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Buggy1111

anonymize-mcp

by Buggy1111
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

check_readability

Analyze Czech text readability using PONK metrics, grammatical rules, lexical surprise, and speech acts. Identify complex sentences, rare vocabulary, and sentence types to improve official communication.

Instructions

Analyzuje čitelnost českého textu pomocí PONK — 4 feature sety (v0.7.0).

PONK byl navržen pro úřední komunikaci s občany. V0.7.0 wrapper vystavuje
všechny 4 jeho feature sety, ne jen metriky:

1. **Overall metrics** — ARI (years of education needed), Verb Distance,
   Activity, Lexical diversity. (Always returned.)

2. **Grammatical rules** (``include_rules=True``) — list pravidel které se
   v textu aktivovala. Každé pravidlo má český název a popis. Aktuálně PONK
   detekuje: Nedostatek sloves, Přemíra podstatných jmen, Dlouhé věty,
   Sloveso příliš daleko v klauzi, ...

3. **Lexical surprise** (``include_lexical_surprise=True``) — distribuce
   sémantické překvapivosti slov (1=běžné, 16=velmi vzácné/odborné).
   Vrátí summary: kolik slov je common / surprising / very_surprising.

4. **Speech acts** (``include_speech_acts=True``) — typy vět (Situace,
   Kontext, Postup, Proces, Podmínky, Doporučení, Odkazy, Prameny).

Args:
    text: Vstupní text.
    input_format: ``txt`` (default), ``md``, ``docx``.
    include_rules: Default ``True``. List aktivovaných gramatických pravidel.
    include_lexical_surprise: Default ``True``. Distribuce vzácnosti slov.
    include_speech_acts: Default ``True``. Typy vět/řečové akty.
    include_highlighted_html: Default ``False`` (úspora bandwidthu — HTML
        má 100+ KB). Zapni pro vizualizační report/PDF.

Returns:
    ``metrics``, ``counts``, ``version``, ``processing_time_s``,
    + volitelné ``rules`` (list), ``lexical_surprise`` (dict),
    ``speech_acts`` (dict), ``highlighted_html`` (str).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYes
input_formatNotxt
include_rulesNo
include_lexical_surpriseNo
include_speech_actsNo
include_highlighted_htmlNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Tool Definition Quality

A4.3/5.0
Behavior4/5

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

No annotations are provided, but the description details behavioral aspects: which features are always returned, defaults for boolean parameters, and a warning that highlighted_html is large (100+ KB). It also notes the library version (v0.7.0). This is transparent and helpful.

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 well-structured with headings and lists, enabling quick scanning. It is fairly long but each sentence provides necessary information. Minor redundancy (e.g., listing feature sets twice) but still efficient.

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?

The tool is complex (4 feature sets, 6 parameters) and the description covers all aspects: input formats, optional features, return values, and even a performance note. With an output schema present, the description complements it effectively.

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

Parameters5/5

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

The input schema has 0% description coverage, but the description's Args section explains all six parameters in detail, including their defaults, types, and effects. This adds high value beyond the schema alone.

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 clearly states the tool's purpose: analyzing readability of Czech text using PONK with 4 specific feature sets. It distinguishes itself from sibling tools (morphology, anonymize, correct_text, etc.) by focusing on readability metrics, rules, lexical surprise, and speech acts.

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 description does not provide explicit when-to-use or when-not-to-use guidance. It implies usage for Czech readability analysis but does not compare with alternatives or specify scenarios where other tools might be preferred.

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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