glossary_lookup

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

With no annotations, the description carries the full burden of behavioral disclosure. It does disclose one important behavior: an empty term lists all entries. However, it does not mention whether lookups are exact-match, case-sensitive, or how unknown terms are handled. The output schema exists, which covers return shape, but behavioral edge cases are left unstated.

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 a single sentence that front-loads the tool's core purpose, provides illustrative examples, and packs the important empty-input behavior into a short follow-up clause. Every word earns its place and there is no redundant or filler content.

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 tool's low complexity (one optional parameter), an output schema, and a clear sibling set, the description is largely complete. It covers purpose and the one non-obvious behavior. The only minor gap is not specifying behavior for unknown or malformed terms, but this is a small omission for such a simple tool.

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 schema provides only a 'term' string with a default of empty, and the schema description coverage is 0%. The description compensates by explaining that the term is a methodology term, giving examples, and specifying that an empty value means 'list all'. This adds meaningful semantic context beyond the raw 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 the tool's function: it defines a methodology term, with concrete examples like exposure_score and return_period. While 'define' could arguably be clearer than 'look up the definition of', the standalone use and the empty-input behavior make the purpose unambiguous and distinct from the sibling tools.

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 context for when to use the tool: when a methodology term needs to be defined. It also specifies the empty-input behavior, which is an implicit but clear usage instruction. It does not explicitly exclude alternatives, but the tool's purpose is narrow enough that no strong exclusion is needed.

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