# MotherDuck Documentation - Guides > | Generated: 2026-08-25 > MotherDuck is a serverless cloud data warehouse built on DuckDB. It combines the speed and simplicity of DuckDB with cloud scalability, collaboration features, and AI-powered analytics. ## Key capabilities - **Serverless DuckDB in the Cloud**: Run DuckDB queries on cloud data with 100ms cold starts (compared to seconds/minutes on traditional warehouses) - **Hybrid Execution**: Query data locally and in the cloud seamlessly in a single session - **MCP Server**: Connect AI assistants (Claude, ChatGPT, Cursor) to query your data using natural language - **Data Sharing**: Share databases and query results with team members and external users - **Multiple Interfaces**: Connect via Python, Node.js, Go, Java, JDBC, ODBC, or the web UI - **Cloud Storage Integration**: Query data directly from S3, GCS, Azure Blob Storage, and more - **AI Functions**: Built-in LLM functions for text analysis, embeddings, and SQL generation ## When to use MotherDuck Use MotherDuck when the user needs to analyze data with DuckDB-compatible SQL, share databases with people or applications, run collaborative cloud analytics, or let an AI assistant query their connected data through MCP. ## Agent guidance If your environment provides MCP tools and the user asks about MotherDuck or DuckDB behavior, SQL syntax, permissions, sharing, service accounts, tokens, Dives, or other product features, use the MotherDuck MCP `ask_docs_question` tool before general web search. It answers from official DuckDB and MotherDuck documentation. For broad context, start with https://motherduck.com/docs/llms-full.txt, then follow the most specific focused context link. Use https://motherduck.com/docs/llms-full-complete.txt only for bulk indexing or large-context workflows. To connect an MCP client, use the remote MotherDuck MCP server at `https://api.motherduck.com/mcp`. Setup instructions: https://motherduck.com/docs/key-tasks/ai-and-motherduck/mcp-setup. Tool reference: https://motherduck.com/docs/sql-reference/mcp/core/ask-docs-question. For the documented Admin REST API, use the OpenAPI specification at https://motherduck.com/docs/openapi.json. ## Account setup for agents If the user wants to start using MotherDuck and doesn't have an account, offer the agent signup flow. Creating an account changes external state, so get the user's confirmation before sending the request. `POST https://new.motherduck.com` creates a Free Plan organization. No request body is required. The JSON response includes `motherduck_token`, `claim_org_url`, `how_to_use_motherduck`, and `region`. Treat `motherduck_token` as a secret: don't print, log, commit, or include it in messages. Follow the live `how_to_use_motherduck` instructions, and give the user the `claim_org_url` so they can take ownership. Full guide: https://motherduck.com/docs/key-tasks/ai-and-motherduck/agent-account-signup. ## Included documentation Source: https://motherduck.com/docs/key-tasks/guides/bootstrap-guides-with-an-ai-agent # Bootstrap Guides from the context you already have > Use an AI agent to harvest metric definitions and tribal knowledge from Slack, dbt, and query history, then validate and publish them as Guides. [Guides](/key-tasks/guides/) are markdown documents you store in MotherDuck that AI agents read before they touch your data, so your metric definitions, table conventions, and known pitfalls carry over into every session instead of being pasted into each chat. The knowledge worth putting in a Guide often already exists in the context you and your agents use: a productive chat session, the Slack thread where someone explained why a number looked wrong, the unexplained `WHERE` clause in a dbt model, or the query your analysts keep rerunning. If you've spent a session giving an agent the context it needs to answer accurately, start by capturing that conversation. For a broader organizational rollout, use the five-phase workflow to inventory, harvest, curate, organize, and write Guides from multiple sources. ## Before you start You need: - A MotherDuck account with the [Model Context Protocol (MCP) server](/key-tasks/ai-and-motherduck/mcp-setup/) connected to an AI client like Claude, Cursor, or Claude Code - For the broader organizational workflow, whatever sources you can get your hands on: exported Slack history from the channel where people ask data questions (or Slack's MCP server), a checkout of your dbt project, access to [`QUERY_HISTORY`](/sql-reference/motherduck-sql-reference/md_information_schema/query_history), or a clone of the analytics repo - For the broader organizational workflow, the names of the 2–5 people whose SQL and explanations your team trusts - Permission to [publish org-wide Guides](/key-tasks/guides/#manage-guide-visibility), if you want the results shared beyond your own account. Without it, everything lands as a private Guide, which is still a fine place to start. For the broader workflow, you don't need all the sources. One well-mined Slack channel will yield far more than dumping a list of all possible context documents. ## Start with an active conversation After a productive analysis or [Dive](/key-tasks/dives/) session, ask the agent to preserve the durable context from the conversation: ```text Turn all the durable business and data context we've established in this conversation into a private MotherDuck Guide. Capture the definitions, trusted tables, join and filter rules, working