USER GUIDE

Get started with Dolex

Connect Dolex to Claude, point it at your CSV files, and get expert-level visualizations in minutes. This guide walks you through setup and your first analysis.

01

Install Dolex

Install Dolex globally with npm:

npm install -g @outsidedata/dolex

This installs the dolex command globally. Next, you'll point Claude at it.

02

Connect to Claude Code

Dolex runs as an MCP server that Claude Code connects to over stdio. You register it once and it's available in every conversation.

Add to your MCP config

Open your Claude Code MCP settings and add Dolex as a server. The exact config depends on your setup:

~/.claude/claude_desktop_config.json
{
  "mcpServers": {
    "dolex": {
      "command": "dolex"
    }
  }
}

Note

If you use nvm or a version manager, you may need a shell wrapper to set the right Node path. See the Docs for details.

Verify the connection

Once configured, you can verify Dolex is running by asking Claude:

Y

What visualization tools do you have available?

C

I have access to Dolex, a CSV visualization tool with 43 chart types across 7 categories. I can load your CSV files and create charts and full analysis plans.

03

Your first chart

The fastest way to see Dolex in action is to point it at a CSV file on your machine. Tell Claude to load it, then ask a question about the data.

Y

Load ~/data/sales-2024.csv and show me which regions are performing best.

C

I'll load that CSV with Dolex. Found 4 columns (region, quarter, revenue, units) across 500,000 rows. Based on a comparison intent, Dolex recommends a slope chart to show how each region's trajectory differs across quarters.

Note

Your CSV files must be on your local machine — Dolex reads them from disk via file paths. You can't drag a file into Claude's chat window and have Dolex pick it up. Instead, give Claude a path like ~/data/sales.csv and tell it to use load_csv. Dolex runs locally as an MCP server with full filesystem access, so the path gets resolved server-side on your machine.

Notice that Claude didn't default to a bar chart. Dolex's pattern selector analyzed the data shape and your comparison intent, then recommended a chart type that actually reveals the story — in this case, the crossing trajectories that a bar chart would hide.

Force a specific chart type

If you want a specific chart type, just ask for it by name. Dolex has 43 chart types — far beyond bar, line, and pie:

Bump chartRankings that change over time
BeeswarmDistribution with every data point visible
SankeyFlow between categories
WafflePart-to-whole with visceral impact
Sparkline gridMany trends at a glance
ViolinDistribution shape, not just quartiles
TreemapHierarchical composition
RidgelineDistribution comparison across groups

Browse all 43 chart types →

04

Explore a dataset automatically

This is where Dolex really shines. Instead of asking one question at a time, you can hand it a dataset and let Dolex figure out what's interesting. It examines every column, understands what each one represents, and generates a full analysis plan.

How to use it

Just ask Claude to explore your data. It will load the CSV, call the analysis planner, and execute each recommended visualization with a narrative finding. The output looks like a data analyst sat with you and walked through the dataset.

In Claude
Load ~/data/sales-2024.csv and explore what's interesting

What the planner does

The planner examines column profiles and generates 4–6 analyses, each with a ready-to-execute query. It picks from these categories:

Trend

Monthly revenue over time

line, area, sparkline grid

Comparison

Revenue by region

bar, lollipop, diverging bar

Distribution

Spread of order values

histogram, violin, beeswarm

Relationship

Price vs. quantity correlation

scatter, heatmap

Ranking

Top 15 products by sales

bar, lollipop

Composition

Revenue share by category

treemap, sunburst, donut

The planner diversifies — it won't produce two trend charts or two comparisons. Each step covers a different analytical angle.

Manual approach

If you prefer to drive the analysis yourself, you can load a CSV and call the planner separately:

Y

Load ~/data/sales.csv, then analyze it and tell me what's interesting.

C

I'll load this CSV and run Dolex's analysis planner. Based on the column profiles (date, region, product, revenue, quantity), the planner suggests 5 analyses covering trends, comparisons, distribution, ranking, and relationships. Let me execute each one...

05

Work with CSV files

For anything beyond small inline datasets, load a CSV file (or a whole directory of them). Dolex processes the data locally — only summaries and chart specs flow through the conversation, not your raw data. Load multiple CSVs and Dolex can automatically combine them by matching columns.

Load a CSV

Tell Claude about your CSV file. Dolex loads it locally so you can ask questions about it without sending your actual data to the AI:

Y

Load ~/data/orders.csv and show me revenue by month for the past year.

C

Loaded "orders" — found 8 columns across 500,000 rows. I'll query with a monthly revenue aggregation and visualize the trend.

Once loaded, you can ask follow-up questions without reloading. Loaded CSVs persist across conversations:

Y

Now break that down by product category.

C

I'll add product category as a grouping dimension. Since there are 6 categories across 12 months, Dolex recommends a sparkline grid — one mini trend per category so you can compare trajectories at a glance.

What stays private

Claude sees

  • Column names and types
  • Summary statistics (min, max, mean)
  • Top categorical values
  • 5 sample rows
  • Chart spec IDs (not raw data)

Stays on your machine

  • Your actual data rows
  • All data processing
  • Query result sets
  • The rendered chart HTML
06

Refine and iterate

Every chart Dolex produces can be refined. Ask for changes naturally — Claude will call the refine tool with no data round-trip (it references the chart by its spec ID).

Things you can change

Sort by value descending
Only show the top 10
Use a warm color palette
Highlight North and South regions
Show it as a lollipop chart instead
Change the title to 'Q4 Regional Performance'
Flip the axes

Switch chart types

Every visualization comes with alternative chart type recommendations. You can switch between them without re-sending data:

Y

Show that as a heatmap instead.

C

Switching to heatmap. The same data now shows intensity patterns — you can see the Q4 surge is concentrated in the North and East regions.

Color palettes

Dolex ships with purpose-built palettes. Ask by name or by intent:

categoricalDefault distinct colors
warmReds, oranges, yellows
blueCool sequential
profit-lossGreen (+) to red (-)
traffic-lightRed / yellow / green
temperatureCool to warm
07

Tips for better results

1

State your intent, not just the chart type

Instead of “make a bar chart,” say “compare revenue across regions” or “show how rankings changed over time.” Dolex's pattern selector uses intent keywords to score all 43 chart types — the more context you give, the better the recommendation.

2

Load your CSV files for real datasets

Inline data works for quick demos, but for anything over ~50 rows, give Claude a file path and let Dolex load it. Handles up to 500k rows. It's faster (no data in the conversation), more private (data stays local), and unlocks the full query engine — filtering, grouping, sorting, and even combining multiple CSV files.

3

Let the planner drive discovery

When you don't know what questions to ask, ask Claude to explore your data or call analyze_data. The planner examines column profiles and generates hypotheses — it finds the time trends, comparisons, and distributions you'd want to see.

4

Iterate, don't restart

Every chart has a spec ID. When you ask for changes, Dolex references the existing spec — no data re-upload, no re-querying. This makes refinement nearly instant.

5

Ask for the chart type by name

Dolex has patterns most people have never heard of: ridgeline plots, beeswarm plots, alluvial diagrams, horizon charts. If you know the name, just ask for it. If you don't, describe what you want to see and let the pattern selector figure it out.

6

Set titles upfront

Include a title and subtitle in your initial request to avoid a refine round-trip. “Show revenue by region, title it Regional Performance Q4” is one tool call instead of two.

Ready to go deeper?

The docs cover every tool, the query engine, compound visualizations, and the full rendering pipeline.