get_observation_frequency

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

With no annotations provided, the description carries the full burden of explaining behavioral traits. It does add a useful caveat that the result is a proxy based on historic observation data, not a true detection probability. However, it does not disclose what the output looks like (e.g., a numeric probability, a rating, a category), nor does it mention edge cases, data coverage limitations, or whether the estimate is deterministic. This is a significant gap for a tool that produces a computed value.

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, tightly written sentence that front-loads the core purpose before adding a methodological qualifier. Every clause earns its place: the first clause states the action and scope, the second explains the underlying mechanism. There is no redundancy, fluff, or repetitive phrasing.

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 that there is no output schema, the description should compensate by explaining what the tool returns, but it remains silent on the output format. For a tool that estimates a probability or frequency, the agent needs to know whether the result is a float between 0 and 1, a percentage, a category, or something else. Additionally, the absence of usage guidance and any mention of data limitations makes the description incomplete for an agent to reliably invoke and interpret the 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 input schema has 100% description coverage for all four parameters, so the baseline is 3. The description adds no extra meaning beyond the schema; it mentions 'location' and 'species' which map to regionCode and speciesCode, and 'specific week' which maps to month/day, but it does not clarify formats, units, or relationships beyond what the schema already states. Therefore, no additional value is provided, but the schema is sufficient.

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 what the tool does: estimates how likely a species is to be seen at a location during a specific week. The verb 'estimate' is specific, the resource is observation frequency, and it includes the keyscopes (location, species, week). This distinguishes it from sibling tools that return raw observations or lists (e.g., get_historic_observations, get_recent_observations) by focusing on likelihood rather than actual records.

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 provides no guidance on when to use this tool versus alternatives. It does not mention any exclusions or conditions, nor does it reference sibling tools. For example, it does not say 'use this when you need detection probability rather than raw observations' or 'for exact historical counts, use get_historic_observations instead.' The usage context is entirely implied by the tool's purpose, which is not sufficient.

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