# Query > 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. - Skill: `honeydew-ai/query` (Agent Skill) - Install (CLI): `npx skillmds@latest add honeydew-ai/query` - Raw SKILL.md: https://api.skillmd.com/api/skills/honeydew-ai/query/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Product & Planning - Author: honeydew-ai (https://skillmd.com/u/honeydew-ai) - Updated: 2026-09-10 - Page: https://skillmd.com/skills/honeydew-ai/query --- ## 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 gives you two ways to answer a question about the data, plus tools for reviewing analyses and queries that already ran. Choose between the two by asking whether the specification — which fields, which filters, which population — is given to you or chosen by you. | Method | Tool | Best For | | ------------------------- | ------------------------------------------------- | -------------------------------------------------------------------------------- | | **Deep analysis** | `initiate_analysis` + `monitor_analysis` | **Default for any question about the data.** The only path that applies the context layer — your organization's instructions, knowledge, and memory about which fields and definitions are correct for a given question. | | **Structured query** | `get_data_from_fields` / `get_sql_from_fields` | Executing a specification you already trust. Deterministic execution — and no check that the specification is right. | | **Explain a prior step** | `get_analysis_step_details` | User asks how a specific step in a prior analysis was calculated. | | **Browse past analyses** | `list_analysis_chats` | User wants to see past conversations or find a prior analysis. | | **Read a past conversation** | `get_stored_conversation` | User wants to read or review the full content of a specific past conversation. | | **Review query history** | `list_query_history` | User wants to inspect past query executions — what ran, from which client, the query behind a run, or to debug a failure. | --- ## When to Use Each Method ### Deep Analysis (`initiate_analysis` + `monitor_analysis`) **Use when:** - The goal is to understand, explain, or investigate rather than to retrieve a figure - 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`) - A figure is wanted whose fields you cannot identify, and finding them would take more work than handing over the question ### Structured Query (`get_data_from_fields` / `get_sql_from_fields`) **Use when:** - The user named the exact fields — "show me `detailed_listings.price` by `detailed_listings.room_type`" — so the specification is theirs - A prior analysis reported the fields and filters it used, and you want another slice or the raw numbers behind one of its claims - You are testing a specification rather than relying on one: validating a metric or attribute you just created, spot-checking a field's values, verifying a count - You need precise control over filters, ordering, and pagination for a figure whose definition is not in question **Do NOT use when:** - The goal is to understand, explain, or investigate rather than to retrieve a known figure — knowing the field names does not make a structured query the right tool - The answer depends on a choice you would be making about which field, filter, or population is correct — that choice is what the context layer records, and a structured query applies none of it - You would need more than one query to assemble the answer — hand the question over instead. **And if two have already run for one question, the third is a hard stop** **How it works:** - `get_data_from_fields` — executes the query and returns data rows - `get_sql_from_fields` — returns the generated SQL without executing. Use it when the SQL itself is what's wanted: handing it to the warehouse, dbt, or another tool, or bisecting a failing query to watch a fragment appear and disappear (see the **query-debugging** skill). The output is long machine-generated SQL — to understand what a field computes, read the model with `get_field` / `get_entity` instead Both take the same field parameters. --- ## 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) │ ├─► User wants to inspect past query executions (what ran / from which client / the query behind a run / a failure)? │ └─► list_query_history (filter by status/client/domain/user/time; most recent first) │ ├─► Investigation / "why" / "how" / trends / root cause / anything that │ takes more than one query to answer? │ └─► initiate_analysis + monitor_analysis │ Check this BEFORE the field-names question below — a question │ whose fields you happen to know is still an investigation │ └─► Retrieving a specific figure or row set? │ ├─► The specification is given, not chosen by you — the user named │ the fields, a prior analysis reported them, or the specification │ is itself what you are testing → get_data_from_fields │ (or get_sql_from_fields to preview SQL without executing) │ └─► You would be choosing it → initiate_analysis + monitor_analysis ``` --- ## Structured Query in Detail ### 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'"]`. A filter written over an aggregate instead applies after aggregation — see the **filtering** skill - **`order_by`** — sort order for results, e.g. `["entity.field DESC"]`. When a field is aliased, sort on the alias. - **`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 **Filtered breakdown — entire homes by neighbourhood:** 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"]` Attributes may span entities wherever a relation exists — adding `dim_host.host_is_superhost` to the list above joins host detail into the same breakdown. **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`. ### 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. --- ## Deep Analysis in Detail Deep analysis is a stateful, resumable analyst sub-agent: you delegate a goal to it and monitor it, rather than driving its steps. It plans its own investigation, keeps its own memory (the `conversation_id` is its scratchpad), can pause to ask a clarifying question, and can be aborted and resumed without losing what it has computed. You reach it through a polling loop rather than a single call, so a figure whose **definition is not in question** — one the user specified, one a prior analysis established, one you are spot-checking — is not worth delegating. Knowing the field names is not that