athena_joint_gaussian_update

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

With no annotations, the description carries the transparency burden. It discloses that 'Unknown coefficient coordinates and non-finite state fail closed,' providing edge-case behavior, and states the operation is exact and finite-dimensional. It does not, however, describe return format or side effects, so a small gap remains.

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 extremely concise, with three short sentences that front-load the core purpose and add edge-case and scope information. Every sentence carries meaningful content with no redundancy.

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 is mathematically complex and lacks both annotations and output schema, yet the description omits the return value and the precise structure of the observation parameter. It covers purpose, edge cases, and scope, but these gaps mean it is only partially complete.

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 provides no parameter descriptions (0% coverage), so the description must compensate. It adds meaning by explaining the operation's mathematical context and the observation's linear-Gaussian nature, but it does not detail the structure of the 'observation' object or the individual roles of variables, mean, and covariance beyond their names.

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 clearly states the tool performs an exact finite-dimensional multivariate-Gaussian posterior update for a single linear-Gaussian observation. It differentiates from general continuous Bayes by explicitly saying 'Model state only; not general continuous Bayes,' which distinguishes it from broader Bayesian tools in the sibling list.

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 specifies it is for 'one declared linear-Gaussian observation' and excludes 'general continuous Bayes,' giving clear when-to-use and when-not-to-use context. However, it does not explicitly name alternative tools or provide detailed criteria beyond this mathematical scope.

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