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Autario MCP server | Query 2,500+ verified datasets from your AI agent
Verified data for AI agents. 2,700+ public datasets (World Bank, FRED, Eurostat, OECD, WHO, ECB, US Census, IMF) joined under one ontology, with built-in statistical analysis and chart publishing. Plug it into Claude Desktop, ChatGPT, Cursor, or any MCP-compatible client | your model gets numbers it cannot hallucinate.
autario.com | Documentation | Get an API Key
verify_value to double-check any claim.autario_time + autario_entity shadow columns, so get_entity_data(USA, [gdp, unemployment, life_expectancy]) returns one wide table, joined automatically.correlate, regression, find_drivers, lag_analysis, seasonality_decomposition and more, with effect sizes, p-values, and plain-language interpretations.publish_chart writes a Plotly spec to autario.com. The result is a permanent, embeddable URL like autario.com/chart/{slug} | the LLM builds the spec, autario pulls real rows for it, no hallucinated data path.Install the server, then ask your assistant questions like these. The model picks the right tools and answers with cited data.
Ask: "What drives US inflation? Look at money supply, oil prices, and unemployment."
Tools: list_indicators -> find_drivers
Output: Ranked drivers with r, p-value, R squared per candidate.
Ask: "Compare life expectancy in Germany, USA and Japan from 2000 to 2023, then publish a chart."
Tools: compare_entities -> publish_chart
Output: Wide-format table joined on year, plus a permanent autario.com/chart/{slug} URL.
Ask: "Is consumer confidence a leading indicator of US retail sales?"
Tools: lag_analysis
Output: Cross-correlation peak at lag k, with interpretation in months.
~/.config/claude/claude_desktop_config.json on Mac/Linux, %APPDATA%\Claude\claude_desktop_config.json on Windows.
{
"mcpServers": {
"autario": {
"command": "npx",
"args": ["autario-mcp"]
}
}
}
Restart your client. The server reports tool count to stderr on launch.
Point your client at the hosted endpoint. No install needed.
URL: https://autario.com/mcp
Transport: Streamable HTTP (POST /mcp)
The hosted endpoint also supports MCP prompts: analyze-dataset, create-chart, compare-countries.
Add API credentials to the stdio config or send them as x-api-key / x-api-secret headers to the HTTP endpoint.
{
"mcpServers": {
"autario": {
"command": "npx",
"args": ["autario-mcp"],
"env": {
"AUTARIO_API_KEY": "your_key",
"AUTARIO_API_SECRET": "your_secret"
}
}
}
}
Get keys at autario.com/account.
28 MCP tools, organized by function.
Search the catalog, inspect schemas, pull rows.
| Tool | What it does | Parameters |
|---|---|---|
search_datasets | Search the Autario public data catalog. Returns dataset IDs, titles, descriptions, categories, publishers, row counts, last_refreshed_at, AND trusted ontology fields (topic, subtopic, unit, frequen... | query (string), category (string), limit (number), page (number) |
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 ent... | topic (string), unit (string), frequency (string), entity_type (string), publisher (string), search (string), limit (number) |
get_entity_profile | Get all indicators available for one entity (country, aggregate, etc.). Returns indicator IDs with metadata + time coverage. Use this to discover what you can query about Germany, USA, G7, or any k... | entity_id (string), topic (string) |
get_dataset_info | Get full metadata for a specific dataset including title, description, publisher, category, keywords, row count, and creation date. | dataset_id (string) |
get_dataset_schema | Get the column names, data types, and total row count for a dataset. Always call this before query_dataset to understand the available columns for filtering and sorting. | dataset_id (string) |
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 q... | dataset_id (string), limit (number), offset (number), fields (string), sort (string), filter (array), aggregate (string), groupby (string) |
list_charts | List published chart visualizations on Autario. Returns chart IDs, titles, insights, linked datasets, and creation dates. Use to discover existing analyses. | q (string), limit (number), offset (number) |
get_chart | Get a specific chart by ID or slug. Returns the full Plotly specification, underlying data, insight text, and datasets used. The chart URL is shareable at autario.com/chart/{id}. | chart_id (string) |
The differentiator. Join indicators across datasets via shared time + entity shadow columns. No manual relationship setup.
