athena_gp_register

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

With no annotations provided, the description carries the burden of behavioral disclosure. It does disclose that the tool registers/resets model state, is exact and small-data oriented, and uses fixed RBF kernel hyperparameters. However, it does not mention whether resetting overwrites existing state, what the return value is, or any error conditions.

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 two short sentences with no filler. It front-loads the main action and resource, then adds a crucial limitation ('Model state only'). Every word earns its place.

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?

The tool has 7 parameters, no annotations, no output schema, and many closely related siblings. The description is far too sparse to provide adequate context for an agent to know when to use it, what parameters mean, what side effects occur, or what to expect back.

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 hints at 'fixed RBF kernel hyperparameters', which likely maps to length_scale, noise_variance, and signal_variance, but it does not explain the other parameters such as context_key, features, replace, or metadata. This is insufficient for a 7-parameter tool.

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 the specific verbs 'Register/reset' and identifies the resource as an 'exact small-data Gaussian-process regressor with fixed RBF kernel hyperparameters'. This clearly distinguishes it from prediction/observation siblings like athena_gp_predict or athena_gp_observe, though it does not name any sibling explicitly.

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 phrase 'Model state only' provides an implicit usage boundary, indicating this tool is not for prediction or hyperparameter fitting. However, it does not explicitly state when to use this tool versus alternatives, nor does it mention any exclusions or preconditions.

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