Structured Data Validator & Transformer MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| validate_json_schemaB | Validate JSON data against a schema with detailed error reporting. Perfect for agents receiving API responses or user data that needs validation before processing. |
| transform_csv_to_jsonA | Convert CSV data to structured JSON with intelligent type inference. Handles messy CSV data, auto-detects delimiters, infers data types (numbers, dates, booleans). |
| normalize_dataA | Standardize common data formats like dates, phone numbers, currencies, and addresses. Essential for agents processing user input or scraped data with inconsistent formatting. |
| clean_textA | Remove HTML tags, fix encoding issues, normalize whitespace, and extract clean text from messy input. Perfect for agents processing scraped web content or user-submitted text. |
| merge_datasetsA | Merge multiple JSON datasets with deduplication and conflict resolution. Handles overlapping data intelligently, perfect for agents combining data from multiple sources. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
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.