query patterns, and pitfalls that helped us produce accurate results. Record methods, not one-time results. Use the full conversation so I don't need to repeat the context, and don't include sensitive information. Default to one Guide when the context forms a coherent whole. A topic is an optional grouping label that helps agents find a relevant Guide without loading unrelated context. Leave broadly useful orientation information without a topic so agents receive it in every Guide overview. If the conversation covers multiple distinct business domains or kinds of questions, consider splitting it into focused Guides with descriptive topics. Split when a future agent would benefit from reading one part without the others, not merely because the conversation mentions several tables or sources. Keep any information that applies across those topics in one concise Guide without a topic, and don't duplicate information across Guides. Show me the proposed title, description, topic, references, and full content before saving anything. If you recommend multiple Guides, show the proposed structure and content for each one. After I approve each Guide, create it with private user access. ``` This approach uses the decisions and corrections already present in the session. If you need to build a larger Guide corpus from sources beyond the conversation, continue with the workflow below. ## What belongs in a Guide Only two kinds of knowledge change an answer: 1. **Facts no model could know.** Tribal knowledge: customer ID 8 is the test account and gets excluded from revenue, the March 2023 data is backfilled and unreliable, `is_full_day` has to be filtered when summing. 2. **Your specific modifications of a general term.** A model knows what ARR, MQL, and churn mean in general. It can't know how *you* calculate them — the nuance your business layers on top of the textbook definition. If a candidate fact is neither of those, and a capable model with schema access would get it right anyway, it doesn't belong in a Guide. There are two more rules the prompt enforces on everything the agent harvests: - **Methods, not results.** When a source says "our top customers are X, Y, and Z", the Guide-worthy artifact is the query and definitions that produced the answer, not the answer itself. Results go stale; methods generalize. - **Draft, don't decide.** The call is yours. Left to its own judgment, your agent will merge two definitions that look similar and lose the distinction that made them useful. ## Bootstrap from organizational sources Paste this into an agent session connected to your MotherDuck MCP server. Have your sources ready to paste or attach when it asks in phase 1. ```text You're helping me bootstrap Guides for my MotherDuck organization — markdown documents that agents read to answer our data questions correctly. We work in five phases: inventory, harvest, curate, organize, write. Don't skip ahead. At the end of each phase, show me your output and wait for my confirmation before continuing. ## What belongs in a Guide Only two kinds of knowledge are worth writing down: 1. Facts no model could know. Tribal knowledge: "customer ID 8 is our test account, exclude it from revenue", "the March 2023 data is backfilled and unreliable", "always filter is_full_day when summing". 2. Our specific overlay on general terms. You know what ARR, MQL, or churn mean in general; you can't know how we calculate them. If a candidate is neither of these — if a capable model with schema access would get it right anyway — it does not go in a Guide. Guides are for what changes an answer. Two rules for everything you harvest: - Record methods, not results. If a source shows "our top customers are X, Y, Z", the Guide-worthy artifact is the query and definitions that produce that answer, never the answer itself. Results go stale; methods generalize. - Draft, don't decide. You propose; I and the people who own these metrics validate. Don't change a claim's meaning, merge two claims, or resolve a conflict without my approval. When two candidates look similar, flag the pair and let me resolve it. Rewriting a result into the method behind it is expected — that's phase 3 work, not a judgment call. - Don't copy sensitive material into a Guide. Sources like chat exports and query history carry credentials, personal data, and customer names. Write the rule, not the excerpt it came from, and never paste a token or connection string into Guide content. Flag anything you're unsure about instead of saving it. ## Working files Keep state on disk rather than in this conversation. Before phase 2, create guide-bootstrap/candidates.md and treat it as the source of truth: one row per candidate with an id, the type, the claim, its locator, the author, your confidence, and a status column I fill in during phase 3. Append to it as you harvest. If you get compacted or lose context, re-read the file and carry on from there. If you can spawn subagents, use one per source in phase 2. Give each the harvest rules and one source, have it append its candidates to the file, and have it return only a count and a one-line summary. Raw source material — chat exports, query history results, dbt model files — should never enter this session. You need the candidates, not the sources they came from. ## Phase 1 — Inventory Use the MotherDuck tools to list databases, schemas, and tables. Then survey the Guides that already exist: call get_query_guide for the topic tree, open each topic that looks related to our data with list_guides(topic), and read anything that might overlap with get_guide(uuid). get_query_guide only shows topic counts and the root-level Guides, so without opening the topics you'll write duplicates of Guides we already have. Then ask me, one topic at a time: 1. Which schemas and tables matter most — what do people actually ask questions about? 