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Unicode normalization (NFC/NFKC), script detection and homoglyph/confusable check

unicode_confusables_check
Read-onlyIdempotent

Unicode normalization and confusables check: apply NFC, NFD, NFKC or NFKD, report which forms a string is already in, list the scripts present (all Unicode scripts), and flag confusable and homoglyph characters — Cyrillic а in a Latin word, fullwidth or mathematical look-alikes, all-look-alike words — plus zero-width joiners, bidirectional overrides, invisible and blank characters. Returns per-character detail with code points, names and the ASCII character each imitates. Price: $0.05 per successful call, paid over x402 (USDC on Base).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
formNoNormalisation form to apply, default nfc. Accepted: nfc, nfd, nfkc or nfkd.
textYesText to analyse, at most 1024 characters. Accepted: at most 1024 characters.
detailNosummary (default) or full per-code-point detail (capped at 256 entries). Accepted: summary, full.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.8/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare the safe read-only/idempotent/closed-world profile, so the bar is lower. The description adds real value beyond that: the return shape (per-character code points, names, ASCII imitation) and a mandatory pricing/auth detail ($0.05 per call over x402 USDC on Base) that an agent must know before invoking. It doesn't mention rate limits or the exact response envelope, keeping it just short of a 5.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core normalization/confusables capability in one dense sentence, then closes with the return format and price. It is appropriately sized for the feature breadth, with only mild density in the parenthetical character list.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 3-parameter, no-output-schema utility, the description covers what it does, what it returns, and the payment precondition, so an agent has everything needed to call it correctly. Nothing material is missing given the structured fields it already has.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents form, text, and detail. The description reinforces the accepted forms and the detail flag but adds no syntax or default nuance the schema lacks, which matches the baseline 3 for full-coverage tools.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific set of operations (NFC/NFD/NFKC/NFKD normalization, form detection, script listing, confusable/homoglyph flagging, invisible-character detection) against a clear resource (a text string). An agent can tell exactly what this does and how it differs from adjacent text tools like cyrillic_transliterate or string_similarity without opening a schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no explicit when-to-use or when-not-to-use guidance, and no alternative sibling is named. The intended usage (spotting deceptive glyphs, normalizing input) is only implied by the feature list, leaving the agent to infer the routing.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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