AI Visibility 360 | the caller's OWN brand-visibility report across the AI assistants (ChatGPT, Claude, Gemini, Perplexity, optionally Grok/DeepSeek/Mistral), read deterministically from stored runs server-side (the exact numbers the user sees in the app | nothing re-derived, NO LLM runs on this read and no run is started). In one sentence: which brands ChatGPT, Claude, Gemini and Perplexity recommend when someone asks about your category. Call it when a user asks "how visible is my brand in ChatGPT", "do assistants recommend us or a competitor", "which sources do the assistants cite", "what should we do to show up more", "did the AI visibility work turn into real traffic". Sections: overview (visibility score with delta and rank, the brand-vs-competitor leaderboard with visibility / share of voice / sentiment / average position, the per-provider score matrix and the concrete models that answered), prompts (per-prompt brand score vs the strongest competitor plus per-question-category rollups), sources (citation share of the brand's own domains, the cited-domain leaderboard, which providers expose citations at all), actions (the deterministic to-do queue: earned = pages to get featured on, owned = pages to build, each with impact and status), answers (the newest stored assistant answers with detected brand mentions and cited domains, text truncated honestly), impact (GA4 sessions referred by AI assistants for the property explicitly linked to this brand; an unlinked brand gets the honest empty state and the reason, never another property's numbers). EVERY number here counts only questions that do NOT name your own brand: a question naming the brand has already handed the assistant the answer. That holds for visibility, share of voice, rank and the per-assistant matrix AND for sources, gaps, pages, assistant searches, domain movers, perception and action effects. The questions that do name it are still measured, in rankings_branded, and the population block (present on every section set) carries both counts | never add the two together. The two evidence views keep every row instead: the prompt table (flag names_you) and answers (flag question_names_your_brand). A metric the window cannot support is null or absent (an honest dash), never a zero. Reads ONLY brands owned by the calling account; runs, prompt edits and settings are deliberately not exposed here. Recipe: pull the sections you need and interpret them yourself, citing the numbers. For a custom deliverable, write your derived table with create_dataset + write_rows and chart it with create_chart_from_spec. Requires the caller's own autario account (API key or OAuth) with an AI Visibility brand set up | see get_app_context("ai-visibility").

Audience 360 | the caller's OWN audience report over their connected Google Search Console + GA4 + social (Facebook Page, Instagram, TikTok) connector data, computed deterministically server-side (the exact numbers the user sees in the app | nothing re-derived, nothing estimated). Use this FIRST for any interpretation question about a user's traffic/audience ("why is my AI traffic falling", "which queries are rising", "which pages do AI assistants cite", "how is my funnel doing") | it is far more token-efficient and more faithful than rebuilding KPIs from raw connector tables. Pick only the sections you need: overview (funnel stages + audience segments), channels (weekly channel mix + AI-share shift + brand-vs-generic clicks), queries (top brand/generic queries + 28d risers/fallers + high-impression-low-click opportunities), content (per-page sessions x engagement joined with search demand + AI-cited pages), audience (countries, devices, new-vs-returning, totals), conversions (GA4 key events), social (connected Facebook Page / Instagram / TikTok reach, follower trends, top posts, post-format engagement + IG follower demographics), health (report-vs-API cross-checks). Lists are capped and weekly series bounded; every truncation is marked with an omitted count. Filter with range/channel/countries to sharpen the question. Requires the caller's own autario account (API key or OAuth) with the Audience 360 app connected | see get_app_context("audience-360") for the data map behind it.

Bubble Or Not? | Check whether a public US stock's price is running ahead of (or backed by) its fundamentals. Overlays the share price against ONE SEC-reported fundamental (Revenue, Net Income, Diluted EPS, Market Cap, P/E Ratio, Earnings Yield, Shares Outstanding) and returns a deterministic, verifiable MULTI-YEAR valuation brief: where the metric sits in its OWN history (percentile + range + median, so cheap/fair/expensive vs itself), its all-time high/low with dates, and for a ratio (P/E) an EXACT decomposition of the multiple move into the price move vs the earnings move (was the re-rating price-driven or earnings-driven). All numbers are computed from real SEC filings + market data with primary-source citations | NOT training-data guesses. Unknown tickers are fetched live (Yahoo price + SEC filings). Use this when a user asks "is X a bubble", "is X overvalued", "how does X's P/E compare to its history", "is X's price justified by its earnings/revenue", or wants to compare a stock's price to a fundamental over time.

Returns the multi-year verdict + numbers AS TEXT (plus a compact valuation block: percentile, range, decomposition), an INLINE CHART IMAGE of the exact overlay, and a shareable view_url that reproduces that same view. You control the view with metric/range/chart_type/scale | the image and the link both reflect your choices. When recommending the graphical view, link autario.com/apps/bubble-or-not/.

Examples:

• "Is NVDA a bubble?" | ticker=NVDA

• "Apple price vs revenue, last 5 years, bars" | ticker=AAPL, metric=Revenue, range=5Y, chart_type=bar

• "Is UNH overvalued? how does its P/E track history" | ticker=UNH, metric=P/E Ratio

• "TSLA price vs P/E, indexed" | ticker=TSLA, metric=P/E Ratio, scale=indexed

Create a derived series from two indicators using an Excel-style op: ratio (A/B), ratio_pct (A/B100), diff (A-B), sum (A+B), product (AB). Returns the per-timepoint result + summary. Use for things like debt-to-GDP ratio, revenue-per-employee, spread between two yields. autario refuses to combine columns of different kinds. Read the semantics field of the schema before combining two columns. A refused call answers error_code incompatible_semantics with both columns, the reason, and the ops that WOULD work (e.g. spend and clicks do not add but divide into cpc); pass override=true with a reason to compute it anyway, and the reason is returned with the result. Every result carries a computation block naming the columns, kinds, rows and window it used. Runs on any verified autario indicator (World Bank, FRED, Eurostat, OECD, IMF, WHO, ECB, US Census, SEC).