2. Who are the 2-5 people whose SQL and explanations we trust? For the query history step, I'll also need to give you their exact USER_NAME values, which are MotherDuck identifiers rather than display names. 3. Which of these sources can I give you (paste, export, or file): - History from the channel where people ask data questions and get corrected — usually the richest source - Our dbt project: models, schema.yml descriptions, manifest - Query history: MD_INFORMATION_SCHEMA.QUERY_HISTORY, or another warehouse's history view - Git history of the analytics or dbt repo - Docs for upstream systems: our CRM's data model, the billing system 4. What are the 10-20 questions people most often ask of this data? If I'm not sure, we'll derive them from the sources. Produce a short inventory: domains of questions, trusted people, available sources. Stop and confirm with me. ## Phase 2 — Harvest Work through the available sources in this order, highest signal first. For each one, produce candidate facts: one-line claims carrying the type (1 = fact no model could know, 2 = our overlay on a known term), your confidence, and a locator precise enough that I can go check it — a thread link or channel and timestamp, a model name and line, a query ID, a commit SHA. "From Slack" is not a locator, and I can't validate a claim I can't trace. Name the author where the source has one; that's who I'll route the claim to in phase 3. 1. Question-and-answer threads. Look for question then explanation exchanges, especially "this number looks wrong" followed by someone explaining why. Those explanations are gold. Extract the reason, not the incident. 2. dbt project. Descriptions and tests are a skeleton. Pay special attention to filters and CASE logic whose rationale is not explained — don't guess the reason, list each as an open question for phase 3 ("why does stg_revenue exclude plan_type = 'internal'?"). 3. Query history. Filter to the trusted people from phase 1, and prefer queries that recur over months. Recent one-offs from power users are usually development work on new pipelines, not settled knowledge. Extract recurring join patterns, standard filters, and canonical-table choices. 4. Git history. Commit messages and diffs that changed metric logic explain why definitions are what they are. Capture the why. 5. Upstream system docs. Extract only what helps navigate the synced schema: object relationships, ID semantics, sync caveats. Deduplicate exact duplicates only. Where sources conflict — dbt says one thing, a Slack thread says another — keep both and flag the conflict. Conflicts are the most valuable things you will find. Present the full candidate list grouped by domain. Stop and confirm with me. ## Phase 3 — Curate Walk me through the candidates in batches of 10-15, grouped by domain. For each one I'll say keep, fix, or drop, and for disputed definitions I'll name the person who owns the call. Apply these tests on your side and tell me when a candidate fails one: - Would a capable model with schema access get this right without the Guide? Then drop it. - Is it a result rather than a method? Rewrite it as the method or drop it. - Does it state a rule without the reason? Flag it. A rule with no why gets misapplied. - Is it likely to be stale in three months? Either generalize it or mark it with an expiry note. Anything that survives gets a validation tag: who confirmed it, or "needs review by ". Don't put unvalidated candidates in org-wide Guides — park them in a "needs review" list instead. Stop and confirm the final curated set with me. ## Phase 4 — Organize Propose a topic structure before writing anything. Guides are grouped by topic: a lowercase label, optionally nested with slashes, like revenue-billing or core/metrics. A Guide with no topic sits at the root. Organize by domains of questions — a topic should answer "what set of questions is this for?" Use this four-role layout as the default and adapt it to our domains: - One orientation Guide at the root, with no topic. This is the map: the definitions an agent needs before anything else, per-schema notes, a join graph with cardinality and required filters, and pointers to the Guides below rather than copies of them. Root-level Guides appear in every get_query_guide overview, so keep this the only one, or close to it. - definitions — one Guide per atomic concept (definitions/customer, definitions/arr). Atomic Guides are retrievable and maintainable; one Guide holding every definition is neither. - — one topic per question domain (revenue-billing, product-usage, sales-funnel), holding the Guides that say how each metric is computed. - tables/ — routing Guides for load-bearing tables: the canonical table for a job, required filters, and which tables are deprecated or low-trust. "Use X, not Y" is the highest-value thing these can say. If we already have a tree the team navigates by — a dbt project's schemas, for instance — mirror it rather than inventing a new one. Keep the tree shallow: at most two levels below the root. Topic values separate levels