Data Validator
The Data Validator MCP server checks data for schema compliance, type correctness, format adherence, and integrity issues. Data engineers and analysts use it to verify datasets, API payloads, and inputs before processing in analytics, ML training, or storage pipelines.
Overview
Data Validator is an MCP server that performs programmatic validation of data structures and content. It processes JSON, CSV, or custom formats to identify errors like missing fields, invalid types, or rule violations, integrating directly into LLM workflows or automation scripts.
Key Capabilities
No specific tools are listed, but the server supports core data validation functions:
- Schema validation: Compares data against JSON Schema or custom definitions.
- Type and format checking: Verifies strings, numbers, dates, and regex patterns.
- Integrity verification: Detects duplicates, ranges, and referential issues. These are accessed via MCP calls like validate_data(schema, data).
Use Cases
- ETL Pipelines: Run schema validation on incoming CSV files to flag malformed records before database insertion.
- API Gateways: Check request payloads for required fields and types to prevent invalid data from reaching backends.
- ML Data Prep: Validate training datasets for missing values or outliers prior to model fitting.
- User Inputs: In web apps, validate form data for business rules like email formats or numeric ranges.
Who This Is For
Data engineers building pipelines, backend developers securing APIs, data analysts cleaning datasets, and ML practitioners ensuring input quality. Ideal for teams handling high-volume data where errors propagate costly issues.