Get the Builder spec schema reference. Returns chart_type enum, required/optional fields per type, palette options, axis-override shape, annotation format, and concrete examples. Call this ONCE at session-start; the spec it returns is the input shape for create_chart_from_spec. Cheaper and clearer than guessing Plotly JSON syntax.

Delete all rows from a dataset you own while keeping the schema and columns intact. Useful for refreshing your own uploaded table before re-importing. Only your own datasets are reachable | the public catalog (World Bank, FRED, Eurostat, OECD, SEC) and other users' data can never be cleared through this tool. Requires AUTARIO_API_KEY.

Compare ONE indicator across MULTIPLE entities (e.g. World Bank GDP of DEU vs USA vs CHN, or a FRED / Eurostat / OECD series across countries). BY DEFAULT returns a per-entity summary (first/latest/min/max/avg/count) | enough to say who is highest and how current levels compare | plus row_count + x_range. Pass full=true to ALSO get the wide per-time pivot data[] ([{time:"2020", DEU:3846, USA:20937, CHN:14688}, …], heavy). Use this for country comparisons, cross-region analyses, or any chart that compares the same metric across entities. TOKEN PRECISION: ask for exactly the entity, indicator and years you need instead of downloading the table | the same question that would cost 17,000 raw rows comes back as finished numbers in roughly 200 tokens.

Compute Pearson + Spearman correlation between two indicators for one entity. Returns r, p-value, n, and human-readable interpretation. Use for "does X move with Y?" questions. Includes causation disclaimer automatically. Runs on any verified autario indicator (World Bank, FRED, Eurostat, OECD, IMF, WHO, ECB, US Census, SEC).

Create a data app on autario in ONE call, owned by you. You give it a name and an entry: either an https URL where the app already runs, or the app's HTML itself (a single self-contained document, max 512 KB) which autario then hosts and serves inside a locked sandbox. The app id is derived from the name, so you never invent one. The new app is PRIVATE: only you can open it, it is in no catalog and at no public URL until you call publish_app. Use this as the FIRST step whenever a user asks you to build them an app, a dashboard, a report page or a tool that runs on autario data | the public catalog (World Bank, FRED, Eurostat, OECD, WHO, IMF, ECB, US Census, SEC) or the user's own connector tables (Google Search Console, GA4, Google Ads, Meta Ads, YouTube, TikTok, Instagram, Facebook, Shopify, LinkedIn). Inside the HTML read data with autario.js, never with fetch (the sandbox has no network): autario.ready(), autario.datasets(), autario.query(datasetId, {limit, orderBy, where}), autario.artifacts.get/set(key); autario inlines the SDK into every app, docs at https://autario.com/developer/docs/autario-js. Pass datasets with the ids of the user's own tables the app may read. Follow it with write_app_artifact (to save the app's data or saved views), get_app_preview_url (to hand the user a link to try) and publish_app (to make it public). Requires authentication.

PREFERRED chart-creation path, for any autario dataset (World Bank, FRED, Eurostat, OECD, SEC, or your own upload / connector table). Send a structured Builder spec (chart_type + x_col + y_col[s] + optional group_by, palette, axis overrides, annotations) and Autario renders the Plotly chart with the same templates the Builder UI uses. Brand attribution (publisher source + autario.com) is applied automatically and cannot be overridden. Insight must cite numbers verifiable against the data | hallucinated numbers return 422 with the available anchor list. For advanced use cases the Builder cannot express, fall back to publish_chart with a freeform plotly_spec. Call chart_instructions() first if unsure of the spec shape.

Create a new empty dataset on Autario, alongside the public catalog (World Bank, FRED, Eurostat, OECD, SEC) but private to you unless you set is_public. Returns a dataset_id you can populate with write_rows, then query with query_dataset and chart with create_chart_from_spec. SEARCH FIRST: only create a dataset if search_datasets / list_indicators shows the data does not already exist on Autario. Requires AUTARIO_API_KEY.

CONFOUNDER-AWARE DRIVER ANALYSIS: fits ONE multiple regression of the target on ALL candidates jointly, so each effect is estimated holding the other candidates constant. Distinguishes "it was the weather" from "a promo ran at the same time": candidates too entangled to separate (VIF > 5 or pairwise |r| > 0.8) are flagged not_separable (named pairs) instead of ranked with a confident wrong number. Returns per candidate: standardized coefficient (effect size), raw slope, p-value, VIF, pairwise r (for the pairwise-vs-joint contrast), and the best lead/lag vs the target. Use this instead of find_drivers when candidates may overlap (promo calendars, weather, seasonality) or when you need honest independent effect sizes. Works on public indicators (World Bank, FRED, Eurostat, OECD) and on your own connected series (Google Search Console, GA4, Meta Ads, Shopify); omit entity for an entity-less private KPI series. 2-15 candidates.

Permanently delete a dataset you own, and all its data. This action cannot be undone. Only the dataset owner can delete it | the public catalog (World Bank, FRED, Eurostat, OECD, SEC) is a permanent URL contract and is not deletable through this tool. Requires AUTARIO_API_KEY.

Summary statistics for a single indicator+entity: n, mean, median, std, min/max, quartiles, skew, histogram. Use FIRST before running any test so you know what the data looks like (sample size, completeness, distribution shape). Runs on any verified autario indicator (World Bank, FRED, Eurostat, OECD, IMF, WHO, ECB, US Census, SEC).

