autario-mcp

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

Why autario-mcp

• No hallucinated numbers. Every value is sourced from a known publisher and cited back to a primary URL. Use verify_value to double-check any claim.
• Cross-dataset joins, no setup. Indicators across different datasets share autario_time + autario_entity shadow columns, so get_entity_data(USA, [gdp, unemployment, life_expectancy]) returns one wide table, joined automatically.
• Statistical primitives built-in. correlate, regression, find_drivers, lag_analysis, seasonality_decomposition and more, with effect sizes, p-values, and plain-language interpretations.
• Charts that persist. 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.
• LLM-agnostic. Works with Claude, GPT, Gemini, local models | anything that speaks MCP.

Quick Demo

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.

Install

Claude Desktop, Cursor, Cline (stdio)

~/.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.

Claude Web, OpenAI Custom GPTs, any HTTP MCP client

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.

Enable write tools (publish charts, create datasets)

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.

Tool Reference

28 MCP tools, organized by function.

Discovery & Query

Search the catalog, inspect schemas, pull rows.

Cross-Dataset Joins (Ontology)

The differentiator. Join indicators across datasets via shared time + entity shadow columns. No manual relationship setup.

Statistical Analysis

Run analyses against verified data. Outputs include effect sizes, p-values, and plain-language interpretations.