Find and Fix PostgreSQL Data Issues: Quality & Profiling Tools
Bad data causes bugs. It causes bad decisions. It causes headaches.
The Data Quality & Profiling MCP server bundle helps you find data problems before they cause real issues. Nine MCP tools for profiling columns, finding duplicates, validating constraints, detecting outliers, and checking referential integrity. It's your data quality audit toolkit.
What This Bundle Does
Data quality validation covers a lot of ground. Profile columns to understand data distribution. Find duplicate rows. Validate constraints. Detect outliers. Check referential integrity.
This bundle gives you MCP tools for all of it.
Profiling Columns to Understand Data
What's actually in your columns? What are the min, max, average values? How many nulls? How many distinct values?
The MCP tools profile columns with statistical information. Get min, max, avg, median, null counts, distinct value counts. Understand your data distribution. Know what's normal before you start looking for problems.
You've got a users table and want to understand the age column. Profile the column to see min age, max age, average age, null count, distinct values. Understand what's normal.
Ask "Profile the age column in the users table" and the AI profiles the column, calculates statistics, shows you the data distribution.
Finding Duplicate Rows
Duplicates are everywhere. Duplicate email addresses. Duplicate order IDs. Duplicate records that shouldn't exist.
The MCP tools find duplicate rows based on column combinations. See which rows are duplicates. Understand how many duplicates exist. Clean them up.
Find duplicates in the users table by email. See which emails appear multiple times. Clean them up.
Ask "Find duplicate email addresses in the users table" and the AI finds duplicates, shows you which rows are duplicates, suggests cleanup options.
Validating Constraints
Constraints exist for a reason. Are they being followed? Are there NOT NULL violations? UNIQUE violations? CHECK constraint violations?
The MCP tools validate constraints to find violations. Check NOT NULL, UNIQUE, and CHECK constraints. Find data that violates your rules. Fix them before they cause problems.
You've got a NOT NULL constraint on email, but some rows have null emails. Validate constraints to find violations. Fix them.
Ask "Check for constraint violations in the users table" and the AI validates all constraints, finds violations, shows you what's wrong.
Analyzing NULL Values
Nulls aren't always bad, but you need to know where they are. Which columns have nulls? How many? What percentage?
The MCP tools analyze NULL value distribution. See which columns have nulls, how many, what percentage. Understand data completeness. Know what's missing.
Analyze nulls in the users table. See which columns have nulls. Understand data completeness.
Ask "Analyze NULL values in the users table" and the AI analyzes null distribution, shows you which columns have nulls, explains the impact.
Checking Referential Integrity
Broken foreign keys are a nightmare. Foreign keys pointing to non-existent rows. Orphaned references. Data that doesn't make sense.
The MCP tools find orphaned foreign key references. See which foreign keys are broken. Fix referential integrity issues before they cascade.
You've got orders referencing users, but some user IDs don't exist. Check referential integrity to find orphaned references. Fix them.
Ask "Check referential integrity for orders referencing users" and the AI checks foreign keys, finds orphaned references, shows you what's broken.
Detecting Statistical Outliers
Outliers might be errors. Or they might be legitimate. Either way, you need to know about them.
The MCP tools detect statistical outliers using IQR or z-score methods. Find values that are unusual. Understand if they're errors or legitimate outliers. Decide what to do with them.
Detect outliers in the age column. Find ages that are way outside normal ranges. Decide if they're errors.
Ask "Find outliers in the age column" and the AI detects outliers using statistical methods, shows you unusual values, helps you decide if they're errors.
Generating Quality Reports
Sometimes you need the full picture. A complete data quality report for entire tables. What are all the issues?
The MCP tools generate quality reports covering all quality checks. Profile columns, find duplicates, validate constraints, check referential integrity. Get a complete picture of data quality.
You want a complete quality report for the users table. Generate a report covering all quality checks. See all issues at once.
Ask "Generate a data quality report for the users table" and the AI profiles columns, finds duplicates, validates constraints, checks referential integrity, provides a complete report.
How AI Uses This Bundle
When you ask "Find duplicate email addresses in the users table," the AI loads the Data Quality & Profiling MCP server bundle, uses postgres_find_duplicates to detect duplicates, uses postgres_profile_column to understand data distribution, provides cleanup recommendations.
Ask "Profile the age column" and it profiles the column, calculates statistics, shows you the data distribution.
Want to validate constraints? "Check for constraint violations" triggers constraint validation, finds violations, shows you what's wrong.
Getting Started
Enable the Data Quality & Profiling MCP server bundle in your MCPBundles account. Connect your PostgreSQL database (host, port, database, user, password—one-time setup). Then start asking data quality questions. The AI automatically uses the right MCP tools based on what you ask.
Want more? Check out the Data Exploration MCP Server Bundle for browsing and searching data, the Schema Discovery MCP Server Bundle for understanding database structure, or the Main PostgreSQL MCP Server Bundle for all 38 MCP tools.
Enable the Data Quality & Profiling MCP server bundle and start finding data issues.