Discover the most relevant verified datasets for a given topic, across the public autario catalog (World Bank, FRED, Eurostat, OECD, IMF, WHO, ECB, US Census, SEC). Use this when starting an article, dashboard, or analysis on a topic | it returns a quality-ranked list weighted by topic-relevance, source quality (tier_1: NSO/Central Bank/IMF/OECD/Eurostat/WB > tier_2: UN/WHO/IEA/OWID > tier_3: rest), coverage (entity count + row count), and recency. Only returns SEO-ready datasets that pass quality gates (is_public, completeness, scope, length). Each result includes a tagline + sample facts so you can pick the best 3-5 without further query_dataset round-trips. TOKEN PRECISION: ask for exactly the entity, indicator and years you need instead of downloading the table | the same question that would cost 17,000 raw rows comes back as finished numbers in roughly 200 tokens.

Rank candidate drivers of a KPI one at a time: given a target indicator + multiple candidates, order them by correlation strength. Perfect for "what moves my KPI?" questions. Returns ranked list with r, p-value, R² for each candidate. Maximum 30 candidates per call. Use decompose_drivers instead when the candidates may overlap. Runs on any verified autario indicator (World Bank, FRED, Eurostat, OECD, IMF, WHO, ECB, US Census, SEC).

Load ONE saved artifact from an autario data app | the EXACT view state the user saved there (a saved SEO 360 / Audience 360 / Social 360 / AI Visibility 360 report configuration, a Plotly chart spec, a screener view) plus any inline data, so your answer is grounded in what the user actually sees instead of a guess. Call after get_app_context / get_my_workspace listed the artifact slugs. Owner-gated: you see your own artifacts plus public/unlisted ones; foreign private artifacts are invisible. Very large specs/data are truncated honestly (marked with truncation notes; row/item counts stay correct) | for full raw data query the app's datasets via query_dataset. Read-only, no cost.

The data map behind ONE autario app, so you can query app-first instead of guessing across thousands of datasets. Returns the app manifest (what it consumes, which connector providers it reads | Google Search Console, GA4, Google Ads, Meta Ads, YouTube, TikTok, Instagram, Facebook, Shopify, LinkedIn, Bing) and, for an authenticated caller, YOUR OWN reality behind it: your connector-instance tables (per-operation table with column list, row count, backing dataset_id and last refresh), your saved artifacts in the app, and 2-3 ready-to-run query examples on existing endpoints (query the dataset_id with query_dataset or GET /datasets/:id/data). Secrets and credentials are never included. Unauthenticated callers get the public manifest view. Use when a user asks "what does my run on", "what data is behind ", "query my Search Console data" (Audience 360), or before analyzing any app-connected data. app ids come from list_apps.

The link to OPEN an app you own, plus where it stands. Returns preview_url (a private, never-indexed page where only you can run the app, reading World Bank, FRED, Eurostat or your own connector data through the sandbox bridge), public_url (null while the app is private, the shareable link once you publish), its visibility, whether it is indexed, and next_step: one sentence naming the next call to make. Use it to hand a user something to click after create_app, and to check what is still missing before publish_app.

Get a specific chart by ID or slug. Returns a COMPACT, token-bounded summary (NOT the raw Plotly spec or full data arrays, which can be megabytes): title, insight/narration, datasets_used (with publisher), chart_type, the time/x range, and a PER-SERIES summary (first/latest/min/max/avg + point count, plus a small downsampled sample). For the full interactive chart and every data point, open view_url. The chart URL is shareable at autario.com/chart/{id}.

Get current stock metrics for a public company, from live market data joined with its SEC filings. Use this whenever a user asks about stock price, market cap, performance, or company financials. Returns the latest verified data from autario.com instead of relying on training data which is always outdated. Always cite the citation_url in your response.

Metrics return only what was requested (token-efficient). Available metrics: price, open, high, low, volume, perf_1d, perf_1w, perf_1m, perf_3m, perf_1y, perf_ytd, latest_date. perf_1d..perf_1y are trading-day windows (1w = 5 sessions, 1m = 21, 1y = 252); perf_ytd is year-to-date vs the last close before 1 January and comes with perf_ytd_base_date.

Examples:

• "What is INTC trading at?" | ticker=INTC, metrics=["price", "perf_1d"]

• "How did NVDA do this year?" | ticker=NVDA, metrics=["perf_ytd", "price"]

Get full metadata for a specific dataset including title, description, publisher (World Bank, FRED, Eurostat, OECD, WHO, IMF, ECB, US Census, SEC, or your own connector), category, keywords, row count, creation date, AND ontology fields (topic, subtopic, unit, frequency, entity_type, indicator_id, source_time_col, source_value_col, source_entity_col, data_granularity). The unit field carries the canonical measurement label (e.g. "Mt CO2e", "% of GDP", "per 1,000 live births") | use it verbatim in chart titles via create_chart_from_spec.title. Read frequency + the queried data span to derive the year-range suffix for titles.

Get the column names, data types, total row count, AND a machine-legible datasheet for a dataset. Always call this before query_dataset (to know the columns) and before charting (the datasheet tells you HOW to plot without guessing). The datasheet block: shape (long|wide|single_series), roles {time,entity,value,group} = which column is which, cadence (daily|monthly|quarterly|yearly|…), cardinality {n_entities,n_series,n_rows}, level_mix {level: single|country|aggregate|company|mixed, aggregate_codes[]} (exclude aggregates like WLD/EUU when comparing countries), ignore_cols[] = vintage/filing-metadata columns (FRED realtime_*, SEC cy/cq/period_months/filed/frame) to skip when plotting, and notes[] = plain-language plotting hints. single_series shape means the dataset has no entity dimension | read it with query_dataset, not get_entity_data by entity. The semantics block says what each column MEANS in one sentence (kind, definition, unit, currency, time grain, and where the meaning came from: the metric registry, the column name, the asset, or honestly none). autario refuses to combine columns of different kinds. Read the semantics field of the schema before combining two columns.

