athena_bayes_observe

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

With no annotations, the description must convey behavioral traits. It discloses that the tool processes 'one explicit observed reward' and 'retains pre-update prediction for calibration', which are meaningful. However, it omits details about side effects, required permissions, state mutability, or error conditions, leaving a moderate transparency gap.

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, tightly worded sentence with no filler. It front-loads the primary action and includes a secondary behavioral note. Its brevity is admirable, though it sacrifices some completeness for conciseness.

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 tool with 9 parameters, no output schema, and no annotations, this description is far too sparse. It does not clarify return values, parameter formatting, error semantics, or prerequisite conditions. The agent would struggle to invoke this tool correctly for complex use cases.

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 is the only source of parameter meaning. It explicitly references only 'reward' and vaguely implies 'features' via 'contextual state'. The other seven parameters (ridge, scope, actor, regime, arm_id, weight, target_coverage) are unexplained, leaving the agent without sufficient semantic guidance for parameter values.

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 identifies the core action: 'Update full-covariance Bayesian contextual state' from 'one explicit observed reward'. This specifies both the verb ('Update') and the precise resource ('full-covariance Bayesian contextual state'), which distinguishes it from sibling observe/predict tools. The addition of 'retains pre-update prediction for calibration' further clarifies its unique role.

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 context: after a single reward observation, update the Bayesian state, and the retained prediction is useful for calibration. However, it does not explicitly state when to use this tool versus alternatives like athena_bandit_observe or athena_elder_observe, nor does it provide any exclusions or prerequisites.

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