ChatExcel
A powerful server for Excel file processing, data analysis, and visualization, leveraging Python and Go for high performance.Read full description
Lillard01LABSCO SUMMARY
From a question to a chart without writing the SQL or the plot.
Both halves are handled — the query against your data and the rendering — and because the chart choice comes from rules rather than another model call, the same question gives the same chart and nothing leaves the machine on the DuckDB path.
What it is
A visualization server that pairs a database connection with chart generation. You describe what you want to see, it queries and renders a Plotly chart as an interactive HTML widget, and the chart suggestion is rule-based rather than delegated to another model — so it works offline once configured.
What you get
- Eight chart types with the case each one serves: bar for comparing categories, line for trends over time, scatter for relationships, pie for proportions, histogram for distributions, box for comparing distributions, heatmap for correlations, area for cumulative trends
- Data access in plain language — list the tables, analyse one, or query the top rows by a measure
- DuckDB locally, with CSV import and an interactive database browser, plus the ability to switch to another
.duckdbfile mid-conversation - Databricks as an alternative — browse catalogs, list schemas, switch catalog, and check which database you are connected to
- Automatic statistical analysis and pattern detection alongside the chart
- A CLI:
mcp-viz configuresets up the client,mcp-viz statusreports what is configured,mcp-viz testexercises the server,mcp-viz create-dbbuilds a sample database, and themcp-viz databrickssubcommands configure, test and remove credentials
Requirements
pip install mcp-visualization-duckdb brings DuckDB and everything else with it — no separate database install. Then mcp-viz configure detects your platform, finds the client config, backs it up, writes the entry and validates the result. For Databricks you need the workspace hostname, the SQL warehouse HTTP path and a personal access token, entered through mcp-viz databricks configure, which stores them in your system keyring or an encrypted file rather than in plain text or shell history. Setup is one-time; you reconfigure when a token expires or you move workspace or warehouse. SQL injection protection is built in.
Setup effort
One command — pip install mcp-visualization-duckdb && mcp-viz configure
USE CASES
01
Seeing what is in a dataset you just received
List the tables, analyse the one you want, and pull the top rows before deciding what to plot.
02
Turning a CSV into something you can look at
Load the file as a table, then ask for the chart in words rather than opening a spreadsheet.
03
Checking whether two things move together
Ask for the correlation and get a scatter or a heatmap, with the statistical read alongside it.
04
Working against the warehouse instead of a local file
Configure Databricks once, then browse catalogs and schemas and chart from the warehouse in the same conversation.
05
Switching between projects
Point at a different DuckDB file mid-conversation instead of restarting with new configuration.
BEFORE YOU INSTALL
What to know first
Requires
pip on PATH
INSTALLATION
Connect your client
Server setup
TERMINAL
pip install mcp-visualization-duckdb && mcp-viz configureMCP Clients
Claude Desktop
CLAUDE DESKTOP · TERMINAL
mcp-viz configure
