compare_values

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

No annotations are present, so the description carries the full behavioral disclosure burden. It states that deviations are qualified and that a ComparisonResult is returned, but it does not explain how qualification is determined, what deviation metrics are produced, or any edge-case behavior such as missing values or mismatched periods.

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 compact and front-loaded with the core purpose, followed by an Args section and a Returns line. Each section adds value without excessive verbosity, though the Args definitions could have been tighter by combining parameter semantics with format details.

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 description covers all four parameters and notes the return type, and an output schema exists. Still, it lacks context about period formatting, required unit alignment, qualification thresholds, and how the tool behaves when inputs are invalid or incomplete.

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 has 0% description coverage, but the Args block provides meaningful semantic definitions for all four parameters, distinguishing claimed vs. actual values and periods. This compensates well, though it omits expected formats or units for period strings and numeric 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 states a specific operation: compare a claimed value to actual data and qualify the deviation. This clearly identifies the tool's function and distinguishes it from the data-retrieval siblings, though it does not explicitly differentiate it from analysis tools like analyze_cherry_picking.

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?

No usage guidance is provided. The description does not say when to use this tool versus alternatives, how it fits into a fact-checking workflow, or what prerequisites or exclusions apply. The purpose implies a use case, but no explicit when/when-not guidance is given.

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