Autario MCP server

Read from the package source without running it. The installed server may list more.

• ai_visibility_360

  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). Call it when a user asks "how visible is my brand in ChatGPT", "do assista

• audience_360

  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

• bubble_or_not

  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 + med

• calculatewrite action

  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.

• chart_instructions

  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.

• clear_rowswrite action

  Delete all rows from a dataset while keeping the schema and columns intact. Useful for refreshing data before re-importing. Requires AUTARIO_API_KEY.

• compare_entities

  Compare ONE indicator across MULTIPLE entities (e.g. GDP of DEU vs USA vs CHN). 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, cros

• correlate

  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.

• create_chart_from_specwrite action

  PREFERRED chart-creation path. Send a structured Builder spec (chart_type + x_col + y_col[s] + optional group_by, palette, axis overrides, annotations) and Autario builds the 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 num

• create_datasetwrite action

  Create a new empty dataset on Autario. Returns a dataset_id you can populate with write_rows. Only create new datasets if the data does not already exist on Autario. Requires AUTARIO_API_KEY.

• decompose_drivers

  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.

• delete_datasetwrite action

  Permanently delete a dataset and all its data. This action cannot be undone. Only the dataset owner can delete it. Requires AUTARIO_API_KEY.

• describe

  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).

• discover_by_topic

  Discover the most relevant verified datasets for a given topic. 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 qualit

• find_drivers

  KILLER ANALYSIS: given a target KPI + multiple candidate indicators, rank which candidates best predict the target 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.

• get_app_artifact

  Load ONE saved artifact from an autario data app | the EXACT view state the user saved there (report configuration, chart spec, OKR board, 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; foreig

• get_app_context

  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) 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 t

• get_chart

  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_u

• get_company_snapshot

  Get current stock metrics for a public company. 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,

• get_dataset_info

  Get full metadata for a specific dataset including title, description, publisher, 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

• get_dataset_schema

  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|…), `

• get_engine_report

  ADMIN/CURATOR ONLY. The machine-readable health of the autario data engine, 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

• get_entity_data

  Fetch data for ONE entity across MULTIPLE indicators — joined automatically on time via shadow columns. 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/

• get_entity_profile

  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 I

• get_my_workspace

  YOUR data-app workspace in ONE call: every autario app the calling user has activated or connected, each with its providers, 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 <app>", "my dashboard", "my report" or asks what they have on autario | it replaces one get_app_context round

• get_traction_overview

  ADMIN/CURATOR ONLY. Fetch the autario traction overview | 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), and the signup funnel (new signups, source/medium/trigger), plus the biggest drop-off in plain language, MCP-calls-per-dataset (what agents pull), top charts by views, top API endpoints (h

• lag_analysis

  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?".

• list_apps

  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, e.g. Search Console; public = it runs on public autario datasets only). 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

• list_chart_candidates

  AUTARIO-INTERNAL (admin only). List datasets 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) +

• list_charts

  List published chart visualizations on Autario. Returns chart IDs, titles, insights, linked datasets, and creation dates. Use to discover existing analyses.

• list_connectors

  List the REST API connectors set up on this Autario account, each with 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 to find a connector before refreshing it or reading its hosted, auto-typed table. Connectors are created by the account owner

• list_indicators

  Browse the Autario indicator registry — semantic layer over all 2600+ datasets. Each indicator has a topic (economy, health, energy, …), unit (USD, %, years, …), frequency (year/month/day), and entity_type (country/subnational/aggregate). Use this to discover what data is available before querying it. Much more precise than search_datasets when you know what topic or unit you need.

• pct_change

  Period-over-period percentage change for an indicator. Use for growth rates (YoY, QoQ, MoM).

• publish_chartwrite action

  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_dataset

  Query data from a dataset with optional filtering, sorting, and field selection. Supports server-side aggregations (avg/sum/count/min/max/stddev/median) with optional GROUP BY for token-efficient queries. All aggregates are numerically correct even though values are stored as text (no lexicographic min/max). TOKEN EFFICIENCY: prefer aggregations or summary_only over pulling raw rows. "average GDP

• refresh_connector

  Pull the latest data from a connector's source REST API now and refresh its hosted Postgres table on Autario. Returns the new row count and the dataset_id you can then read with query_dataset / get_dataset_schema. Use when the user wants fresh data before analysis. The connector must already exist (the owner sets it up in the UI at autario.com/manage). Deterministic fetch, no LLM cost. Requires AU

• regression

  Linear regression of y ~ x for one entity. Returns slope, intercept, R² and interpretation. Use for "how does X predict Y?" questions.

• report_data_issue

  Report a data-quality problem you found in a dataset or chart, 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 p

• request_chart

  AUTARIO-INTERNAL (admin only). HIGH-LEVEL chart request: 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 mul

• rolling_stats

  Rolling window statistics (mean/std/min/max/sum) for an indicator. Smooths noise, reveals trends.

• search_datasets

  Search the Autario data catalog. 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 neve

• seasonality_decomposition

  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.

• seo_360

  SEO 360 | the caller's OWN deterministic 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

• update_chartwrite action

  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.

• verify_value

  Verify that a claimed value is correct. 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

• what_matters

  HEADLINE OP: 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). 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 coars

• write_rowswrite action

  Append rows of data to an existing dataset. 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.

• Code scan9 source files scanned25/25
• –Live reliabilityno gateway calls yet and no remote to proben/a
• –Tool poisoningtools not inspected (local package is not executed); not countedn/a
• Auth qualitystatic API keys via environment variables6/15
• Maintenancelast push 33 days ago12/15
• Maintainer identityregistry namespace matches repository owner; GitHub account older than a year8/10

Safety score

78/100

higher than 62% of 1,759 Finance servers

Tools

47

the typical Finance server has 8

Tools that change things

17%

typical: 0%. Fewer write tools means less an agent can break

GitHub stars

2

more than 80% of 945 Finance servers

Downloads a week

130

more than 74% of 422 Finance servers with a package

Autario: common questions

Is Autario MCP server safe?

Mostly: it is graded B (78/100). Read the Autario safety report

How do I install Autario?

It runs on your machine. Copy the Claude Code, Cursor, VS Code or Claude Desktop config from the install section.

Does Autario need an API key?

Yes. The registry entry asks for AUTARIO_API_KEY, AUTARIO_API_SECRET.

Is Autario maintained?

The last commit was 39 days ago (2026-09-27). The latest release is v2.11.2.

What can I use instead of Autario?

Servers from other publishers that do the same job: Worldbank MCP server, BankMCP™ MCP server and Yahoo Finance MCP server.

How does Autario compare with other Finance MCP servers?

safety score 78/100 (higher than 62% of 1,759 Finance servers); tools 47 (the typical Finance server has 8); tools that change things 17% (typical: 0%. Fewer write tools means less an agent can break). Full comparison.