Skill v1.0.2

version: "1.0.2" name: query description: Use when the user wants to query or analyze data through the Honeydew semantic layer — including natural language analysis questions, deep multi-step investigations, and structured queries. For model/field discovery use the model-exploration skill.

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 workspace-branch skill for the full workspace/branch tool reference.

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.

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 (initiate_analysis + monitor_analysis)

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)

Decision Flow

User asks a data question
    │
    ├─► User asks to explain / drill into a step from a prior analysis?
    │       └─► get_analysis_step_details (step_id from monitor_analysis)
    │           Returns semantic query, data results, and SQL for that step
    │
    ├─► User wants to browse past conversations / find a prior analysis?
    │       └─► list_analysis_chats (paginated list, newest first)
    │
    ├─► User wants to read the content of a specific past conversation?
    │       └─► get_stored_conversation (conversation_id from list_analysis_chats)
    │
    ├─► 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 → initiate_analysis + monitor_analysis (plain English, any complexity)
    │
    └─► Plain English question / investigation / "why" / trends?
            └─► initiate_analysis + monitor_analysis

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.

Method 2: Deep Analysis

initiate_analysis + monitor_analysis

Deep analysis is a two-step async process:

Step 1 — start the analysis:

Call initiate_analysis with:

• question (required): the analysis question
• agent (required for new conversations): agent name — use list_agents to discover available agents and their associated domains. Workspace and branch must be set before calling `list_agents` (agents are workspace-scoped)
• conversation_id (optional): ID from a previous call, for follow-up questions

Returns a conversation_id immediately. New conversations also return a ui_url — always display this URL to the user so they can follow the analysis in the Honeydew application.

Step 2 — poll until done:

Call monitor_analysis repeatedly with the conversation_id until status is "DONE". Each call returns only new messages since the last call.

`stop_reason` values — what they mean:

Report progress when it's meaningful to the user — not every poll, but not silently either. Use judgement:

• On interpretation / plan: tell the user what the analysis intends to do
• On every step_insight with a substantive finding: post a one-liner describing what was found — e.g. "58 menu items found, top is The King Combo at $431M"
• If several steps have passed without a user-facing update, post a brief aggregate — e.g. "Analyzed pricing by neighbourhood, computed averages, filtered outliers" — so the user knows progress is being made
• On internal errors, retries, or backtracking steps: skip reporting — the user doesn't need to know the agent corrected itself, only that meaningful progress is being made

When status is "DONE", the final user-facing report is in the responses array. If the user then expresses satisfaction or dissatisfaction with the result, call provide_analysis_feedback with the conversation_id:

• Positive: a short affirmative string, e.g. "Good"
• Negative: <Reason>: <details> where Reason is one of Chart Issue, Data Issue, Wrong Judgement, or Other — e.g. "Data Issue: revenue figures don't match our BI tool"
• Clear existing feedback: pass null

Trigger this when the user says things like: "that was correct", "that's wrong", "the data looks off", "great analysis", "this is incorrect because..."

# Example
initiate_analysis(question="Analyze revenue by cuisine type", agent="my_agent")
→ { conversation_id: "abc123", ui_url: "https://..." }
 
# Poll loop — report to user after each call that has new content
monitor_analysis(conversation_id="abc123")
→ interpretation + plan received → tell user what the analysis will do
 
monitor_analysis(conversation_id="abc123")
→ step insight received → report: "Top item is Indian at $1B"
 
monitor_analysis(conversation_id="abc123")
→ step insight received → report: "% contribution: Indian leads at 34%, followed by Italian at 22%"
 
...
 
monitor_analysis(conversation_id="abc123")
→ { status: "DONE", responses: [{ text: "..." }] }

Follow-up Questions

Use conversation_id from the previous analysis to ask follow-up questions that build on prior state. Start a new conversation (omit `conversation_id`) when the topic changes completely — reusing a conversation for an unrelated question carries stale context into the new analysis and can skew results.

initiate_analysis(
  question="Now break this down by room type — does the pattern hold?",
  conversation_id="abc123"
)

Aborting and Resuming an Analysis

Aborting stops the current execution run but preserves all conversation state — groups already computed remain available. Resume by sending a new initiate_analysis with the same conversation_id; the analysis will reuse prior results rather than restarting from scratch.

Always abort when the user asks. Initiating an abort on your own (without the user asking) is reserved for extreme cases where the analysis is clearly going badly off track — normally let it finish and redirect via a follow-up question.

Explaining a Prior Analysis Step

When the user asks to explain, review, or drill into a specific step (e.g., "how was that calculated?", "explain the cuisine breakdown", "what happened in step 5"):

1. Identify the step_id from monitor_analysis progress messages (e.g. "STEP/5")
2. Call get_analysis_step_details with conversation_id and step_id

Returns:

• The semantic query used (attributes, metrics, filters)
• Context resolved (which entities/fields Honeydew selected)
• Context items used (if any instructions or knowledge were applied)
• Data results from that step
• The SQL generated for that step

References in the response can be expanded for deeper inspection:

• Field references (e.g. entity.field_name) → get_field — shows the full definition, useful if the user wants to understand how a metric or attribute is calculated
• Context item references → get_context_item — shows the instruction or knowledge applied during that step

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?"

Step explanation (use `get_analysis_step_details`, not a new question):

• "How was that calculated?"
• "Explain the cuisine breakdown."
• "Show me what happened in that step."
• "What fields were used there?"

Browsing Past Analyses

list_analysis_chats

Returns a paginated list of past analysis conversations for the current workspace, sorted newest first.

• Admins see all conversations; non-admins see only their own.
• Each entry includes: conversation_id, title, domain, agent, creation time, user feedback, and the display name of the user who created it.
• Use limit and offset for pagination.

list_analysis_chats(limit=20, offset=0)

Use this when the user asks to:

• "Show me my recent analyses"
• "Find the analysis I ran last week on revenue"
• "List all conversations in this workspace"

get_stored_conversation

Returns all messages from a past analysis conversation. Use this to read the full content of a specific conversation identified via list_analysis_chats.

• is_complete in the response indicates whether the conversation reached a terminal state.
• with_step_ids: false (default) — returns only final responses and plan/interpretation messages. Use this for a readable summary.
• with_step_ids: true — also includes step_start and step_insight progress entries. Use this when you need to identify specific steps to pass to get_analysis_step_details.

get_stored_conversation(conversation_id="abc123")
get_stored_conversation(conversation_id="abc123", with_step_ids=true)

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 initiate_analysis + monitor_analysis 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: initiate_analysis → "What factors most influence listing price? Analyze correlations with room type, location, amenities, and reviews."

Documentation Lookup

Use the search_docs and query_docs_filesystem tools from the honeydew MCP server 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".

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

Presenting Results

Present analysis results clearly — format tables, highlight key numbers, and surface what's notable, unexpected, or actionable.

If your environment has visualization tools, render visualizations when they would reveal patterns faster than text — e.g. trends, distributions, or ranked lists of 5+ items. Skip it for 2–3 numbers or a single yes/no conclusion. When it might help but isn't a clear win, offer rather than render.

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
• Report meaningful progress, not every step — surface a one-liner when a step produces a substantive finding; skip internal retries and error-recovery steps the user doesn't need to see
• Explain a prior step — use get_analysis_step_details with the step_id; the response includes the semantic query, data results, and SQL for that step
• 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