test. The rules below follow from that — hand it the goal rather than a plan, correct it by interrupting and resuming rather than starting over, and open a fresh conversation for an unrelated task rather than putting it in this one's context. ### Asking the Question State the goal, not the plan. The analyst has context you do not: instructions, knowledge, and conventions held in the semantic layer about which fields, filters, and definitions are correct for a given question. An over-constrained question suppresses that context twice over — the steps you prescribe may be wrong because you are missing it, and a question that already contains a full plan gives the analyst little reason to load it. - Ask: "What's driving the revenue decline in Q3?" - Not: "Compare revenue by category and region for Q3 vs Q2, then rank categories by delta." Add precision through follow-ups instead, once the interpretation and plan come back — by then the analyst has its context loaded and a specific redirection lands on top of it rather than in place of it. Precision about what you want to know, though, not about how to compute it: a follow-up is as much a goal as the first question, so ask whether the pattern holds rather than naming the breakdown you expect. ### 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`. Each call returns only new messages since the last call. `status` is `"STILL_RUNNING"` while the analysis works and `"DONE"` once it has stopped; `"NO_CHAT_CURRENTLY_RUNNING"` means it had already finished before you polled — read that one with `get_stored_conversation` rather than polling on. `status` and `stop_reason` are separate fields and `DONE` belongs to both. `status` says whether the run has stopped; once it has, `stop_reason` says what kind of ending it was. `NO_CHAT_CURRENTLY_RUNNING` is never a `stop_reason`. | `stop_reason` | Meaning | What to do | |---------------|---------|------------| | `"DONE"` | Finished normally | Read `responses` array for the final report | | `"ASK"` | Analysis paused to ask a clarifying question | Read `responses` for the question, answer with a new `initiate_analysis` on the same `conversation_id` | | `"FAIL"` | The run ended without producing an answer | There is nothing in `responses` to report. Do not present partial progress as a result — say the analysis did not complete. A follow-up on the same `conversation_id` starts fresh rather than resuming | | `"ABORTED"` | This execution run was stopped | **Not terminal.** Conversation state is preserved — continue with a new `initiate_analysis` on the same `conversation_id` | **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. ``` # Example initiate_analysis(question="What's driving revenue across cuisine types?", agent="listings-analyst") → { 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: "..." }] } ``` ### Feedback on a Finished Analysis `provide_analysis_feedback` attaches feedback to a `conversation_id`: - **Positive**: a short affirmative string, e.g. `"Good"` - **Negative**: `:
` 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` There is one entry per conversation, covering the conversation as a whole, and a second call overwrites the first — so **the user's judgement always wins**. Never overwrite feedback the user gave with your own. **From the user** — trigger when the user says things like: "that was correct", "that's wrong", "the data looks off", "great analysis", "this is incorrect because...". Record what they said. **On your own initiative** — only from evidence, never from satisfaction. You asked the question, so "it answered what I needed" or "the numbers look plausible" is self-assessment: biased in your own favour, and it dilutes the feedback curators review. What qualifies is a check you can name: - The result disagrees with, or confirms, an independent source — a structured query over the same fields and filters, a figure the user supplied, a documented value - A data defect visible in the result — impossible nulls, counts inflated by a fan-out join, two routes to the same number disagreeing - A conclusion the analysis's own numbers do not support - A chart that misrepresents the data it plots Tag it `[agent]` and name the evidence, so a review pass can separate agent-sourced feedback from the user's. Keep `:` leading on negative feedback so the reason stays machine-readable: - `"Data Issue: [agent] reported total revenue 4.2M, but get_data_from_fields on order_detail.revenue with the same date filter returns 3.8M"` - `"[agent] verified against an independent get_data_from_fields call on order_detail.revenue by menu.item_category for 2021 — figures match"` With no check to name, leave no feedback. That is a correct outcome, not a skipped step. ### 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="Does that pattern hold across room types, or is one of them driving it?", conversation_id="abc123" ) ``` ### Continuing Inside an Analysis Once a conversation exists, each next question is a choice between a follow-up and dropping out to a structured query. **Send a follow-up when:** - The next step needs reasoning rather than a different breakdown — "does the pattern hold if we control for market size?", "why is that outlier there?" - The next step builds on groups the conversation already computed. A structured query cannot express these ad hoc — ranks, table calculations, filtering on a rank exist as calculated attributes in the model, not as something a query can compute on the fly. A ranking the analysis has already built is reusable for free inside the conversation, and outside it would have to be modelled or rebuilt by hand **Drop out to `get_data_from_fields` when:** - You need the raw numbers behind a specific claim the analysis made - You want another slice of a metric the analysis has already established - It is a distinct-values or count lookup - You want a result that is deterministic and reproducible Only work done inside the conversation shows up at the `ui_url` you gave the user. Numbers you produce with a structured query afterwards won't be there, so report them yourself rather than leaving a gap between what the app shows and what you say. A finished analysis is also the best source of field names for structured queries: it reports the exact `entity.field` references and filters it used, and `get_analysis_step_details` gives the semantic query behind any step. Feed those attributes, metrics, and filters into `get_data_from_fields` instead of rediscovering them from `list_entities`. The compiled SQL a step also returns is not an input to `get_data_from_fields` — it is for reading or handing to the warehouse. ### Interrupting and Resuming an Analysis `abort_analysis` with the `conversation_id` stops the current execution run. It is an interrupt, not a cancel: all conversation state survives, including groups already computed. Resume with a new `initiate_analysis` on the same `conversation_id` and the analysis continues from the aborted point, reusing prior results rather than restarting. **The question you send on resume is the correction.