Structured Data Validator & Transformer MCP Server
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- AlicenseNot gradedqualityAmaintenanceExposes data validation as tools so an AI agent can check whether data is actually correct before using it, scoring CSV files with per-row problem details, catching impossible dates, inferring schemas, and generating readable reports.0MIT

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AlicenseNot gradedqualityCmaintenanceProvides deterministic tools for transforming, formatting, and inspecting structured data for AI agents.3 npm19Apache 2.0- AlicenseNot gradedqualityDmaintenanceProvides data preparation, anonymization, and processing tools for AI agents, handling big data with Polars and DuckDB.MIT
- AlicenseNot gradedqualityBmaintenanceEnables AI assistants to validate CSV exports against schemas, infer schemas from example files, profile datasets, and diff before/after exports for lab/LIMS data-quality checks.MIT
- AlicenseBqualityCmaintenanceProvides 40+ specialized tools for AI assistants to load, transform, analyze, and validate CSV data from URLs and string content through the Model Context Protocol.412Apache 2.0
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TDQS
Scored across 5 tools
Each tool targets a distinct data operation: JSON schema validation, CSV-to-JSON conversion, format normalization, text cleaning, and dataset merging. No two tools have overlapping purposes, ensuring clear selection for agents.
All tool names follow a consistent verb_noun pattern in snake_case (e.g., validate_json_schema, transform_csv_to_json). The pattern is uniform and predictable, aiding agent understanding.
With 5 tools, the set is well-scoped for a data validator and transformer. Each tool covers a core operation without unnecessary bloat or deficiency.
The tool set covers major data transformation tasks (validation, conversion, normalization, cleaning, merging). Minor gap: missing reverse CSV-to-JSON or schema inference, but core workflows are supported.