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

Annotations already establish read-only, idempotent, non-destructive behavior, so the bar is lower. The description adds real context beyond them: results are bounded, limited to 'reviewed, rights-eligible' records, carry dataset revision and limitations, and San Angelo records are separately labeled rather than counted as Central Texas member data. What it does not state is the bound itself or any pagination behavior.

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 first sentence is well front-loaded and the return-value inventory is dense but efficient. The final sentence about San Angelo labeling is a narrow edge case that consumes a third of the text, and the middle sentence reads like a field dump rather than guidance.

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?

An output schema exists, so the description's long enumeration of returned fields is partly redundant, while the genuinely useful information — how to use the 8 undocumented filter parameters — is absent. Adequate for a simple read call, but not complete for a tool with this many enums and patterns.

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 0% across 8 parameters, so the description must carry the burden and largely does not. It gestures at species ('goat, sheep, or cattle') and 'weight bands,' but gives no meaning for class, purpose, location syntax, weight_lb units, breed_type, or the limit cap. Most filter parameters remain semantically opaque.

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 gives a specific verb and resource: 'Returns a bounded list of ... published goat, sheep, or cattle observations.' That is enough to distinguish it from get_methodology and get_data_coverage, though it never names a sibling tool outright. Clear purpose, but sibling differentiation is left implicit.

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?

There is no when-to-use guidance, no statement of prerequisites, and no mention of alternatives such as get_market_report or get_usda_cattle_benchmark. The agent must infer usage purely from the tool name and the surrounding tool set.

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