termdat_lookup_term

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

Annotations indicate readOnly, idempotent, non-destructive. The description adds transparency about data source subset (77,692 of ~400,000 entries) and that 'rm' is practically empty. This goes beyond annotations by disclosing data limitations that affect results. No contradiction with 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?

The description is concise and structured with clear tags: use_case, important_notes, example. Each section adds essential information without redundancy. Front-loaded with main action. No wasted words.

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?

Given the output schema exists, the description need not detail return values. It covers data source limitations, language coverage, and provides an example. It could mention pagination or result format but is sufficiently complete for typical use.

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 input schema has detailed descriptions for all parameters (term, limit, target_languages). The tool description adds value with an example usage and context but doesn't significantly enhance clarity beyond what schema already provides. Schema coverage is effectively high, so baseline 3 is 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?

The description clearly states the verb ('nachschlagen'), resource (TERMDAT), and what it returns (equivalents in other languages with definition). It distinguishes from sibling tools like termdat_get_concept by focusing on term lookup for translations.

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 use_case tag explicitly provides a typical query ('Wie heisst dieser Begriff auf Französisch/Italienisch?') and context (amtliche Terminologie, mehrsprachige Stellungnahmen). Important_notes clarify the data source limitations and that a negative result doesn't mean absence. This gives clear when-to-use guidance and expectations.

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