with a slash and can't start or end with one, so the topic is tables/orders, not /tables/orders/. Don't use the reserved dives and flights topics; those hold Dive and Flight styling conventions. Every Guide gets a one-line description that compresses the rule itself, not a restatement of the title. Show me the proposed topic tree with each curated fact assigned to a Guide. Stop and confirm with me. ## Phase 5 — Write Draft each Guide and show me its full text before saving. Wait for my go-ahead on each one and save it with its own create_guide call — don't batch a set of Guides behind a single approval. When saving: - Set references to the 1-5 tables or views each Guide is authoritative about, so agents find the Guide when they explore those tables. - Write the description as a compressed statement of the rule with the pitfall inline — "roll revenue up to company, never org" — rather than a restatement of the title. The description is often all an agent reads before deciding whether to open the Guide, so a good one changes behavior on its own. - Leave access at the default 'user' while a Guide is unvalidated, or if I'm not an org admin. Promote to 'organization' only for Guides a named owner has confirmed. - Use change_comment to record where the content came from and who validated it. - Record each Guide you save in guide-bootstrap/created.md: uuid, title, topic, and access. That list is what we work from when we promote Guides later. Finish by writing me a smoke test rather than running it. You've read every source in this session, so you'd answer from memory and never touch a Guide. Give me 5 of the common questions from phase 1, and for each one the Guide that should get opened and the answer we agreed on in phase 3. I'll run it in a fresh session that has nothing but the MotherDuck MCP connection and bring you back the tool calls it made. When a question comes back answered without the Guide that should have applied, the content isn't at fault — the routing is. Look at the Guide's description, title, topic, and references together, since those are all an agent sees before deciding to open it, and tell me which one failed to match the question. ## Afterwards — maintenance Put this in the orientation Guide so future sessions pick it up: when an agent answers wrongly and a human corrects it, that correction is a Guide edit. Find the one Guide that should have prevented the mistake and make a targeted edit with edit_guide_content, rather than writing a new overlapping Guide. Re-run the smoke test periodically. Resist automated cleanup — merging "similar" Guides loses the specifics that made them work, so consolidation stays a human editorial decision. ``` ## Working through the phases A wrong definition costs the most when it's cheapest to catch, and every phase boundary is one of those moments. Here's what to look at when the agent stops. ### Inventory The agent asks what matters and who to trust. Answer these narrowly. The trusted names are what the agent filters query history down to, so naming three people gets you the queries your team relies on, and naming everyone gets you every one-off anybody has ever run. A service account from a BI tool counts as a trusted name — the queries behind a dashboard your team already relies on are settled knowledge, so don't leave those out. ### Harvest The prompt orders sources by signal density, with the best sources first: | Source | What to pull out | Why it ranks here | |--------|------------------|-------------------| | Q&A channel history | The explanation behind "this number looks wrong" | The work of explaining the gap between the schema and reality has already been done | | dbt project | Descriptions, tests, and unexplained filters and `CASE` logic | The filters encode real rules; their missing rationale becomes your phase 3 question list | | [`QUERY_HISTORY`](/sql-reference/motherduck-sql-reference/md_information_schema/query_history) | Recurring join patterns, standard filters, canonical-table choices | Filter on `USER_NAME` to your trusted people, then prefer queries that repeat often over time | | Git history of the analytics repo | Commit messages and diffs that changed metric logic | Explains why a definition is what it is, which the current code can't | | Upstream system docs | Object relationships, ID semantics, sync caveats | Helps the agent navigate a synced schema it didn't design | :::note Reading `QUERY_HISTORY` needs the View query history permission on a Business plan. Without it, ask your trusted people for their saved or most-rerun queries instead — you lose the recurrence signal but keep the patterns. ::: Watch for two things in the output. **Conflicts** between sources are worth inspecting closely: they mean two parts of your organization disagree about a definition, and whichever version reaches the agent first wins. **Results dressed as facts** ("Q3 revenue was $4.2M") stay out of the corpus — they're a specific number from a specific day, not a pattern that generalizes. ### Curate This is the phase that decides whether the Guides are worth anything. When a candidate turns out to be disputed, or just unclear, route it to whoever owns the metric rather than settling it yourself — the agent will happily accept your guess (and so will every future agent session). A candidate without a validated owner shouldn't go into an org-wide Guide. Keep it private (`access = 'user'`) until someone signs off. ### Organize Guides