athena_gaussian_belief_observe

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

With no annotations, the description must carry the full burden of disclosing behavioral traits. It reveals the operation is an update (a mutation), but does not state side effects, required prior state, return values, or error conditions. The phrase 'Bayesian linear update' gives some computational insight but lacks necessary behavioral context.

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 concise sentence with no filler, front-loading the core operation. It is efficient and every word earns its place. However, the extreme brevity contributes to ambiguity, preventing a perfect score.

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?

This is a complex 7-parameter tool with nested objects, no annotations, and no output schema, yet the description provides only a terse algorithmic statement. It fails to address prerequisites, parameter relationships, or expected outcomes, making it nearly impossible to invoke correctly. The description is drastically under-specified for the tool's complexity.

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 maps 'observed target' to the target parameter and 'feature vector' to features, but the remaining five parameters (context_key, actor, weight, evidence_ref, noise_variance) are left unexplained. The meaning of 'complete feature vector' for the nested object is also ambiguous.

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 states a specific operation: 'Bayesian linear update' from an observed target and feature vector. It references the Gaussian belief resource through the tool name and hints at the data contract via 'complete feature vector', which helps distinguish it from sibling observe tools. However, it doesn't explicitly name the belief object being updated, leaving a slight ambiguity.

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?

No guidance is provided on when to use this tool versus alternatives such as athena_bayes_observe or athena_gp_observe. There is no mention of prerequisites (e.g., whether a belief must be registered) or the intended scenario. The use case is only implied by the wording, not explicitly framed.

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