athena_gaussian_belief_state

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

With no annotations, the description carries the burden of disclosure. The verb 'Read' indicates a non-mutating operation, and the description specifies the returned data (mean/covariance and observation count). However, it does not disclose error behavior (e.g., missing context_key), permissions, or whether the observation count is included in the covariance structure. It provides minimal but non-contradictory transparency.

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, compact sentence that leads with the verb and object. Every word contributes to the core meaning. It is appropriately sized for a simple read operation without unnecessary elaboration.

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 one parameter, no output schema, and no annotations, so the description must cover both input semantics and return structure. It partially covers the return (mean/covariance/observation count) but omits the meaning of context_key and the exact format of the output. Given the large sibling toolset and the need for precise invocation, this is incomplete.

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 schema has one required parameter, context_key, with no description (0% coverage). The description fails to explain what context_key represents or how to obtain it. An agent cannot determine what value to pass, making the tool difficult to invoke correctly. The description adds zero semantic value for the parameter.

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 ('Read') and resource ('Gaussian linear belief'), clearly stating what is read: mean/covariance and observation count. This distinguishes it from sibling tools like 'athena_gaussian_belief_observe' (which likely updates) and 'athena_gaussian_belief_register' (which creates), as well as the more generic 'athena_belief_state'.

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 usage guidance is provided. The description does not mention when to use this tool versus alternatives, nor any exclusions or prerequisites. It simply states what the tool does, leaving the agent to infer context from the name and siblings.

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