deepl_list_languages

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

Annotations already declare a safe, read-only, idempotent, open-world lookup. The description adds that the result is the set of supported translation languages, but does not mention auth requirements or the source/target distinction beyond what the schema provides.

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 redundant wording. It communicates the tool's scope efficiently.

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 simple read-only lookup with full schema coverage and annotations, the description is mostly sufficient. One minor gap is that it does not clarify the source/target option in prose, though the schema already documents it.

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%, including the source/target enum and the required auth_key, so baseline is 3. The description does not add meaning beyond the schema, such as how source versus target language lists differ.

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: lists languages supported by DeepL for translation. The purpose is clear, but it does not differentiate this tool from translation-related siblings such as deepl_translate_text or deepl_get_usage.

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?

Usage is implied by the name and description—this is the tool for discovering supported languages—but there is no explicit guidance on when to choose it over related DeepL tools or how the type parameter should influence use.

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