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

Annotations only indicate non-read-only, non-destructive behavior. The description meaningfully adds the 50,000-character cap and credit-cost model (1 per 1,000 fast, 3 advanced), which are behavioral details an agent needs. It does not cover failure behavior or max_credits enforcement, but it adds solid value beyond annotations.

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?

Two tight sentences with no filler. The most decision-relevant facts—character limit and credit cost—are front-loaded, while tier pricing efficiently supports parameter choices.

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 5-parameter tool with no output schema, the description covers purpose and cost but omits expected response format, behavior when exceeding 50,000 characters or max_credits, and language code validation. It is adequate but has clear gaps.

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 coverage is 60%, and the schema already documents target/source language and tier. The description adds cost semantics for tier and character count, but leaves max_credits' role and text length validation implicit, so it only partially compensates for the coverage gap.

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 identifies the tool as translating text, with a specific resource ('text') and a concrete limit (50,000 characters). It stops short of explicitly distinguishing itself from sibling translate_document, although the word 'text' vs 'document' implies the boundary.

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 gives clear usage context: translate plain text up to 50k characters, with tier and cost implications. However, it provides no explicit when-to-use guidance versus translate_document or list_languages, and no exclusions or alternative routing.

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