compute_golden_ratio_continued_fraction

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

With no annotations provided, the description carries full burden for behavioral disclosure. It states what the tool computes but doesn't describe important behavioral aspects: it doesn't mention whether this is a pure computation (no side effects), what happens with different depth values (convergence behavior, overflow risks), what format the result returns, or any precision/accuracy characteristics. The description is mathematically correct but operationally incomplete.

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 appropriately concise - a single sentence with the mathematical formula and domain/category tags. It's front-loaded with the core purpose. The domain/category information adds context without being verbose. However, the mathematical notation could be clarified for non-expert users.

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 a mathematical computation tool with 1 parameter (0% schema coverage), no annotations, and no output schema, the description is insufficient. It doesn't explain the parameter's meaning, the computation's behavior with different inputs, what the output represents (approximation accuracy, format), or how this method compares to alternatives. For a tool that computes a mathematical constant with configurable precision, more operational context is needed.

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, so the description must compensate. It mentions 'depth' only implicitly through the continued fraction notation '...' but doesn't explain what the depth parameter means, what valid ranges are, or how it affects the computation. For a single parameter tool with no schema documentation, this represents a significant gap in parameter understanding.

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: 'Compute golden ratio using continued fraction' with a specific mathematical formula provided. It distinguishes this method from alternatives like 'compute_golden_ratio_fibonacci' (a sibling tool) by specifying the continued fraction approach. However, it doesn't explicitly contrast with other golden ratio computation methods beyond the formula.

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. While it mentions the continued fraction method, it doesn't explain when this approach is preferable to other golden ratio computation methods (like the Fibonacci-based approach available as a sibling tool) or what trade-offs exist. There's no mention of accuracy considerations, computational complexity, or appropriate depth values.

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