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

Annotations already establish readonly, non-destructive, and idempotent behavior. The description adds meaningful behavioral nuance by stating that the tool returns a single missing observation and does not promote hypotheses to facts, which is useful beyond the annotations.

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 that front-loads the action and result. Every phrase earns its place, including the clarifying constraint about not promoting hypotheses.

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 output schema and annotations cover return and safety semantics, and the core behavior is clear. However, the required input parameter is entirely undocumented and usage guidance is only implicit, leaving a moderate gap for correct invocation.

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, 'description', with 0% schema description coverage. The tool description never explains what should go into this parameter, leaving the agent to guess whether it is a problem statement, a query, or contextual text.

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 names a specific verb ('Returns') and resource ('missing observation'), and adds a distinguishing constraint ('without promoting hypotheses to facts'). This clearly separates it from hypothesis-generation and verification siblings.

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 intended use is implied: call it when you need the next highest-value missing observation. However, it does not explicitly state when not to use it or point to alternatives, so the agent must infer the decision context.

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