athena_gaussian_belief_register

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

With no annotations, the description carries full burden for behavioral disclosure. It indicates mutation ('Register/reset') but doesn't disclose side effects like whether it overwrites existing beliefs, the role of the 'replace' parameter, or any prerequisites. It doesn't mention what happens to prior data or any safety considerations.

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 sentences, front-loaded with the primary action, and includes a useful negative clarification. No wasted words, though it sacrifices depth for brevity.

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?

For a complex 7-parameter tool with no annotations and no output schema, the description is far too minimal. It doesn't explain return values, parameter semantics, or the difference between register and reset behavior. The tool's schema carries the entire burden, but schema descriptions are absent, leaving the agent under-informed.

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%, and the description provides no explanation of parameters. It doesn't mention context_key, parameters, mean, noise_variance, prior_variance, replace, or metadata. For a 7-parameter tool, this is a significant gap.

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+resource ('Register/reset a finite-dimensional Gaussian linear parameter belief in natural form') and explicitly excludes GP/neural/nonparametric posteriors, providing some differentiation from sibling tools like athena_gp_register. However, it doesn't name a specific alternative tool or clarify the exact lifecycle semantics (create vs reset), so it's clear but not fully distinguishing.

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 implies usage for registering or resetting Gaussian linear beliefs and warns against using for GP/neural/nonparametric posteriors, but it provides no explicit when-to-use or alternatives (e.g., athena_gaussian_belief_observe for observations, athena_gaussian_belief_state for reading). Guidance is minimal and mostly implied.

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