| Tool | What it does | Parameters |
|---|---|---|
get_entity_data | Fetch wide-format 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.... | entity_id (string), indicators (array), time (string) |
compare_entities | Compare ONE indicator across MULTIPLE entities (e.g. GDP of DEU vs USA vs CHN). Returns wide-format rows like [{time:"2020", DEU:3846, USA:20937, CHN:14688}, …]. Use this for country comparisons, c... | entities (array), indicator (string), time (string) |
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... | indicator (string), entity (string), time (string), expected (number) |
Run analyses against verified data. Outputs include effect sizes, p-values, and plain-language interpretations.
| Tool | What it does | Parameters |
|---|---|---|
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, com... | indicator (string), entity (string), time (string) |
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 d... | entity (string), a (string), b (string), time (string) |
regression | Linear regression of y ~ x for one entity. Returns slope, intercept, R² and interpretation. Use for "how does X predict Y?" questions. | entity (string), y (string), x (string), time (string) |
pct_change | Period-over-period percentage change for an indicator. Use for growth rates (YoY, QoQ, MoM). | entity (string), indicator (string), time (string), period (string) |
rolling_stats | Rolling window statistics (mean/std/min/max/sum) for an indicator. Smooths noise, reveals trends. | entity (string), indicator (string), window (number), op (string), time (string) |
calculate | 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 thin... | a (string), b (string), entity (string), op (string), time (string) |
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 ret... | a (string), b (string), entity (string), max_lag (number), time (string) |
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, unemp... | indicator (string), entity (string), period (number), time (string) |
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 ranke... | entity (string), target_indicator (string), candidates (array), time (string) |
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). Re... | entity (string), outcome (string), candidates (string), time (string) |
Current quotes for public companies. Beats stale training-data answers.
| Tool | What it does | Parameters |
|---|---|---|
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 in... | ticker (string), metrics (array) |
Publish charts, create + populate datasets. Get keys at autario.com/account.
| Tool | What it does | Parameters |
|---|---|---|
publish_chart | Publish a new chart visualization to Autario. Requires a Plotly spec with column references (x_col, y_col, group_by, group_value). Autario pulls real data from the specified datasets to ensure data... | title (string), plotly_spec (object), insight (string), narration (string), dataset_ids (array) |
update_chart | 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. | chart_id (string), plotly_spec (object), title (string), insight (string), narration (string) |
create_dataset | 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. | title (string), description (string), category (string), is_public (boolean) |
write_rows | 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 dat... | dataset_id (string), rows (array) |
clear_rows | Delete all rows from a dataset while keeping the schema and columns intact. Useful for refreshing data before re-importing. Requires AUTARIO_API_KEY. | dataset_id (string) |
delete_dataset | Permanently delete a dataset and all its data. This action cannot be undone. Only the dataset owner can delete it. Requires AUTARIO_API_KEY. | dataset_id (string) |
| Variable | Default | Purpose |
|---|---|---|
AUTARIO_API_URL | https://autario.com | API base. Override only for self-hosting. |
AUTARIO_API_KEY | unset | Required for write tools. Read tools work anonymously. |
AUTARIO_API_SECRET | unset | Companion secret for the API key. |
World Bank, FRED, Eurostat, OECD, IMF, ECB, WHO, US Census Bureau, plus user-contributed datasets. Every dataset record includes a source_url pointing back to the primary publisher. Live catalog: autario.com/data.
This package is part of the autario monorepo. Tool definitions live in tools.js (single source of truth, shared with the HTTP transport in remote.js). The Tool Reference section above is auto-generated.
# regenerate the README Tool Reference from tools.js
npm run build-readme
# verify README is in sync (used in CI)
npm run check-readme
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