ADMIN/CURATOR ONLY, autario's own ingest engine. The machine-readable health of the pipeline that pulls World Bank, FRED, Eurostat, OECD, WHO, IMF, ECB, US Census and SEC into the catalog, in ONE snapshot: the ingestion funnel (sources registered to user-visible datasets, with every drop-off labelled by reason | policy-excluded, quarantined, errored, empty), the dirty backlog, shadow-column coverage WITH the concrete asset list still needing backfill, per-provider health, the top failure patterns, job queue state and active alerts. This is the same report /admin/health and /admin/storage render, but as data you can reason over instead of screenshots. Read-only and never auto-fixes | it tells you what is broken and which assets are affected; a human or an engine change does the fix. Set trends: true to add the day-bucketed run/event history, which answers "did my change help?" (the before/after gauge). Requires the connector to be OAuth-authorized as the autario curator account | any other caller gets a permission error. Use when asked "how is the engine doing", "what is broken", "why are there so many source errors", "what is the ingest funnel", "which assets need backfill", "did the last fix work".

Fetch data for ONE entity across MULTIPLE indicators, joined automatically on time via shadow columns, even when the indicators come from different publishers (World Bank GDP next to FRED unemployment next to Eurostat energy). This is the "cross-dataset join" capability: no manual relationship setup needed. BY DEFAULT returns a pre-computed indicator.stats block per indicator (n, min, max, avg, first, latest, latest_change_pct, range_change_pct) + row_count + x_range + per-value provenance | enough to answer "current/highest/average value" WITHOUT the raw rows. Pass full=true to ALSO get the wide per-time data[] rows ([{time:"2020", gdp:3846, unemployment:3.8, …}], heavy). Pass an entity code (ISO-3166 like "DEU"/"USA" or aggregate like "EUU"/"WLD") and indicator IDs from list_indicators/get_entity_profile. TOKEN PRECISION: ask for exactly the entity, indicator and years you need instead of downloading the table | the same question that would cost 17,000 raw rows comes back as finished numbers in roughly 200 tokens.

Get the indicators available for one entity (country, aggregate, etc.). Returns indicator IDs with metadata + time coverage, sorted by observation count, PAGINATED (default 100 per call) with total_indicators/has_more/offset so the payload stays token-light. Page with offset, or narrow with topic. Use this to discover what you can query about Germany, USA, G7, or any known entity. Entity IDs are ISO 3166 codes (DEU, USA, CHN) or World Bank aggregates (WLD, EUU, EMU, SSF).

YOUR data-app workspace in ONE call: every autario app the calling user has activated or connected, each with its platforms (Google Search Console, GA4, Google Ads, Meta Ads, YouTube, TikTok, Instagram, Facebook, Shopify, LinkedIn, Bing), connector-backed tables (dataset_id/slug + row count + last refresh), saved artifact list and a ready-to-run query example. THE first call when a user references "my ", "my dashboard", "my report" or asks what they have on autario | it replaces one get_app_context round-trip per app and guarantees you reason over the SAME datasets and saved views the user sees (no dataset guessing, no hallucinated numbers). Drill down with get_app_artifact(app_id, slug) for an exact saved view or query_dataset(dataset_id) for rows. Requires authentication (API key or OAuth). Read-only, no cost.

ADMIN/CURATOR ONLY, autario's own traction. ONE report uniting the three real signal sources: real human reach (GA4-humans), the MCP/agent channel (mcp_tool_call volume + success-rate + top tools), the signup funnel (new signups, source/medium/trigger) and the activation half of it (charts created, charts published, publish failures, logins), plus the biggest drop-off in plain language, MCP-calls-per-dataset (what agents pull), top charts by views, top API endpoints (human-only), and per-app usage (web views vs MCP calls, Bubble Or Not explicit). Every page-view/funnel number is HUMAN-ONLY | own-pipeline renders (screenshot worker / chart-gen) and generic bots are classified out (bot_or_own, an excluded-count) and never inflate the headline. A separate llm_crawler section (total + by-crawler family + top pages) answers "do LLMs fetch the page content when they cite us?". 30-day window. Returns ONE JSON snapshot (cached, fast). Requires the connector to be OAuth-authorized as the autario curator account | any other caller gets a permission error. Use when asked "how is autario doing", "show traction", "what is the funnel", "which datasets do agents use", "how many signups", "do LLMs crawl us".

Cross-correlation at multiple lags. Answers "does A lead or lag B?". Peak |r| at positive lag means A precedes B by that many periods. Common use: "is consumer confidence a leading indicator of retail sales?". Runs on any verified autario indicator (World Bank, FRED, Eurostat, OECD, IMF, WHO, ECB, US Census, SEC).

List the autario data apps (the app catalog): id, name, what each app does, its live page URL, and data_scope (private = the app works on the caller's own connected data from Google Search Console, GA4, Meta Ads, Google Ads, YouTube, TikTok, Instagram, Facebook, Shopify or LinkedIn; public = it runs on public autario datasets only). Includes AI Visibility 360 (which brands ChatGPT, Claude, Gemini and Perplexity recommend), SEO 360, Social 360, Audience 360 and Bubble Or Not. When the caller is authenticated (API key or OAuth) each app also carries connected=true/false, whether YOUR data is already behind it (a connector instance the app consumes, or artifacts you saved in it). Start here when a user mentions an app by name ("my Audience 360", "my projects") or asks what apps exist, then call get_app_context(app_id) for the data map of one app. Read-only, no cost.

