MCP Presidio
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TDQS
Scored across 10 tools
Every tool has a clearly distinct purpose with no ambiguity. The tools are well-organized into categories: custom recognizer management (add_custom_recognizer), analysis (analyze_text, analyze_structured_data, batch_analyze), anonymization (anonymize_text, anonymize_structured_data, batch_anonymize), metadata retrieval (get_supported_entities, get_anonymization_operators), and validation (validate_detection). Each tool serves a unique function within the PII processing workflow.
The tool names follow a highly consistent verb_noun pattern throughout. All tools use snake_case with clear action-object naming: add_custom_recognizer, analyze_text, anonymize_structured_data, batch_analyze, get_supported_entities, validate_detection. The naming convention is perfectly uniform across all 10 tools, making them predictable and easy to understand.
With 10 tools, this server is well-scoped for PII detection and anonymization. Each tool earns its place by covering essential operations: analysis (single, structured, batch), anonymization (single, structured, batch), configuration (custom recognizers), metadata (entities, operators), and validation. The count is appropriate for the domain without being overwhelming or insufficient.
The tool surface provides complete coverage for PII processing workflows. It includes all necessary CRUD-like operations: adding custom recognizers, analyzing text/structured data (individually and in batch), anonymizing with various operators (individually and in batch), retrieving metadata about supported entities and operators, and validating detection accuracy. There are no obvious gaps that would hinder agent workflows.