check_numeric_claims

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

With no annotations, the description carries the full burden. It discloses specific detection algorithms (round-number ratio, MAD-based outlier detection), conditions for compound growth detection, and optional iteration-based escalation/stall detection. It does not describe return format or error behavior, but for a read-only analysis tool this is fairly transparent.

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 structured with a summary and numbered layers, but it is repetitive (mentions optional context twice) and includes a misleading example that takes up space. The required input format is unnecessary and incorrect. Several sentences fail to earn their place, making it less concise than it appears.

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 analysis layers but fails to map parameters accurately to the schema, omits the 'description' field, and provides no return/output information. Given the tool has a nested object parameter and no output schema, the description is incomplete and partially misleading, leaving critical usage details unresolved.

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 description directly contradicts the input schema: it labels 'context' as a string, but the schema defines it as an object with nested properties. It also completely omits the 'description' parameter, which the schema indicates enables compound growth detection. The example JSON uses 'context' as a string, misleading agents into constructing invalid requests. This actively harms parameter understanding despite high schema coverage.

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 multi-signal numeric analysis with specific layers (fabrication detection, outlier detection, arithmetic verification), giving a specific verb+resource. It distinguishes itself from sibling verify_arithmetic by adding fabrication and outlier detection, though it doesn't explicitly name that alternative.

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 guidance is provided on when to use this tool vs alternatives like verify_arithmetic or validate_confidence. The description focuses on input format and analysis layers, but does not explain selection criteria, prerequisites, or exclusions. Usage is only implied by the tool name and analysis scope.

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