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

With no annotations, the description carries the full behavioral burden. It helpfully discloses that the AI engine key is never returned, which is real information beyond structured fields, but it says nothing about whether this is a network call, its cost/latency, caching, or the shape of the result.

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?

A single front-loaded sentence with no filler; what the tool reports and the key-redaction caveat both land immediately.

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 trivial read-only, zero-parameter listing tool with no output schema, the description covers what is returned well enough for correct invocation. A brief note on result format or that it requires a configured backend would close the remaining gap.

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 tool takes zero parameters, so there is nothing for the description to disambiguate; the baseline for a parameterless tool applies.

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?

States a specific verb ('report') and two concrete resources (available translation engines, AI engine configuration), so the agent knows exactly what comes back. It is inherently distinct from the translate_* siblings, though it never names them explicitly to reinforce the contrast.

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?

There is no statement of when to call this versus translate_text/translate_batch, nor any prerequisite or ordering guidance. The discovery use-case (check engines before translating) is inferable only from the purpose, not stated.

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 it does disclose a meaningful behavioral trait: the engine-dependent call pattern ('one batched Google request, or one AI call per text'), which has latency/cost implications. However it says nothing about per-text failure handling, partial results, result ordering, or BYOK/auth requirements for the ai engine.

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?

A single sentence with the core batch capability front-loaded and a compact parenthetical for the engine nuance. Nothing is redundant and the size matches the tool's simplicity.

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 4-parameter batch tool with no annotations, no output schema, and a 50-item input, the description is adequate but thin: it omits how partial failures or ordering are handled and what auth the ai engine needs. The description need not cover return values, but batch failure semantics are a real gap.

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 schema already documents texts, engine, source_lang, and target_lang; baseline of 3 applies. The description reinforces the 50-item cap and hints that the engine choice changes call behavior, but adds no format or default details beyond the schema.

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?

States a specific verb and resource ('Translate ... texts') plus a concrete capacity ceiling ('up to 50 texts in one call'), which distinguishes it from the singular translate_text sibling. It stops short of naming the sibling or contrasting directly against it, so differentiation is inferable rather than explicit.

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 'up to 50 texts in one call' phrasing implies this is the batch alternative to translate_text, giving usable but implicit guidance. No explicit when-to-use, when-not-to-use, or reference to translate_text / list_engines is provided, so the agent must infer the routing.

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?

No annotations are provided, so the description carries the full disclosure burden, yet it only restates the engine choice already documented in the schema enum ("google" free vs "ai" BYOK). It says nothing about rate limits, auth/BYOK setup requirements, defaults applied, or failure behavior for a tool that depends on external providers.

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?

A single front-loaded sentence with the action first and the qualifier (engine/provider) after. No filler or redundancy.

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?

With no annotations and no output schema, the description should carry more weight than it does. The schema covers the parameters well, but the description is silent on what is returned and on the BYOK configuration dependency implied by "the configured AI provider".

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 schema already documents text, engine, source_lang, and target_lang with defaults and examples. The description confirms the single-text scope but adds no format, language-code, or engine-selection detail beyond the schema, making the baseline 3 appropriate.

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?

States a specific verb+resource ("Translate one piece of text") and names the backends used, scoping it to a single item which implicitly sets it apart from the translate_batch sibling. It stops short of explicitly naming that sibling, so the differentiation must be inferred from the word "one".

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 phrase "one piece of text" implies the single-item case versus a batch, but no explicit when-to-use, when-not-to-use, or alternative routing (e.g., translate_batch or list_engines) is stated. Usage is only implied.

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