athena_gp_observe

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the action (append) but omits important behavioral details: whether it mutates persistent state, what 'bounded' means (e.g., what happens when the bound is reached), whether it is idempotent, what error conditions apply, and what, if anything, is returned. This is a significant gap for a mutation tool.

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 that immediately states the action and resource. Every word contributes, with no redundancy, filler, or irrelevant detail. It is appropriately concise for the information it conveys.

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 the tool's complexity (5 parameters, nested object, no annotations, no output schema, 0% schema coverage), the one-sentence description is highly incomplete. It lacks context about parameter formats, dataset bounds, return values, and prerequisites, preventing an agent from confidently invoking the tool without further information.

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 only clarifies the 'target' parameter via 'actual observed target'. The semantics of 'context_key', 'features' (a nested object), 'actor', and 'evidence_ref' are left unexplained, forcing the agent to guess their roles. This is insufficient for five parameters.

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 verb 'Append' and identifies a unique resource: the 'bounded fixed-kernel GP dataset'. This clearly distinguishes it from sibling GP tools like athena_gp_predict (prediction) and athena_gp_hyperfit (hyperparameter fitting), and from generic observe tools. The phrase 'actual observed target' adds precision about the kind of data being appended.

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 gives no guidance on when to use this tool versus alternatives. It does not state prerequisites (e.g., that a GP dataset must already exist), when not to use it, or how it differs from related tools like athena_transition_observe or athena_gp_register. The usage context is only implied by the action of appending.

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