autario

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 quali

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_

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

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|mixe

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), or a table another account shared with you (read-only, listed by get_my_workspace under shared_with_you) | 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 numerical

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.

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.

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.

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.

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, last refresh time, and `health`: state (ok | stale |

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. Use when health.state is stale or never_ran, or the user says "get the latest". The answer is the finished run: status (refreshed | failed), rows, dataset_id and `health` with one outcome per report (delivered | empty | not_fetched, with the sentence why) and next_step.

Explain the state of ONE connected platform (Search Console, Shopify, an ads account, an AI usage bill): is its data fresh, which reports arrived, which came back empty because the account has no such data, which did not arrive and why, and who has to act. Returns the connector plus `health`: state (ok | stale | needs_sign_in | reports_missing | retrying | ours | never_ran | running), sentence, last_run_at, last_success_at, schedule, next_run_at, data_as_of per dataset (basis data_day = newest d

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

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,

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

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).

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 | eno

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

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

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).

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).

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).

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).

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).

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

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).

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).

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).

Create a derived series from two indicators using an Excel-style op: ratio (A/B), ratio_pct (A/B*100), diff (A-B), sum (A+B), product (A*B). 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 tha

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

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.

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 transpare

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 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.

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. IMPORTAN

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 | hallu

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.

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.

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 usa

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 pattern

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 a

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

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, plus shared_with_you: tables another account shared with you read-only (dataset_id, title, shared_by). THE first call when a user referen

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. V

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 mor

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 C

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 com

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 Go

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 Cha

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

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 resul

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 t

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 report

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.

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 se

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

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

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.