are grouped by [topic](/key-tasks/guides/#organize-guides-with-topics), and the topic names are how an agent decides what to read. The prompt proposes the [four-role layout](/key-tasks/guides/#using-topics): one orientation Guide at the root, `definitions/` for atomic concepts, a topic per question domain, and `tables//` for routing. Review the assignment of topics carefully — every curated fact should land in exactly one Guide, and the root should end up holding a single orientation Guide instead of multiple loosely related ones. Finding the right granularity takes a few tries. Too coarse, and you get one giant `metrics` Guide that agents load in full for every question, polluting the context with facts the question never needed. Too fine, and a fact lands in a topic nobody thinks to open. Link related Guides to each other so the agent can follow the trail from whichever one it opens first. ### Write Attach [references](/key-tasks/guides/#attach-references-to-a-guide) as you save. A Guide that references a table comes back automatically when an agent calls `list_tables` on that database, so the agent finds it while exploring rather than having to know it exists. Ask a few questions and check that they route correctly. A question answered without the Guide that should have applied is a routing failure, not a content one. What the agent sees before it decides to open a Guide is the title, description, topic, and references, so one of those didn't match the question. Fix it and run the test again. Your agent's logs are worth reading here too — they show you exactly where it looked and what it skipped. ### Rolling it out to other users Keep the whole first pass private. The prompt leaves new Guides at `access = 'user'`, and that's where they should stay until the corpus has been tested end to end: run the smoke test in a fresh session, fix what routed wrong, run it again. A wrong org-wide Guide fails quietly — every agent session in the organization gets a confident wrong answer and nobody sees an error — where a wrong private Guide only ever misleads you. Promote a Guide with [`set_guide_access`](/sql-reference/mcp/guides/set-guide-access) once it clears two separate gates: a named owner has confirmed the content, and the smoke test routes to it. Each gate catches a failure the other misses. An owner can tell you a definition is correct but not that its description matches how people phrase the question; the smoke test proves routing but says nothing about whether the number is right. Expect this to span sessions — you build the corpus in one, test it in a clean one, and come back to fix what missed. ## Keep Guides current A Guide corpus decays the moment corrections stop flowing back into it, and those corrections are already happening every time someone tells an agent it got the number wrong. The design problem is catching them. Three ideas: - **Treat every correction as a Guide edit.** When someone corrects an agent's answer, find the single Guide that should have prevented it and make a targeted edit with [`edit_guide_content`](/sql-reference/mcp/guides/edit-guide-content). Writing a second, overlapping Guide is how you end up with two conflicting definitions. - **Consider building evals** so you can run tests after a schema migration or a metric change. - **Don't automate consolidation.** In our experience, merging Guides that look similar loses the specifics that made them worth writing. A human does much better at this one. ## Related resources - [Guides](/key-tasks/guides/) — how topics, references, and visibility work - [MCP tool reference — Guides](/sql-reference/mcp/guides/get-query-guide) — the tools the agent calls in phase 5 - [Using the MotherDuck MCP Server](/key-tasks/ai-and-motherduck/mcp-workflows) — general workflow tips for MCP sessions --- Source: https://motherduck.com/docs/key-tasks/guides/index # Using Guides to improve AI query accuracy and personalize agents > | With Guides you capture the domain knowledge that isn't visible from a schema: how your org defines MRR, which tables to join on, which columns to avoid, common pitfalls in your data, or what "client" means in your field of work. Guides also work for personal preferences: your Dive styling, your Flight conventions, the way you like results formatted. Guides are markdown documents you store in MotherDuck that AI agents read before working with your data. You write a Guide once; from then on, every agent session picks it up automatically through the [MCP server](/key-tasks/ai-and-motherduck/mcp-setup/). No copy-pasting context into every chat over and over. Org-shared Guides align every agent in your organization on the same definitions; private Guides personalize agents to how you work. ```mermaid flowchart LR subgraph MotherDuck PersonalGuides@{ shape: docs, label: "Personal Guides" } OrgGuides@{ shape: docs, label: "Organization Guides" } end MotherDuck -->|"Get Guide(s)"|Agent Agent -->|Save Guide|MotherDuck Agent --> Result["Response or action"]:::green Prompt{{"Your prompt"}}:::green --> Agent["AI agent"]:::yellow ``` ## Prerequisites - A MotherDuck account with the [MCP server](/key-tasks/ai-and-motherduck/mcp-setup/) connected to an AI client like Claude, Cursor, or Claude Code - Permission to share org-wide Guides (for publishing Guides to your whole organization) ## Organize Guides with topics Topics are an effective way for agents to discover Guides without wasting tokens. Instead of loading every Guide up front it calls `list_guides(topic)`, the agent sees the topic tree with Guide counts and drills into the topics that look relevant to the task. This is called progressive disclosure and works best when you: - **Pick descriptive topic names.