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check_natural

Check Ukrainian text for unnatural calque phrases and verify naturalness, with recommended alternatives to improve phrasing.

Instructions

Check Ukrainian text for calque-prone phrases and naturalness.

Returns spans flagged as: • calque-prone — phrase appears in the antisurzhyk book corpus (Сербенська, Караванський, Антоненко-Давидович) as something to avoid; prefer gives the recommended alternative. • natural — phrase is attested as a high/medium-confidence rendering in the 29k-sense corpus.

Use this AFTER drafting Ukrainian text, BEFORE returning it to the user. Rewrite calque-prone spans before delivering your response.

Args: text: Ukrainian text (2 chars min).

Returns: { "input": str, "spans": [ {"span": str, "start": int, "end": int, "verdict": "calque-prone" | "natural", "prefer": str | None, # for calque-prone "for_en": str | None, # for natural "source": str | None, "source_url": str}, ... ], "summary": {"calque_prone": int, "natural": int, "score": float}, "citation": {...}, }

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

No annotations provided, so description carries full burden. It explains return format with verdicts and their meanings (calque-prone vs natural), sources, and fields. However, it does not explicitly state it is read-only or non-destructive, though implied by analysis nature.

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

Conciseness5/5

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

Well-structured with clear purpose, usage instruction, and structured return format using bullet points. No superfluous sentences; each sentence adds value.

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?

With output schema described in detail and one input parameter fully explained, the description is complete for the tool's complexity. Sibling tools are not compared, but usage guidance is sufficient.

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?

Schema has zero description coverage; description adds 'Ukrainian text (2 chars min)' which specifies language constraint and minimum length, adding significant meaning beyond type string.

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?

Clearly states the tool checks Ukrainian text for calque-prone phrases and naturalness, with specific verb 'Check' and resource 'Ukrainian text'. Distinct from siblings like 'check' which is more general, and 'render', 'search', 'substantiate' which serve different purposes.

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

Usage Guidelines5/5

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

Explicitly says 'Use this AFTER drafting Ukrainian text, BEFORE returning it to the user' and instructs to rewrite calque-prone spans before delivery. This provides clear when-to-use and action guidance.

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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