athena_pheromone_adaptive_reinforce

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

With no annotations provided, the description carries the full burden. It only mentions the methodology (learned diffusion coefficients) and does not disclose side effects, state mutations, return values, permissions, or prerequisites, which is insufficient for a tool with this complexity.

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, front-loaded sentence with no wasted words. It efficiently communicates the core action and differentiator, though it is arguably too sparse for the tool's complexity.

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 7 parameters, nested objects, no output schema, no annotations, and 0% schema description coverage, a one-sentence description is drastically incomplete. An agent would lack essential context for correct invocation.

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%, so the description must compensate. It implicitly references 'coordinates' and 'multiscale' (source_scale) but leaves deposit_gain, evaporation_rate, observations, age, and actor completely unexplained, providing minimal help in understanding parameter purposes.

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 uses a specific verb ('reinforce') and resource ('declared multiscale coordinates'), and explicitly differentiates from siblings by noting 'learned diffusion coefficients rather than fixed scale attenuation.' This clearly distinguishes it from related pheromone reinforce 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 'rather than fixed scale attenuation' clause implies when to use this tool (when adaptive learning is desired), but it does not explicitly name alternatives, exclusions, or provide concrete when-to-use/when-not-to-use guidance.

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