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

Annotations only declare readOnlyHint and openWorldHint; the description adds substantial context beyond them: 3 free calls/day across all tools, then $0.005 USDC via x402 or card credits with the payment passed in-band at params._meta["x402/payment"]. It also discloses the underlying model (small LLM on Workers AI), that source language is auto-detected, and that both GET query and POST JSON are accepted.

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?

Compact and information-dense with no filler sentences, but the billing/rate-limit boilerplate is front-loaded ahead of what the tool actually does, delaying the agent's ability to identify the operation. The purpose should lead, with pricing after.

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?

An output schema exists so return values need no explanation, and the description covers everything else an agent needs: input size limit, valid target codes, auto-detection, transport options, cost model, and payment mechanism. Nothing material is missing for correct invocation.

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 100%, so the baseline is 3, but the description adds real value: it enumerates all 28 valid target language codes even though the schema declares no enums, and it clarifies that source is auto-detected, explaining why 'from' is optional. The 3,000-character cap is repeated from the schema but reinforced as a hard constraint.

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 ('Text translation: translate up to 3,000 characters') with hard limits and the exact language set. It is immediately distinguishable from siblings like classify, summarize, and entity_extract, which are different NLP operations.

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 'When to use' sentence only restates the purpose ('Use translate for short texts into 28 languages') without naming a condition that selects it over alternatives or an exclusion. The word 'short' plus the 3,000-char cap implies the boundary, but no alternative (e.g., page_rag or summarize for long documents) is named.

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