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IranTools (paid)

unicode_normalize

Origin URL: https://irantools.johnaio281.workers.dev Origin hostname: irantools.johnaio281.workers.dev Origin brand: IranTools - paid MCP server for AI agents Provider aliases: Unicode normalizer, NFC/NFD converter Family key: irantools.johnaio281.workers.dev::text Endpoint: POST /api/iran/unicode_normalize Summary: Normalize text to NFC, NFD, NFKC or NFKD and report the first differing position. Keyword: unicode normalize Keyword: nfc Keyword: nfd Keyword: nfkc Keyword: canonically equivalent Keyword: normalization form Use case: normalize a string to NFC before comparing it Use case: find where two strings first differ after normalization Use case: apply NFKC to fold compatibility characters Source description: Normalize text to NFC, NFD, NFKC or NFKD and report the first differing position. Auth mode: paid

Price: $0.003 per call (USDC on Base via x402, or USDC on Tempo via MPP).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
formNoNFC
textYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

C2.8/5.0
Behavior2/5

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

With no annotations, the description carries full behavioral burden. It does disclose real operational context (paid access, $0.003 per call, x402/MPP auth, POST endpoint), which is genuinely useful. But it says nothing about return shape, whether the operation has side effects, or rate/error behavior, and the 'first differing position' claim contradicts the single-input schema.

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

Conciseness2/5

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

Front-loaded content is boilerplate (origin URL, hostname, brand, aliases, family key, endpoint) rather than functional meaning. Keywords are duplicated as separate lines and the price/auth block repeats what the endpoint field already implies, burying the one useful sentence.

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

Completeness3/5

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

For a two-parameter, read-only normalization call with no annotations and no output schema, the description supplies the valid forms and plausible use cases. It still leaves the return value and the meaning of the required text input unexplained, and the comparison semantics don't match the schema.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate. It names the NFC/NFD/NFKC/NFKD forms, but those are already the enum values in the schema, and it never states the default (NFC) nor explains the required 'text' parameter at all. Marginal added value over the schema.

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

Purpose4/5

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

The core sentence states a specific verb+resource: normalize text to one of four Unicode normalization forms. It is separable from siblings like persian_normalize or homoglyph_fold in shape, but the description never explicitly distinguishes itself from them. The trailing clause about 'report the first differing position' is confusing since the schema accepts only a single text input.

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

Usage Guidelines3/5

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

The 'Use case:' lines give implied contexts (normalize before comparing, fold compatibility characters), which is better than nothing. However, there is no explicit when-not guidance and no routing to any sibling such as persian_normalize, which an agent might wrongly pick for Persian-specific work.

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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