convergent_properties

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

No annotations are provided, so the description must fully disclose behavioral traits. It fails to do so, offering no information on what the tool returns (e.g., convergence rates, error bounds), computational characteristics (e.g., complexity, limitations), or side effects. This leaves the agent with no understanding of the tool's behavior beyond its name.

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 and front-loaded, consisting of a single sentence that directly states the tool's purpose and domain. There is no wasted text, making it efficient for quick comprehension, though this brevity contributes to gaps in other dimensions.

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 complexity of mathematical analysis tools, no annotations, 0% schema description coverage, and no output schema, the description is severely incomplete. It does not explain what properties are analyzed, how results are returned, or any behavioral context, making it inadequate for effective tool use.

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 2 parameters with 0% description coverage, meaning the schema provides no parameter documentation. The description adds no semantic information about parameters, such as what 'cf' (continued fraction array) or 'target' (optional target value) represent, their expected formats, or usage examples, failing to compensate for the schema's lack of detail.

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: 'Analyze properties of continued fraction convergents.' It specifies the verb ('analyze'), resource ('properties of continued fraction convergents'), and provides domain/category context. However, it does not explicitly differentiate from sibling tools like 'cf_convergence_analysis' or 'convergents_sequence', which are closely related, preventing 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 lacks any mention of prerequisites, typical use cases, or comparisons to sibling tools such as 'cf_convergence_analysis' or 'convergents_sequence', leaving the agent without direction on tool selection.

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