translate_text

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

Discloses model (Qwen3-32B), auto-detection of source language, privacy (no data stored), and pricing (1 sat per 1000 chars, min 1 sat). Without annotations, description carries full burden; these details are sufficient. Could mention if synchronous or result format, but still strong.

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 front-loaded with purpose and key details, but it redundantly lists all 119 languages already present in schema enums. This makes it verbose. Overall structure is logical otherwise.

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?

Covers primary aspects: function, payment flow, parameters, supported languages, privacy, pricing. Missing output description and potential limitations (e.g., max text length). No output schema exists, so description should mention return format.

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%, but description adds value: explains source language is optional (auto-detect), paymentId from create_payment, modelId optional. Also notes that language params accept English names or ISO codes, which is not in 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?

The description clearly states 'Translate text across 119 languages' with high accuracy, specifying the model and auto-detection. It distinguishes from siblings like transcribe_translate (audio translation) and generate_text (text generation) by focusing solely on text-to-text translation.

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?

Provides guidance on when to use: privacy-preserving, pay-per-request with Bitcoin Lightning, no signup. Mentions prerequisite step (create_payment). However, does not explicitly compare to alternative translation tools or state when not to use it.

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