kasahorow

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

The description indicates the tool is read-only and returns 'definitions with parts of speech'. However, since no annotations are provided, the description carries the full burden and fails to disclose potential error cases or idempotency guarantees.

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 a single, well-structured sentence that front-loads the purpose. It is brief and every phrase adds meaning, though it could be slightly expanded with usage guidance without losing conciseness.

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?

Given the absence of an output schema, the description adequately summarizes the return value. All three required parameters are covered, and the sibling tool is mentioned. It does not specify error handling or language availability, but for a straightforward lookup, the description is sufficient.

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?

The description adds value beyond the schema by explicitly defining 'fl' as 'your language' and 'tl' as 'the word's language', clarifying the directionality of the lookup. The schema already provides ISO code examples, but the description resolves ambiguity.

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's purpose: looking up a word in the kasahorow dictionary. It specifies the dictionary name (WOAKA) and distinguishes from the sibling tool 'validate_translation' by focusing on lookup rather than validation.

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 provides no guidance on when to use this tool versus alternatives. It does not mention when not to use it or any prerequisites for using the dictionary (e.g., language availability or internet connection).

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

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

With no annotations, the description carries the full burden and does well: it discloses the paid nature, exact dollar amounts per tier, the asynchronous process (payment link first, then report link/email after payment clears), and the explicit behavior 'It never rewrites your words.' It could add details about auth or failure states, but for a paid validation service this is substantial transparency.

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 compact, front-loaded with the core purpose, and then gives essential pricing and workflow details in a few sentences. It earns its space, though phrases like 'nabanya, the Connection Validator' and 'transmit their feelings' add slight branding wordiness without harming clarity.

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 tool with no annotations and no output schema, the description covers important context: what it does, what it never does, the cost, and what the user will receive (payment link, report link, email). It lacks failure-payment or error-handling details, but for an agent deciding whether to invoke it and setting user expectations, it is largely complete.

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 description coverage is 100%, so the baseline is 3. The description adds pricing context for the 'product' parameter but does not add meaning beyond what the schema already provides (e.g., N4 uses source and leaves translation empty, N1 needs translation). The schema itself already explains these parameter relationships, so the description contributes little extra semantic value.

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 opens with a specific verb and resource: 'Validate a translation' and names the validator 'nabanya'. It clearly distinguishes the tool from a rewrite tool by stating 'It never rewrites your words,' which separates it from related functions like lookup_word or rewriting services.

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 communicates context such as being a PAID reading, the N1-N4 tier pricing, and the payment/report-link flow. However, it does not explicitly state when to choose this tool over alternatives (e.g., lookup_word) beyond implying 'validate a translation', and it provides no clear exclusions other than 'never rewrites your words,' which is more behavioral than a usage directive.

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