horner_method

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 mentions 'efficient computation', which hints at performance characteristics, but fails to describe critical behavioral traits: what the tool returns (e.g., a single numeric result), error handling (e.g., for invalid inputs), computational complexity, or any side effects. For a tool with no annotations, 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 concise and front-loaded with the core purpose. The single sentence 'Evaluate polynomial using Horner's method (efficient computation)' efficiently states the main function, and the parenthetical domain/category adds minimal but relevant context without redundancy. However, the lack of parameter explanations or usage guidelines means it may be overly terse for full understanding.

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 (polynomial evaluation with a specific algorithm), no annotations, 0% schema description coverage, and no output schema, the description is incomplete. It fails to explain parameters, return values, error conditions, or algorithmic details. While it names the method and hints at efficiency, it doesn't provide enough context for reliable use without external knowledge of Horner's method.

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 does not explain the parameters at all—no mention of what 'coefficients' represents (e.g., polynomial coefficients in descending order) or what 'x' is (e.g., the evaluation point). The description adds no semantic information beyond the bare schema, leaving parameters undocumented.

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: 'Evaluate polynomial using Horner's method (efficient computation)'. It specifies the verb ('evaluate'), resource ('polynomial'), and method ('Horner's method'), and distinguishes it from generic polynomial evaluation tools by mentioning efficiency. However, it doesn't explicitly differentiate from potential sibling polynomial evaluation tools (though none are listed among siblings).

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 minimal usage guidance. It mentions the domain ('numerical') and category ('series'), which gives some context, but offers no explicit guidance on when to use this tool versus alternatives (e.g., direct polynomial evaluation methods). There's no mention of prerequisites, performance trade-offs, or specific scenarios where Horner's method is preferred.

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