wheel_sieve

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 uses 'wheel factorization for improved efficiency,' which hints at performance characteristics but lacks critical details: what the tool returns (e.g., list of primes), computational complexity, memory usage, or error handling. For a tool with no annotations, this is insufficient.

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 extremely concise—a single sentence plus domain/category tags—with zero wasted words. It is front-loaded with the core purpose and efficiently includes relevant metadata. This is a model of brevity without sacrificing essential clarity.

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 complexity (a specialized sieve algorithm), lack of annotations, 0% schema description coverage, and no output schema, the description is incomplete. It does not explain the tool's behavior, output format, or parameter meanings, leaving significant gaps for an AI agent to understand and invoke it correctly.

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 description coverage is 0%, meaning parameters 'limit' and 'wheel_primes' are undocumented in the schema. The description adds no information about these parameters—it does not explain what 'limit' represents (e.g., upper bound for primes) or what 'wheel_primes' should contain. This fails to compensate for the lack of schema documentation.

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: 'Wheel sieve using wheel factorization for improved efficiency.' It specifies the verb ('sieve'), resource (implied prime numbers), and domain context ('arithmetic, Category: sieve_algorithms'). However, it does not explicitly differentiate from sibling sieve tools like 'sieve_of_eratosthenes' or 'incremental_sieve', which prevents 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 mentions 'improved efficiency' but does not specify scenarios where wheel factorization is preferred over other sieve algorithms, nor does it reference sibling tools. This leaves the agent without practical usage context.

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