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It runs locally: point it at a folder of CSVs and they load as one joinable database, or connect a Postgres/MongoDB source and query it in place. It profiles every column, classifies what each one is, and builds a prioritized analysis plan with ready-to-run queries. Every answer traces back to an inspectable query, and the findings come back as artifacts you keep. Dolex ships as a published npm package with two front ends over one core: an MCP server any AI assistant can call, and a command-line tool your terminals, scripts, and pipelines run directly.\n\n## What you get\n\n- **Analysis on your own data.** Point Dolex at a folder of CSV files — they become one database your assistant can join across — or connect a live PostgreSQL or MongoDB database and query it in place. Same tools, same analysis, whatever the source.\n- **Data you can stand behind.** A built-in audit surfaces type traps, sentinel values, leaked and duplicate columns, and outliers before they reach a conclusion.\n- **Rigor on every result.** Full column profiling and statistics back the analysis, and each finding traces to a query you can inspect.\n- **A real analysis plan.** Dolex classifies your columns and produces a prioritized analysis plan with ready-to-run queries; derived columns persist across sessions.\n- **Artifacts you keep.** Findings render across 43 chart types as self-contained HTML or PNG, with React components, a design system, and the queries behind every result.\n- **One analyst on call everywhere.** The same core serves your chat assistant and your pipelines, with concurrent work running in its own lane.\n\n## Install\n\n```bash\nnpm install -g @outsidedata/dolex\n```\n\nTo update:\n\n```bash\nnpm update -g @outsidedata/dolex\n```\n\n> **Optional — PNG export.** Rendering charts to PNG (the `--png` flag and the MCP `screenshot` tool) needs Playwright, which is *not* installed by default. Enable it once with `npm install playwright && npx playwright install chromium`. Everything else — HTML charts, querying, analysis, the MCP data tools — works without it.\n\n### Claude Desktop\n\nAdd to your config (`~/Library/Application Support/Claude/claude_desktop_config.json` on macOS):\n\n```json\n{\n  \"mcpServers\": {\n    \"dolex\": {\n      \"command\": \"dolex\"\n    }\n  }\n}\n```\n\n### Claude Code\n\n```bash\nclaude mcp add dolex -- dolex\n```\n\n### Any MCP Client\n\nPoint your client's server command at `dolex`. Launched by an MCP client over stdio, it runs the server. To start it explicitly — or to confirm it boots from a terminal — use the subcommand:\n\n```bash\ndolex mcp\n```\n\n## Command Line\n\nThe same analysis engine, query layer, and 43 chart types are a CLI away — point\nit at a CSV and get a chart, an analysis plan, or a data-quality audit.\n\n```bash\n# Chart a CSV — the pattern matches the shape of the data\ndolex visualize sales.csv -i \"compare revenue by region\"\n\n# Query first, then chart; force a type and palette; render a PNG\n# (PNG export is optional — enable it once with:\n#    npm install playwright && npx playwright install chromium)\ndolex visualize games.csv \\\n  --sql \"SELECT genre, SUM(na_sales) sales FROM video_games_sales GROUP BY 1\" \\\n  -i \"sales by genre\" --pattern lollipop --palette blueRed --png genre.png\n\n# Refine the last chart — works across separate invocations\ndolex refine spec-1a2b3c4d --sort desc --limit 10   # hash printed by visualize\n\n# Explore without charting\ndolex check diamonds.csv          # audit data quality + footguns before trusting it\ndolex query diamonds.csv \"SELECT cut, MEDIAN(price) FROM diamonds GROUP BY 1\" --format json\ndolex analyze diamonds.csv        # auto analysis plan with ready-to-run SQL\ndolex describe diamonds.csv       # column types, roles, stats, sample rows\ndolex patterns                    # browse all 43 chart types\n\n# Persisted derived columns (survive across commands via a .dolex.json manifest)\ndolex transform diamonds.csv --create price_per_carat --expr \"price / carat\"\ndolex query     diamonds.csv \"SELECT cut, AVG(price_per_carat) FROM diamonds GROUP BY 1\"\ndolex columns   diamonds.csv      # list source / derived / working columns\n```\n\n| Command | What it does |\n|---------|-------------|\n| `visualize` | Turn a CSV / source / inline JSON into a chart (HTML, optionally PNG) |\n| `refine` | Tweak a chart by its hash — sort, filter, palette, switch type, … |\n| `query` | Run SQL and print rows (`table` / `json` / `csv` / `ndjson`) |\n| `analyze` | Auto-generate an analysis plan with ready-to-run SQL |\n| `describe` | Profile columns: types, roles, stats, sample rows |\n| `check` | Audit for bad data & footguns (type traps, sentinels, duplicate/leaked columns…) |\n| `transform` | Add a persisted derived column (`--create … --expr …`) |\n| `columns` | List columns by layer (source / derived / working) |\n| `drop` | Remove derived/working columns |\n| `patterns` | List the 43 chart patterns, or show one in detail |\n| `sources` | Register & manage data sources — CSV, Postgres, MongoDB (shared with the MCP server) |\n| `deps` | Report which data sources & optional features are available in this environment |\n| `mcp` | Run the MCP stdio server |\n\nCharts default to `~/.dolex/charts/`; pipe-friendly via `--stdout` / `--json`.