Estimate Uncertainty

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

Annotations already declare readOnlyHint=true. The description adds that it uses 'past feedback', implying a reliance on historical data, but does not disclose other behavioral traits like performance constraints or data volume sensitivity.

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?

A single sentence of 12 words conveys the essential purpose. It is front-loaded with the verb and object, with no superfluous information.

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 simple, read-only tool with one parameter and no output schema, the description is largely sufficient. It does not explain the output format, but the domain likely expects uncertainty values. Slight gap, but overall complete given the minimal context.

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 input schema covers 100% of parameters with a clear description ('Tags to analyze for uncertainty'). The tool description reiterates this meaning but adds no further semantics (e.g., tag format, constraints, or cardinality). Baseline score of 3 is appropriate.

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 ('estimate') and resource ('uncertainty for a set of tags'), and adds domain detail ('Bayesian', 'based on past feedback'). This clearly distinguishes it from sibling tools like 'feedback_stats' or 'describe_semantic_entity'.

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 provides no guidance on when to use this tool versus alternatives. No explicit conditions, prerequisites, or exclusions are mentioned, leaving the agent to infer usage from the name alone.

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