get_values

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

Annotations already cover safety (readOnly, idempotent, non-destructive), so the description's job is to add operational context — and it does: unresolvable names are reported in 'errors' with candidates rather than failing, and the default tie-break behavior for shared Glottocodes is disclosed. It doesn't mention size/rate limits for the 50x50 cross product, which would have been the last useful detail.

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?

Front-loaded with what the call returns, then two tight paragraphs covering error semantics and disambiguation. Every sentence carries information; the parenthetical Basque example is a small luxury but aids comprehension.

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?

With an output schema present, return shape needn't be spelled out, and the description still flags the 'errors' field an agent must handle. Combined with the disambiguation rule, an agent can call this correctly; only the interaction with sibling query tools is left unaddressed.

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% with both parameters documented and exemplified, so the baseline is 3. The description adds real meaning on top: it explains the default resolution when multiple dataset entries share a Glottocode and shows the dataset-ref override syntax ('wals:bso'), which the schema only gestures at.

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: look up the value of each feature for each language, returning sources, comments and citations. That cross-product framing implicitly separates it from single-entity siblings like get_feature and language_profile, but no sibling is named explicitly, so differentiation is inferred rather than stated.

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?

Gives a concrete conditional rule for parameter choice: pass a dataset ref like 'wals:bso' when several entries share a Glottocode. However, it never says when to use this tool instead of crosstab, filter_languages or get_feature, so the when-to-use dimension is only implied.

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