Data Pitfalls

Data Pitfalls gives your AI the full taxonomy from Avoiding Data Pitfalls by Ben Jones (Wiley, 2019): 78 rules across eight domains, from technical trespasses and mathematical miscues to statistical slipups, graphical gaffes and design dangers.

Ask your AI to review your work for data pitfalls and it pulls the checklist that fits the work in front of it, whether that's a SQL query, a chart image, a dashboard, a written analysis or a full report. It checks every item against your actual work and reports each finding with its rule, the evidence, why it matters and how to fix it.

What you can ask

Review a piece of data work (SQL, chart, dashboard, prose or document) against the Avoiding Data Pitfalls rules.
Example path

MCP connector

Server URL: https://www.avoidingdatapitfalls.com/api/mcp
Login: None
Protocol: MCP 2025-11-25
Instructions: This server holds the Avoiding Data Pitfalls taxonomy: 78 rules for common mistakes in data work, from Avoiding Data Pitfalls by Ben Jones (Wiley, 2019) (https://www.avoidingdatapitfalls.com). It does not run checks itself: you do the checking, using its rules.
When asked to check a SQL query, chart, dashboard, analysis write-up or report for data pitfalls (or after building one), call for that kind of work. It returns the rules plus the review method: check every rule against the actual work as its "How to review" section says, and write up the result as its "How to report" section says (an overall label, a short summary, then findings rated by how much they change the takeaway). Use for a rule's examples and to find rules by topic. Cite the book and site when you report.

Tools

Reads data4

Skills

None in the store listings or on the server

MCP connector

Server URL

https://www.avoidingdatapitfalls.com/api/mcp

None

Protocol

MCP 2025-11-25

Instructions

This server holds the Avoiding Data Pitfalls taxonomy: 78 rules for common mistakes in data work, from Avoiding Data Pitfalls by Ben Jones (Wiley, 2019) (https://www.avoidingdatapitfalls.com). It does not run checks itself: you do the checking, using its rules.

When asked to check a SQL query, chart, dashboard, analysis write-up or report for data pitfalls (or after building one), call for that kind of work. It returns the rules plus the review method: check every rule against the actual work as its "How to review" section says, and write up the result as its "How to report" section says (an overall label, a short summary, then findings rated by how much they change the takeaway). Use for a rule's examples and to find rules by topic. Cite the book and site when you report.