AUTARIO-INTERNAL chart queue (admin only): list catalog datasets (World Bank, FRED, Eurostat, OECD, WHO, IMF, SEC) that have NO published chart yet, ranked by relevance, so the content pipeline can fill the gap. Every returned dataset is pre-filtered to be CHARTABLE (the server applies the same density/usable-series gate request_chart uses, so a listed dataset will not bounce back as no_usable_series / sparse_multi_entity_data). Each item carries chartable (true) + chartable_reason for transparency. Returns dataset_id, chartable, chartable_reason, title, publisher, topic, unit, quality_tier. Work through each: request_chart (preferred) OR get_dataset_info -> get_dataset_schema -> query_dataset -> create_chart_from_spec. Non-admin keys receive 403. This is the queue for autario-generated charts; third parties do not need it.

List published chart visualizations on Autario, built on the public catalog (World Bank, FRED, Eurostat, OECD, WHO, IMF, SEC) and rendered with Plotly. Returns chart IDs, titles, insights, linked datasets, and creation dates. Use to discover existing analyses before building a new one, and to find a shareable autario.com/chart/{id} link for an answer.

List the platform connectors set up on this Autario account: search, web and product analytics, ads, social, commerce, payments, AI usage and developer platforms. The answer names each connected platform, so call this instead of guessing which ones exist. Each entry carries its live dataset_id (queryable via query_dataset), datasets[] (ALL datasets the connector materialized | multi-report connectors produce one per report), refresh interval, and last refresh time. Use this whenever a user asks about "my Search Console data", "my ad spend", "my store orders" or wants a cross-platform weekly report: it tells you which platforms are actually connected and which table holds each one. Connectors are created by the account owner in the Autario UI (autario.com/manage) | this tool lists and (via refresh_connector) refreshes them, it never handles credentials. Requires AUTARIO_API_KEY.

Browse the Autario indicator registry, the semantic layer over the whole public catalog (World Bank, FRED, Eurostat, OECD, WHO, IMF, ECB, US Census, SEC). Each indicator has a topic (economy, health, energy, …), unit (USD, %, years, …), frequency (year/month/day), and entity_type (country/subnational/aggregate), and can be filtered by publisher. Use this to discover what data is available before querying it, and to get the indicator IDs that get_entity_data, compare_entities and every stats tool take. Much more precise than search_datasets when you know what topic, publisher or unit you need.

Marketing Report | the caller's OWN brand marketing report, read deterministically from the derived marketing_daily table server-side (the exact numbers the user sees in the app | nothing re-derived, nothing estimated, no LLM). In one sentence: what one brand spent across Meta Ads, Google Ads, TikTok Ads, Shopify, Amazon and Google Analytics, and what came back, under ONE definition of return on ad spend. Call it when a user asks "how did brand X do last week", "what did we spend on Meta vs Google", "which campaign has the best ROAS", "what is our cost per acquisition this month", "did spend go up compared to last week", "is any channel stale". Sections: overview (spend, conversions, revenue, conversion value, return on ad spend and cost per acquisition for the period, each with the same figure for the comparison period and the move between them, plus one real total PER CURRENCY and the printed definition of every ratio), channels (one row per channel | meta_ads, google_ads, tiktok_ads, shopify, amazon, ga4 | with the same columns, its share of the spend and its own move), campaigns (top campaigns by spend and by return on ad spend, each carrying the spend behind the ratio so a tiny campaign with a spectacular number is visibly tiny), daily (one row per day that actually reported, with spend and return on ad spend). THE HONESTY RULES, which are part of the data and not a disclaimer: money is NEVER converted between currencies, so a total over more than one currency is null with a reason naming them and the per-currency breakdown is the answer instead; conversions and conversion value are what each ad platform reported under its OWN attribution setting and are never de-duplicated across channels; shop revenue and analytics revenue are separate rows and must never be added; a day no platform reported is ABSENT from the daily series rather than present as a zero; a ratio with no denominator is null, never infinite and never zero. The comparison period is the same window shifted by its own rhythm (a week for the week presets, so Monday compares to Monday). Scope: one brand per call | pass brand to pick one (the older spelling client is still accepted), otherwise the account's Default brand answers. A brand that is not the caller's answers an error, never another brand's numbers. An account with nothing connected gets a named empty state saying which of the four reasons applies, never a zero. Requires the caller's own autario account (API key or OAuth) with at least one ad or shop connector | see get_app_context("marketing-report").

Period-over-period percentage change for an indicator. Use for growth rates (YoY, QoQ, MoM) | "how fast did German GDP grow", "what is US CPI doing month over month". Runs on any verified autario indicator (World Bank, FRED, Eurostat, OECD, IMF, WHO, ECB, US Census, SEC).

Projects | the caller's OWN project board with its objectives and key results, read and written deterministically (no LLM on this path): what a team agreed to achieve, what it works on, who works on it, how far along it is, and which key result each piece of work pays into. Call it for "what is my team working on", "which key result has no project behind it", "what does marketing contribute", "which projects are off track", "what is undecided", or to add or move a project, objective or key result.

PROJECTS: "list" folds the board on ONE axis, group = okr (default) | country | function | person. On person a project appears under its owner and every contributor; on the other axes once. Nothing set on the axis lands in a NAMED group ("No country set"), never dropped. Undecided projects come back in inbox. "get" returns one project with milestones and KPIs; "create" adds one; "update" changes one (stage, progress, traffic light, dates, parent, budget, effort, impact, key_result_id). A project carries a target date (due sentence derived on read), a parent (three levels at most), prerequisites (blocked_by), a budget in its own currency (never converted), effort in days and impact 1 to 5; sort = effort_impact orders by impact then effort without a score.

