is_happy_number

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool's function but doesn't describe what a 'happy number' is, how the check is performed (e.g., iterative process), what inputs are valid (e.g., positive integers only?), or what the output looks like (e.g., boolean). For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior.

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 very concise—a single sentence plus domain/category annotations. It's front-loaded with the core purpose and avoids unnecessary words. However, the brevity comes at the cost of completeness, as noted in other dimensions, but as pure conciseness, it's efficient.

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?

Given the tool's moderate complexity (involves a mathematical concept), no annotations, no output schema, and low schema coverage, the description is incomplete. It doesn't define 'happy number', explain the algorithm, specify input/output formats, or provide examples. For a tool that requires understanding a specific mathematical property, this is inadequate.

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 one parameter 'n' with 0% description coverage (no schema description). The tool description doesn't mention parameters at all, failing to compensate for the lack of schema documentation. It doesn't explain what 'n' represents (e.g., integer to test) or any constraints (e.g., must be positive). With low schema coverage, the description adds no parameter semantics.

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's purpose: 'Check if a number is a happy number.' It includes a specific verb ('Check') and resource ('a number'), and the domain/category annotation ('Domain: arithmetic, Category: iterative_sequences') provides additional context. However, it doesn't explicitly differentiate from its sibling tool 'happy_numbers' (which likely lists happy numbers rather than testing a single number), so it falls short of a perfect score.

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. It doesn't mention sibling tools like 'happy_numbers' or other number-checking tools (e.g., 'is_prime', 'is_perfect_square'), nor does it specify prerequisites or typical use cases. The domain/category hints at context but doesn't constitute explicit usage instructions.

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