

This groups by the attribute, counts occurrences, and filters to only rows that appear more than once — surfacing duplicates.

SQL preview only:

Call get_sql_from_fields with the same field parameters to see the generated SQL without executing.

Filter Syntax

Filters use standard comparison expressions: =, >, <, IN (...), ILIKE, SEARCH(...), IS NULL, booleans, date ranges, and AND/OR combinations.

For the complete filter expression reference — including SEARCH, date handling, and type casting — see the filtering skill.

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Method 2: Deep Analysis

ask_deep_analysis_question

Call with:

• question (required): the analysis question
• agent (optional): agent name to use as analysis context — use list_agents to discover available agents and their associated domains
• conversation_id (optional): ID from a previous deep analysis call, for follow-up questions

question: "Analyze the relationship between host response time and review scores. Are there significant patterns?"
agent: "my_agent"

Returns:

• Markdown analysis report with findings
• Supporting data
• Suggested follow-up questions
• conversation_id for continuing the conversation

After Deep Analysis: Display the UI Link

After a successful ask_deep_analysis_question call, the response includes a ui_url field. Always display this URL to the user so they can view the full analysis in the Honeydew application.

Follow-up Questions

Use conversation_id from the previous response to ask follow-up questions that build on the prior analysis:

question: "Now break this down by room type — does the pattern hold across all types?"
agent: "my_agent"
conversation_id: "<id from previous response>"

Example Questions

Simple natural language:

• "What are the top 10 neighbourhoods by number of listings?"
• "Show me average price by room type."

Complex analysis:

• "Why did the average review score drop for listings in Brooklyn?"
• "What factors most influence listing price? Analyze the key drivers."
• "Compare superhost vs non-superhost performance across all metrics."
• "Identify unusual patterns in listing availability over the past year."
• "What are the characteristics of top-performing listings?"

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Combining Methods

For complex tasks, combine methods in sequence:

1. Discover — Use list_entities / get_entity to understand the model
2. Query — Use get_data_from_fields for precise, targeted queries
3. Investigate — Use ask_deep_analysis_question for root cause or trend analysis

Example Workflow

User: "Help me understand pricing patterns for Airbnb listings."

1. Discover entities: list_entities → find detailed_listings
2. Explore fields: get_entity for detailed_listings → find price, room_type, neighbourhood_cleansed
3. Targeted query: get_data_from_fields → price distribution by neighbourhood for Entire homes only
4. Deep dive: ask_deep_analysis_question → "What factors most influence listing price? Analyze correlations with room type, location, amenities, and reviews."

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Documentation Lookup

Use the honeydew-docs MCP tools to search the Honeydew documentation when:

• The user asks about query capabilities or features not covered in this skill
• You need to understand how the query API interacts with domains, parameters, or governance rules
• The user encounters unexpected query behavior and needs deeper context on how the semantic layer resolves queries

Search for topics like: "queries", "perspectives", "dynamic datasets", "parameters", "query API".

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Tip: Getting Distinct Values for a Field

To retrieve the distinct (unique) values of a field, include it in attributes and add a count metric in metrics. Use the entity's built-in count metric (e.g., entity.count) if available, or an ad-hoc count metric using COUNT(entity.field) on the field whose distinct values you want — never use COUNT(*). The metric forces aggregation, which groups by the attribute and returns one row per distinct value.

Example — distinct room types:

Call get_data_from_fields with:

• attributes: ["detailed_listings.room_type"]
• metrics: ["COUNT(detailed_listings.room_type)"]
• order_by: ["\"COUNT(detailed_listings.room_type)\" DESC"]

This returns each unique room_type along with its count, ordered by frequency. The count is a useful bonus — it tells you how common each value is — but the key point is that the query returns one row per distinct value.

This pattern is useful for:

• Exploring filter values — find out what values exist before writing a filter expression (see the filtering skill)
• Validating a new attribute — after creating a calculated attribute, check its distinct output values to confirm the logic is correct (see the attribute-creation skill)
• Understanding data distribution — see how data is spread across categories

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Best Practices

• Start with discovery — always check list_entities / get_entity before building queries, so you reference real fields
• Use structured queries for precision — when you know the fields, get_data_from_fields gives you full control and reproducible results
• Use deep analysis for insight — when the question is about "why" or requires investigating multiple dimensions
• Paginate large results — use limit and offset in get_data_from_fields to avoid overwhelming output
• Show SQL when debugging — use get_sql_from_fields to inspect the generated query
• Reference fields correctly — always use entity.field_name syntax in field parameters