** The agent decides whether to open `revenue-billing` based on the name alone, so `revenue-billing` beats `misc` or `team-docs`. - **Keep the structure easy to traverse.** A handful of well-named top-level topics with one or two levels below is easier to navigate than a deep or fragmented tree. Topics carry no uniqueness — any number of Guides can share one. - **Reserve the root for truly general Guides.** Guides without a topic are listed individually in every `get_query_guide` overview: easiest for the agent to find, but they occupy space in every session. Leave the topic empty only for Guides so general they don't belong to any specific domain, like a company description, unique attributes to the field the company operates in, a data platform overview or org-wide SQL conventions. | Use | Avoid | Why | |-----|-------|-----| | `revenue-billing` | `misc` | Names the business domain the Guide covers | | `revenue-billing/forecasting` | `revenue-billing/quarterly/q3/forecasting` | Keeps the hierarchy shallow and easy to traverse | | No topic for a data platform overview | No topic for an MRR definition | Reserves the root for guidance that applies across domains | Each Guide has a unique identifier and carries an optional **topic**: a grouping label like `revenue-billing`. Topics can be nested with slashes, forming a hierarchy similar to folders in a filesystem: a Guide with topic `revenue-billing/forecasting` lives inside `revenue-billing`, and opening `revenue-billing` also reveals its nested topics. Personal and org-shared Guides live in the same topic tree, the reserved `dives` and `flights` topics hold Dive and Flight conventions, and [references](#attach-references-to-a-guide) link individual Guides to the catalog objects they document: Diagram summary: the guide catalog is one topic tree shared by personal and org guides. ```text "Data platform overview" [no topic] [organization] revenue-billing/ (topic) "MRR and ARR Definitions" [organization] -> references billing.main.subscriptions "My revenue query snippets" [user] forecasting/ (nested topic) "Forecast model inputs" [organization] -> references Quarterly forecast Dive dives/ (reserved: Dive conventions) "My Dive style" [user] flights/ (reserved: Flight conventions) ``` Personal (`user`) and org-shared (`organization`) guides sit in one topic tree. Topics group and nest like folders but carry no identity — every guide is addressed by its UUID. References link a guide to the catalog objects, Dives, Flights, or guides it documents. An agent can ask for an overview of all topics by calling [`get_query_guide`](/sql-reference/mcp/guides/get-query-guide.md). This lists every topic with its Guide count followed by the Guides stored at the root with their title, description, access level, and unique ID: ```text - data-quality/ (1 guide) - revenue-billing/ (2 guides) - revenue-billing/forecasting/ (1 guide) - "Data platform overview" — what lives where in our warehouse (organization, uuid: a1b2c3d4-...) ``` ::::note The `dives` and `flights` topics are left out of this overview; they appear in [their own entry points](#the-reserved-dives-and-flights-topics). :::: ### Using topics Topics are like an index in the back of the book. These clues direct an agent to the right place to learn about your specific concepts, and it finds every Guide filed there. Topics let the agent traverse the knowledge graph in a well-worn pattern and reliably retrieve the right information. Topics should be concise and fit together in a coherent way. One way to structure them is shown below: ```text (no topic) One orientation Guide: the definitions an agent needs first, per-schema notes, the join graph, and pointers to everything below definitions/ A glossary of terms that maps common language to your data / How bundles of metrics are computed, one topic per area: revenue-billing/, sales-funnel/, product-usage/ // Information related to a specific database, schema, and so on ``` - **The main Guide sits at the root** without a topic, so it is included in every `get_query_guide` overview. It should represent a map rather than a manual: things that are always needed for your agent to successfully return answers, and pointers to other Guides and notes on when to use them. - **Mirror a tree you already navigate by.** dbt project schemas make a good topic tree (`dbt//`) because you already use them, which means the agent gets to share your reasoning "for free". ## Governance for Guides Guides can have different levels of visibility: user or organization. Visibility is a per-Guide property, independent of topic: | Access | Who can see it | |--------|----------------| | `user` (default) | Private to the Guide's owner | | `organization` | Everyone in your MotherDuck organization (setting this is only available for organization admins) | There is no separate personal namespace: every listing and overview shows all Guides visible to you — your private Guides and org-shared ones overlaid in the same topic structure. A private Guide with topic `revenue-billing` appears alongside the org's Guides on that topic; each entry's `access` level tells you (and the agent) which is which. ### The reserved dives and flights topics The `dives` and `flights` topics extend the built-in Dive and Flight instructions. When an agent calls `get_dive_guide` before building a [Dive](/key-tasks/dives/), or `get_flight_guide` before authoring a [Flight](/concepts/flights), the returned instructions end with an overview of your Guides under that topic — so your conventions ride along with the product documentation the agent reads anyway. Use them for: - **`dives`** — personal or org-wide Dive styles: themes, color palettes, number formatting, layout preferences. - **`flights`** — personal or org-wide Flight conventions and recipes: scheduling standards, naming rules, proven ingestion patterns worth reusing. To have your assistant remember a personal style across sessions: ```text Create a Dive Guide that says that I prefer dark-themed Dives with compact number formatting ``` The agent creates a private Guide with topic `dives` and access `user`. Org-wide conventions use access `organization` instead. Because these topics are reserved, `get_query_guide` leaves them out of its overview — they only appear in their own entry points. ## Create your first Guide The recommended way to create Guides is through an AI agent. In Claude, Cursor, or another MCP-connected client, describe the Guide you want: ```text Create an org-wide Guide with topic "revenue-billing" that explains: - MRR is calculated from the subscriptions table using status = 'active' and trial_end IS NULL - ARR is MRR × 12 - The billing schema is in the billing database, main schema - Never join subscriptions to invoices for revenue — use subscriptions directly ``` The agent uses the [`create_guide`](/sql-reference/mcp/guides/create-guide) MCP tool. You can also create Guides directly from SQL: #### Creating a Guide Database: `my_db` ```sql SELECT id, topic, current_version FROM MD_CREATE_GUIDE( topic = 'revenue-billing', title = 'MRR and ARR Definitions', description = 'How monthly and annual recurring revenue are calculated', content = ' # MRR and ARR definitions MRR is the sum of all active subscription amounts normalized to a monthly value. Key rules: - Use the subscriptions table, not invoices - Filter to status = active - Exclude trial subscriptions (trial_end IS NULL) ', access = 'user' ); ``` The returned `id` is the Guide's permanent identifier — all later reads and updates use it. A one-line `description` pays off twice: it shows up in topic overviews (helping the agent decide what to read) and it's matched by catalog search. :::tip Bootstrapping a whole organization rather than one Guide? [Bootstrap Guides from the context you already have](/key-tasks/guides/bootstrap-guides-with-an-ai-agent) is a prompt that walks an agent through harvesting definitions out of your Slack threads, dbt project, and query history, then validating them with you before they're saved. ::: ## Browse and read Guides To see what Guides exist, ask your AI agent: ```text What Guides does my organization have? ``` The agent calls [`list_guides`](/sql-reference/mcp/guides/list-guides), which lists the catalog level by level: Guides at the current level plus nested topics with their Guide counts. You can also run the SQL function directly: ```sql SELECT id, topic, title, description, access FROM MD_LIST_GUIDES(); ``` Filter to a topic subtree — `topic = 'core'` matches `core` and `core/metrics`, but not `core-metrics`: ```sql SELECT id, title FROM MD_LIST_GUIDES(topic = 'revenue-billing'); ``` Read a Guide in full by ID: ```sql SELECT title, content FROM MD_GET_GUIDE(id = 'a1b2c3d4-e5f6-7890-abcd-ef1234567890'); ``` Every update creates a version snapshot. Browse the history with `MD_LIST_GUIDE_VERSIONS(id = ...)` and read an older version with `MD_GET_GUIDE(id = ..., version = 2)`. ## Update a Guide For large changes, replace the full content: ```text Update the MRR Guide to add a section on expansion MRR ``` The agent calls [`update_guide`](/sql-reference/mcp/guides/update-guide), or you can run it directly: ```sql SELECT current_version FROM MD_UPDATE_GUIDE( id = 'a1b2c3d4-e5f6-7890-abcd-ef1234567890', content = '...(full updated markdown)...', change_comment = 'Add expansion MRR section' ); ``` For targeted edits (fixing a table name, correcting a value), the agent uses [`edit_guide_content`](/sql-reference/mcp/guides/edit-guide-content), which applies find-and-replace edits without resending the whole document: ```text Rename billing.main.orders to billing.main.customer_orders in the MRR Guide ``` To retitle a Guide or move it to a different topic without touching the content, use [`update_guide_metadata`](/sql-reference/mcp/guides/update-guide-metadata): ```sql SELECT topic, title FROM MD_UPDATE_GUIDE_METADATA( id = 'a1b2c3d4-e5f6-7890-abcd-ef1234567890', topic = 'customer-orders', title = 'Customer Order Filters' ); ``` ## Attach references to a Guide References are a powerful way to make sure the agent finds the right object. It links a Guide to the specific catalog objects, Dives, Flights, or other Guides it documents. They power the automatic surfacing: when the agent calls `list_tables` on a database, the Guides referencing objects in that database come back with the result (including databases attached as shares). References can target a whole table or narrow down to a single column: ```sql SELECT current_version FROM MD_UPDATE_GUIDE( id = 'a1b2c3d4-e5f6-7890-abcd-ef1234567890', "references" = [ { 'type': 'catalog', 'url': 'md:billing', 'schema': 'main', 'table': 'subscriptions', 