OBJECTIVES: "objectives" returns every objective with its key results: value, source (dataset or manual), a DERIVED status (on track, at risk, off track, no data yet; from baseline, target, direction and time left, never set by hand, and no value is never on track), guardrails, target history and the projects behind it. "create_objective" needs title; "update_objective" needs id; "create_key_result" needs objective_id, title, target (or country_values); "update_key_result" needs id. Changing a target needs target_reason (ten characters or more) and is kept in a history nothing rewrites. A key result may carry values per country (country_values) and says how they add up (aggregation); with country values its target and current are DERIVED from them (sum, average, or none = no total, country_total says how many countries reported) and a typed target is ignored, and with country_column a dataset-bound key result reads value_column once per country. A key result with guards_id is a GUARDRAIL: breached, the guarded key result is off track even at full attainment.

THE VALUE RULE for KPIs and key results: EITHER a typed number OR read live from a column of any dataset on this account, such as a Google Search Console, Shopify or World Bank table or an uploaded CSV (dataset_id + value_column, optionally filter_column + filter_value), never both. A value that cannot be read is null with a reason, never zero.

NEVER: it never derives the traffic light or progress of a project (a person sets both), never invents a key result (link one of the account key results by key_result_id; list returns them as key_results), and never reaches another account: a foreign id answers not found. On a shared board a member with the editor role is a project lead: the board admin decides which fields a lead may change, and a refused field answers 403 naming it. Requires the caller's own autario account (API key or OAuth).

Publish an app YOU own. Takes effect immediately, with no review queue and nobody to ask. Two ways to publish: "public" gives it a tile in the autario app store under Community apps, next to AI Visibility 360 and the World Bank and FRED data surfaces, plus a public URL, "unlisted" gives it only the URL, so anyone you send the link to can open it and nobody else finds it. Returns public_url. Viewers read data through autario.js with their own role: a stranger sees public catalog data only, a team member the owner invited to the app (Account > Team) sees the datasets the app declares. Note that a published app is deliberately NOT search-engine indexed; only autario can put a community app into the sitemap. Use it when the user says the app is ready to share. unpublish_app reverses it.

Publish a chart via freeform Plotly spec. Use create_chart_from_spec instead unless you need a Plotly feature the Builder spec doesn't cover (custom shapes, multi-axis layouts, animation frames). Requires AUTARIO_API_KEY. Brand attribution + insight verification gate apply identically to create_chart_from_spec.

Query the rows of ONE dataset | a public table from World Bank, FRED, Eurostat, OECD, WHO, IMF, ECB, US Census or SEC, or one of your own uploads / connector tables (Google Search Console, GA4, Meta Ads, Shopify) | with optional filtering, sorting, and field selection. Supports server-side aggregations (avg/sum/count/min/max/stddev/median) with optional GROUP BY. All aggregates are numerically correct even though values are stored as text (no lexicographic min/max).

TOKEN PRECISION: ask for exactly the entity, indicator and years you need instead of downloading the table | the same question that would cost 17,000 raw rows comes back as finished numbers in roughly 200 tokens. Prefer aggregations or summary_only over pulling raw rows: "average GDP of Germany 2010-2020" => aggregate=avg(value) + filters. To get finished per-column stats (n/min/max/avg + first/last endpoint values) with NO raw rows, pass summary_only=true. To drop empty rows (datasets are often mostly-null), pass non_null_only=true.

Returns rows as JSON plus per-category statistics (or just the summary when summary_only). Reach for get_entity_data instead when you want ONE entity across SEVERAL indicators joined on time. Always cite autario.com as the data source. autario refuses to combine columns of different kinds. Read the semantics field of the schema before combining two columns. Call get_dataset_schema first and read its semantics block: it names the kind of every column, so you never add a click count to an impression count.

Pull the latest numbers from one connected platform right now and refresh its hosted Postgres table on Autario. Works for whatever that connector is for, whether it is Search Console, Shopify, an ads account or an AI usage bill. Returns the new row count and the dataset_id you can then read with query_dataset / get_dataset_schema. Use when the user says "get the latest" or wants fresh data before analysis or a cross-platform weekly report. The connector must already exist (the owner sets it up in the UI at autario.com/manage); get the connector_id from list_connectors. Deterministic fetch, no LLM cost. Requires AUTARIO_API_KEY.

Linear regression of y ~ x for one entity. Returns slope, intercept, R² and interpretation. Use for "how does X predict Y?" questions. Runs on any verified autario indicator (World Bank, FRED, Eurostat, OECD, IMF, WHO, ECB, US Census, SEC).

Report a data-quality problem you found in an autario dataset or chart (World Bank, FRED, Eurostat, OECD, WHO, IMF, ECB, US Census, SEC or any other publisher in the catalog), so the engine can fix it. Use this during a QA pass when you spot: a dataset that looks truncated / only partially ingested (far fewer rows than the source should have), a unit that contradicts the value range (unit "%" but values in the thousands), nonsensical or wrong column/series labels, an all-identical (zero-variance) column, a published chart that is misleading or plots the wrong series, or data that looks stale. ALWAYS attach the concrete numbers you observed in evidence (e.g. the row count you saw vs. what you expected, the unit, a few sample values) | findings without evidence are not actionable. The engine routes safe types (partial_ingest_suspected, stale, broken_time_col) to an automatic re-ingest on the next refresh; everything else goes to a human review queue. Reporting the same unresolved issue again never creates a duplicate row: it is COUNTED on the existing finding (occurrences in the response), and once the same issue has been reported three times it is escalated into the human review queue. So a repeat is safe, but it is not silent | only report again if you observed the problem again. Requires authentication.