** Interrupt-and-resume is how you steer an analysis mid-flight, rather than waiting for a wrong direction to run to completion and correcting afterwards. Always abort when the user asks. On your own initiative, the bar is a contradiction with what the user asked for — not a difference from how you would have done it: - The `interpretation` restates the question as something the user did not ask - A `step_insight` shows it working from a premise the user's request rules out — a filter that excludes the population in question, a date range other than the one asked about - The user has since said something that invalidates the direction A plan that differs from the one you had in mind is not a misread. You handed over the goal because the analyst has context you lack, so an approach you would not have chosen is the expected case rather than a fault. Neither is a step whose purpose isn't obvious yet — the plan may need it later. When the analysis is merely slower or less direct than you would have been, let it finish. ### 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?" --- ## Delegating to a Subagent A subagent does not inherit the skills you have loaded, and your brief outranks any skill it does load. Put these in it: 1. "Load the `honeydew-ai:query` skill before any Honeydew work — you do not inherit mine. Add `honeydew-ai:model-exploration` if you will be discovering entities or fields." 2. "This task is [an investigation / a set of lookups]. For investigation, call `initiate_analysis` with agent `` and hand it the goal, not a plan. For the lookups, the exact fields are given below." 3. If an analysis already exists: "Continue conversation `` rather than starting a parallel investigation." Name the agent yourself in point 2, looked up with `list_agents` — `initiate_analysis` needs either `agent` or `conversation_id`. Scope tool guidance per question rather than per subagent: a brief covering both a count and an investigation gets governed by whichever instruction it states. Never make the analysis path conditional on the subagent judging the task multi-step. --- ## 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) ``` --- ## Reviewing Past Query Executions To review or inspect **queries that already ran** — what ran, from which client (BI tools, SQL interface, MCP, deep analysis), the semantic definition or compiled SQL behind a run, who ran it and when, and debugging failures — use `list_query_history` (from the `honeydew` MCP server). It lists queries previously executed through the semantic layer, most recent first, with each query's status, error message, owner, client, domain, timestamps, warehouse query ID, and a link to open it in the app. This is a distinct workflow from querying data — see the dedicated **query-debugging** skill. --- ## Combining Methods Methods chain in both directions. **Discover → query → investigate** — explore the model, spot-check a field with a structured query, then hand the actual question to deep analysis. **Investigate → query** — let deep analysis answer the question, then use the field names and filters it reports to run fast deterministic cuts of its result. See **Continuing Inside an Analysis** for which direction a given follow-up belongs in. ### Example Workflow User: "Help me understand pricing patterns for Airbnb listings." 1. Orient: `list_entities` / `get_entity` → find `detailed_listings` and what it holds 2. Pick the analyst: `list_agents` → the agent whose domain covers listings 3. Hand over the goal: `initiate_analysis` → "What factors most influence listing price?" — show the user the `ui_url` it returns 4. Poll `monitor_analysis` and report progress as steps produce findings 5. Cut its result: `get_data_from_fields` with the fields the analysis reported → the exact numbers behind the driver it identified --- ## 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**. The count can also be filtered on. A breakdown by `detailed_listings.host_name` with `filters`: `["COUNT(detailed_listings.host_name) > 1"]` returns only the values occurring more than once — which is how you surface duplicates. 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 - **Use deep analysis for insight** — anything beyond a lookup goes to `initiate_analysis`: "why", "how", trends, drivers, or any question needing more than one query. Let the analysis engine plan the investigation instead of chaining structured queries by hand - **Use structured queries for a settled specification** — when the fields and filters were given to you rather than chosen by you, `get_data_from_fields` executes them exactly and reproducibly - **Discover before you query, not instead of delegating** — check `list_entities` / `get_entity` so you reference real fields; a field list in hand is not a reason to answer the question yourself - **Delegate the goal, not the plan** — an over-specified question suppresses the semantic-layer context the analyst would otherwise load, and the steps you prescribe may be wrong precisely because you lack that context. Add precision in follow-ups - **Stay in the conversation for reasoning, drop out for slices** — follow up when the next step needs reasoning or reuses groups the analysis already computed; use `get_data_from_fields` when you just need deterministic numbers - **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 - **Debug against the model, not the generated SQL** — to understand what a metric or attribute computes, read `get_field` / `get_entity`; the compiled SQL is long machine output and is rarely the shortest route to the answer - **Reference fields correctly** — always use `entity.field_name` syntax in field parameters