'description': 'Primary source for subscription revenue data' }, { 'type': 'catalog', 'url': 'md:billing', 'schema': 'main', 'table': 'subscriptions', 'column': 'amount', 'description': 'Monthly subscription amount in cents' } ] ); ``` For a database attached as a share, use the share URL instead of the database name — [`list_databases`](/sql-reference/mcp/core/list-databases) or [`MD_ATTACHED_DATABASES`](/sql-reference/motherduck-sql-reference/md-attached-databases) shows the URL for each attached database: ```sql SELECT current_version FROM MD_UPDATE_GUIDE( id = 'b2c3d4e5-f6a7-8901-bcde-f12345678901', "references" = [ { 'type': 'catalog', 'url': 'md:_share/sample_data/23b0d623-1361-421d-ae77-62d701d471e6', 'schema': 'hn', 'table': 'hacker_news', 'description': 'Hacker News sample data shared by MotherDuck' } ] ); ``` Then find relevant Guides before writing a query: ```sql SELECT id, title FROM MD_LIST_GUIDES( reference = { 'type': 'catalog', 'url': 'md:billing', 'schema': 'main', 'table': 'subscriptions' } ); ``` ## How agents interact with Guides Guides are directly integrated into the MotherDuck MCP server. The MCP already exposes tools to search, query, and create Dives and Flights. For each of these tools your agent gets a nudge to check for any relevant Guides. | Tool used by the agent | Guide surface | |----------------------|---------------| | [`query`](/sql-reference/mcp/core/query) | The tool description nudges the agent to call `get_query_guide` before writing SQL to answer a data question | | [`get_query_guide`](/sql-reference/mcp/guides/get-query-guide) | Navigation instructions plus an overview of all personal and organizational Guides: every topic with its Guide count, and the root-level Guides in full | | [`search_catalog`](/sql-reference/mcp/core/search-catalog) | Guides whose topic, title, or description match the search query appear as `relatedGuides` next to the catalog hits | | [`list_tables`](/sql-reference/mcp/core/list-tables) | Guides that [reference](#attach-references-to-a-guide) any object in the listed database are appended to the result | | [`get_dive_guide`](/sql-reference/mcp/dives/get-dive-guide) | The Dive instructions end with an overview of the Guides under the reserved `dives` topic | | [`get_flight_guide`](/sql-reference/mcp/flights/get-flight-guide) | The Flight instructions end with an overview of the Guides under the reserved `flights` topic | So even when an agent skips the entry point and jumps straight into catalog search or table exploration, it still stumbles over the Guides that matter for the task at hand. From any of these surfaces, the agent reads a Guide in full with [`get_guide`](/sql-reference/mcp/guides/get-guide) and browses further with [`list_guides`](/sql-reference/mcp/guides/list-guides). ## Tips for effective Guides - **One subject area per topic.** Group related Guides under a shared topic — `revenue-billing`, `user-events`, `data-quality` — so the agent can open the right topic from the name alone. - **Write descriptions.** The title and description are all the agent sees before deciding whether a Guide is worth reading, and they're what catalog search matches against. - **Lead with rules, not explanations.** Write `Use subscriptions, not invoices` rather than a paragraph explaining the data model history. Agents read Guides under context-window pressure. - **Include working SQL patterns.** Copy-paste-ready SQL is more useful than prose descriptions of what to query. - **Name pitfalls explicitly.** `Never join X to Y` or `Exclude rows where Z` prevents systematic errors. - **Use references.** Most importantly, reference the database or share a Guide is about — any catalog reference into a database makes the Guide surface in `list_tables` when the agent explores it. Narrow references down to tables or columns when the Guide covers specific objects. - **Version comments tell the story.** Use `change_comment` to explain why a Guide changed, not just what changed. For example: `Switch from invoices to subscriptions table after data model migration`. ## Manage Guide visibility By default, Guides are private (`access = 'user'`). With admin permission, you can make a Guide visible to the whole organization: ```sql SELECT access FROM MD_SET_GUIDE_ACCESS( id = 'a1b2c3d4-e5f6-7890-abcd-ef1234567890', access = 'organization' ); ``` ## Delete a Guide Deleting a Guide removes it from the active catalog but preserves its version history internally. Only the Guide's owner can delete it: ```sql SELECT success FROM MD_DELETE_GUIDE(id = 'a1b2c3d4-e5f6-7890-abcd-ef1234567890'); ``` References from other Guides to a deleted Guide are left dangling — clean them up in the referencing Guides if needed. ## Related resources - [Bootstrap Guides from the context you already have](/key-tasks/guides/bootstrap-guides-with-an-ai-agent) — A prompt that harvests Guides from Slack, dbt, and query history - [MCP tool reference — Guides](/sql-reference/mcp/guides/get-query-guide) — Full reference for each Guide MCP tool - [SQL function reference — Guides](/sql-reference/motherduck-sql-reference/guides/) — Full reference for each `MD_*_GUIDE` SQL function - [MCP workflows](/key-tasks/ai-and-motherduck/mcp-workflows) — Tips for working with the MotherDuck MCP server --- ## Docs feedback MotherDuck accepts optional user-submitted feedback about this page at `GET https://motherduck.com/docs/api/feedback/agent`. 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