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

With no annotations, the description carries the burden of behavioral disclosure. It reveals the tool is CZ-only, models are from 2013, and accuracy may be limited for proper names and new words. It also lists return fields (corrected, model, mode, changed), providing transparency about output.

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

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

The description is well-structured with sections for use cases, args, and returns. It is front-loaded with the main purpose and every sentence adds value without waste. The length is appropriate for the complexity.

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

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

For a 2-parameter tool with no annotations, the description covers purpose, parameters, modes, return values, and limitations. It lacks details on error handling or idempotency, but overall it is sufficiently complete for an AI agent to understand and invoke the tool correctly.

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

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

Schema has 0% description coverage; the description compensates for the 'mode' parameter by detailing each enum value's purpose and examples. However, for 'text', it only states 'Vstupní český text' without additional semantics, which is minimal but clear given the tool name.

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

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

The description clearly states the tool corrects Czech text using Korektor for spelling or diacritics. It distinguishes from sibling tools by listing specific modes (spellcheck, diacritics, strip) and use cases relevant to legal-tech, with examples like OCR output enhancement.

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

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

The description explicitly explains each mode's purpose and when to use them (e.g., diacritics for OCR outputs, strip for URL slugs). It also cautions that the tool is CZ-only and notes model limitations from 2013, providing clear context for proper usage.

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