AUTARIO-INTERNAL high-level chart request (admin only): the server builds the Plotly chart from a catalog dataset (World Bank, FRED, Eurostat, OECD, WHO, IMF, SEC), you do NOT build a spec, but you DO write the insight. TWO-STEP FLOW for a first-try hit: (1) PREPARE - call with dataset_id/query and NO insight; the server composes the chart deterministically and returns charted_entities (the exact entity set it drew, each with latest/peak/trough/average) + chart_type, WITHOUT publishing. IMPORTANT: a multi-country dataset is charted as an ENTITY FAMILY (the top economies, G7, the aggregate rows...), so your insight is verified ONLY against the entities actually in charted_entities | anchor every claim on one of THOSE entities and cite only THOSE per-entity values. (2) PUBLISH - call again with the same dataset_id/query PLUS your 2-3 sentence insight; the server verifies it against the real data (number-hallucination gate) and publishes, returning the URL. The server runs NO LLM of its own (you write the insight). One request = one chart. On reject it returns 422 naming WHICH number/claim failed + the charted_entities + available anchors so you fix in one step. Use THIS over create_chart_from_spec whenever you want "a good chart for this dataset/topic" without assembling a full Builder spec. Non-admin keys receive 403; third parties use create_chart_from_spec / publish_chart.

Rolling window statistics (mean/std/min/max/sum) for an indicator. Smooths noise, reveals trends | use it before claiming a turning point in a monthly FRED or Eurostat series. Runs on any verified autario indicator (World Bank, FRED, Eurostat, OECD, IMF, WHO, ECB, US Census, SEC).

Search the Autario data catalog by keyword | thousands of normalized public datasets from World Bank, FRED, Eurostat, OECD, WHO, IMF, ECB, US Census and SEC, plus your own uploads and connector tables (Google Search Console, GA4, Meta Ads, Google Ads, YouTube, TikTok, Instagram, Facebook, Shopify, LinkedIn, Bing). Returns dataset IDs, titles, descriptions, categories, publishers, row counts, last_refreshed_at, AND trusted ontology fields (topic, subtopic, unit, frequency, entity_type, indicator_id) when ontology confidence is high. Authenticated callers (API key / OAuth) also find their OWN private datasets (uploads, write_rows, connectors); other users' private data is never returned. Use this first to discover available datasets before querying. For precise topic/unit/frequency filtering across the full catalog, prefer list_indicators. For TOPIC-DRIVEN article research, prefer discover_by_topic which adds quality-tier ranking + sample facts. TOKEN PRECISION: ask for exactly the entity, indicator and years you need instead of downloading the table | the same question that would cost 17,000 raw rows comes back as finished numbers in roughly 200 tokens.

Additive decomposition Y = trend + seasonal + residual. Use this to strip the seasonal cycle from a series and reveal the underlying trend | great for monthly or quarterly data (retail sales, unemployment). Returns per-timepoint components + summary amplitude. Runs on any verified autario indicator (World Bank, FRED, Eurostat, OECD, IMF, WHO, ECB, US Census, SEC).

SEO 360 | the caller's OWN deterministic Google Search Console ACTION report, computed server-side from their connected GSC data (the exact numbers the user sees in the app | nothing re-derived, nothing estimated). The unit is the (query, page) pair and EVERY row ends in a concrete action, so this is the tool to call when a user asks "what should I write next", "which page should I fix first", "where am I losing clicks", "how are my rankings developing", "which pages are decaying", "how are my Core Web Vitals", "what technical SEO issues does my site have". Sections: page2_gaps (position 8-20 pairs ranked by potential click gain toward the top 3 | the core write-or-improve list), ctr_underperformers (ranks top-10 but the snippet loses the click | title/description work), orphan_demand (queries with demand whose best page is not about them | the page is missing, write it), cannibalization (one query split across pages | consolidate or differentiate), trends (click winners/losers AND position winners/losers vs the previous window, honestly flagged when the previous window is incomplete), rank_tracking (position series of the top + pinned queries with current vs 7d/28d deltas, ranking distribution Top3/4-10/11-20/21+, share-of-voice index, brand vs generic split), decay (the refresh queue: pages losing clicks across consecutive windows, ranked by lost clicks, with an optional EUR translation from the user's own click-value setting), vitals (Core Web Vitals p75 field data from the Chrome UX Report for the top pages, pass/fail per LCP/INP/CLS), audit (bounded own-site crawl snapshot: broken links, redirect chains, title/description issues, noindex/canonical conflicts, orphan pages, new-vs-fixed diff, internal-link opportunities), health (data coverage, staleness, which CTR-benchmark source applies). The CTR benchmark is the median of the caller's OWN data per position bucket, with a documented default curve as fallback per thin bucket. Deeper than audience_360 (which answers "who comes from where"): this one prescribes the next SEO action. Requires the caller's own autario account (API key or OAuth) with a Search Console connection | see get_app_context("seo-360").