\nFull reference: [`docs/CLI.md`](https://github.com/outsidedata/dolex/blob/master/docs/CLI.md).\n\n## Data Sources\n\nDolex works the same whether your data is files or a live database. Point it at:\n\n- **CSV** — a single file or a whole folder, loaded as one joinable in-memory database (SQLite under the hood).\n- **PostgreSQL** — a live database queried in place with real SQL; declared foreign keys are read straight from the schema.\n- **MongoDB** — collections profiled as tables and queried with aggregation pipelines.\n\nThe Postgres and MongoDB drivers are **optional** — the base install stays lean and requires neither. Run `dolex deps` (or ask the assistant for `capabilities`) to see which sources are ready here and the exact one-line command to enable anything missing, so you get an install hint instead of a crash. Credentials stay out of the registry file: a Postgres password is read from an env var at connect time, and a source can be registered even while its database is down, then health-checked with `dolex sources test` / the `test_source` tool once it is up.\n\n```bash\n# CSV stays the zero-config default\ndolex sources add sales ./data/sales.csv\n\n# Live databases — driver installed on demand, secret via env var\ndolex sources add warehouse --type postgres --host db.internal --database analytics --user reader --password-env PGPASSWORD\ndolex sources add events    --type mongodb  --host localhost --port 27017 --database app\ndolex sources test warehouse       # confirm it's reachable with its saved credentials\n```\n\n## The Query Engine\n\nPoint your assistant at a folder of CSV files — or a live Postgres/MongoDB database — and start asking questions. Dolex profiles your data — column types, distributions, cardinality, sample values — giving your assistant everything it needs to answer.\n\nThe built-in query language covers the analysis your assistant runs:\n\n**Aggregations**: `sum`, `avg`, `min`, `max`, `count`, `count_distinct`, `median`, `stddev`, `p25`, `p75`, `percentile`\n\n**Window functions**: `lag`, `lead`, `rank`, `dense_rank`, `row_number`, `running_sum`, `running_avg`, `pct_of_total` — with partition and order control\n\n**Time bucketing**: group by `day`, `week`, `month`, `quarter`, `year` — automatic date parsing\n\n**Joins**: inner and left joins across tables within the same source, with dot-notation field references and ambiguity detection\n\n**Filters**: before aggregation (`filter`) and after (`having`), with operators `=`, `!=`, `>`, `>=`, `<`, `<=`, `in`, `not_in`, `between`, `like`, `is_null`, `is_not_null`\n\nEvery query is validated before execution — field names are checked against the schema with fuzzy \"did you mean?\" suggestions for typos.\n\n## 43 Patterns\n\n| Category | What they're for |\n|----------|-----------------|\n| **Comparison** (9) | Bar, Diverging Bar, Slope Chart, Connected Dot Plot, Bump Chart, Lollipop, Bullet, Grouped Bar, Waterfall |\n| **Distribution** (7) | Histogram, Beeswarm, Violin, Ridgeline, Strip Plot, Box Plot, Density Plot |\n| **Composition** (9) | Stacked Bar, Waffle, Treemap, Sunburst, Circle Pack, Metric, Donut, Marimekko, Icicle |\n| **Time** (7) | Line, Area, Small Multiples, Sparkline Grid, Calendar Heatmap, Stream Graph, Horizon Chart |\n| **Relationship** (5) | Scatter, Connected Scatter, Parallel Coordinates, Radar, Heatmap |\n| **Flow** (4) | Sankey, Alluvial, Chord, Funnel |\n| **Geo** (2) | Choropleth, Proportional Symbol — 33 offline maps (world, US, continents, 17 countries) |\n\n## Tools\n\n| Tool | What it does |\n|------|-------------|\n| `load_source` | Load a data source — CSV file/directory, PostgreSQL, or MongoDB |\n| `list_data` | List loaded datasets |\n| `remove_data` | Remove a loaded dataset |\n| `capabilities` | Report which source types & optional drivers are available in this environment |\n| `test_source` | Health-check a registered Postgres/Mongo source (reachable? credentials valid?) |\n| `describe_data` | Profile columns, stats, sample rows |\n| `analyze_data` | Auto-generate an analysis plan |\n| `query_data` | Run queries (SQL for CSV/Postgres; aggregation pipelines for MongoDB) |\n| `visualize` | Data → ranked chart recommendations (inline, cached, or source + query) |\n| `refine_visualization` | Iterate on a chart |\n| `transform_data` | Create derived columns with expressions |\n| `promote_columns` | Persist working columns to disk |\n| `list_transforms` | List columns by layer (source/derived/working) |\n| `drop_columns` | Drop derived or working columns |\n| `clean_column` | Fix one column with a Python `clean(value)` — parse dates, null sentinels, canonicalize categories; preview then non-destructive apply (requires python3) |\n| `list_patterns` | Browse all 43 patterns |\n| `export_html` | Get raw HTML |\n| `screenshot` | Render to PNG |\n| `server_status` | Inspect server state |\n| `clear_cache` | Reset |\n\n## License\n\nMIT\n","readmeFilename":"README.md"}