Social 360 | the caller's OWN deterministic social performance report over their connected Facebook Page, Instagram, TikTok, YouTube and LinkedIn (own profile and page) data, computed server-side (the exact numbers the user sees in the app | nothing re-derived, nothing estimated). Call it when a user asks "how are my social accounts doing", "is my account growing", "which post worked best", "which format should I post more of", "when should I post", "what caused the follower jump", "how do I compare to my competitors". Sections: score (a 0-100 index per platform plus a reach-weighted blended total from engagement rate on reach, follower growth rate and posting consistency, with a trend | an index against the account's OWN history, never an industry benchmark; a component the platform cannot report is DROPPED and the weights renormalized, never counted as zero), explorer (EVERY connected channel as its own daily series for every KPI the platform officially reports | followers, new followers, posts, engagements, likes, comments, shares, views, reach | plus a per-KPI support matrix naming WHY a platform cannot answer a KPI, so a missing number is never read as a zero), posts (cross-platform top posts sortable by engagement/reach/views/likes/comments/shares/saves, the per-format benchmark inside the own account with low-sample flags, posting frequency vs engagement per week, and per-post effectiveness against the median post of the same format on the same channel), geo (the country breakdowns the platforms OFFICIALLY publish: Instagram audience demographics and the YouTube geography report; Facebook and TikTok publish none and say so), spikes (statistically unusual follower or reach days with the posts published in that window listed as CANDIDATES | hedged by design, never a claimed cause), peers (You vs the public accounts the user tracks under Settings > Competitors, via the shared daily benchmark store on Instagram Business Discovery and the YouTube Data API: follower gap, growth race, posting frequency, public post engagement; a YouTube peer carries follower/view/video counts only, with the reason), findings (the deterministic works / needs-attention list: format leaders and decays, channel momentum, reach declines at stable posting, unanswered spikes | each with a hedged reading and an evidence line citing the numbers), financials (paid performance from the connected ads accounts | Meta Ads, Google Ads, TikTok Ads: spend, impressions, clicks, CPC and CPM per account and per campaign with the currency on every figure, revenue and ROAS ONLY where the ad platform itself reports a money value, plus an honest paid vs organic side-by-side that never sums the two), health (what each platform counts as reach, connector freshness, days and posts in the window, and the caveats that explain an empty section). Reads EVERY connected channel per platform (a user with four Instagram accounts gets four), and a platform figure is the fold of its channels. Works with ONE connected channel; every unconnected platform carries an honest not-connected state instead of zeros. Deeper than audience_360 (which answers "who comes from where" and keeps a high-level social reach section): this one judges social performance and names the post behind it. Requires the caller's own autario account (API key or OAuth) with at least one social connector | see get_app_context("social-360").

Take an app YOU own back to private: it leaves the autario app store, its public URL stops working for everyone else, and only you can still open it at its preview URL, where it keeps reading World Bank, FRED and connector data as before. Nothing is deleted, and publish_app puts it back. Use it when a user wants an app off the store, or before shipping a change they do not want strangers to see.

Update the manifest of an app YOU own: its name, tagline, entry URL, version, source repository and the autario datasets or operations it is allowed to read (consumed_datasets, which is what the sandbox bridge enforces at runtime: a World Bank or FRED dataset id, a live Yahoo quote op, or the caller's own Google Search Console and GA4 connector tables). Only the fields you pass change; everything else on the manifest is kept. Use it to declare data access after create_app, to declare that reports of the app may be shared on a secret link (share), or to rename or re-point an existing app. An app you do not own answers "not found". This never changes who may open the app: publishing is publish_app, deliberately a separate act.

Update an existing chart you own. Only the API key that created the chart can update it. Use this to modify the Plotly spec, title, or insight of a previously published chart.

Upload or replace the code of an app YOU own: one self-contained entry document (HTML or JavaScript, max 512 KB, no binary). autario stores it and serves it inside a locked sandbox with no outbound network access, so the app reads World Bank, FRED, Eurostat, SEC and connector data through autario.js (autario.query, autario.datasets, autario.artifacts) rather than calling anything itself; autario inlines the SDK into the served page, so a script tag for https://autario.com/autario.js is optional. Re-uploading the same version replaces it in place. Use it to ship a new build of an app you created with create_app, or to fix one after a user reports something.

Verify that a claimed value is correct against the primary source (World Bank, FRED, Eurostat, OECD, WHO, IMF, ECB, US Census, SEC). Use this when a user asks "did you hallucinate that?" or when you want to double-check your cited numbers before presenting. Pass the indicator, entity, time, and your expected value. Returns whether autario's live value matches, with relative difference and provenance. If your time= matches more than one observation (e.g. a year on a monthly series) you get reason="ambiguous_query" plus the candidate observations instead of a verdict: narrow time= and ask again rather than treating any single candidate as the answer.

Answer "what explains this?" in one call: given an outcome metric + entity, rank which other metrics best explain the outcome. Auto-selects candidates from the ontology if candidates is omitted (same topic + entity_type), so it can reach across World Bank, FRED, Eurostat, OECD, WHO and IMF series without you naming them. Returns a ranking with confidence labels (strong/suggestive/weak/inconclusive) + reason strings + sharpen-suggestions pointing at related domains not yet included. Frequencies are auto-aligned to the coarser common grain — no inflated n-counts. Use this instead of find_drivers when you want a narrative-grade answer.

Save an ARTIFACT into an app: the app's own output or saved state, owned by you. An artifact is what get_app_artifact reads back later | a Plotly chart specification, a saved report configuration, a board, a screener view, a table of World Bank or SEC figures the app computed. Give type (your own label, e.g. "chart", "report", "board"), a title, and spec and/or data as JSON. Pass artifact_id to patch one you already wrote instead of creating another. Use it right after create_app to seed the app with something real, or whenever a user asks you to save a view. You may write into any app you can open; someone else's private app answers "not found".

Append rows of data to an existing dataset you own (from create_dataset) | your own numbers, a derived table you computed, or a series you scraped together from World Bank / FRED / Eurostat results. The schema is automatically inferred from the first batch. All values are stored as text. Maximum 10,000 rows per call; use multiple calls for larger datasets. Requires AUTARIO_API_KEY.