ford_circles

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 'generates' Ford circles, implying a computational or output-producing operation, but does not describe what the output looks like (e.g., graphical data, coordinates, or mathematical representations), any performance considerations, or error conditions. For a tool with no annotations and no output schema, this is a significant gap in transparency.

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. There is no wasted verbiage or redundancy, making it efficient for quick comprehension. The parenthetical domain and category add useful metadata without cluttering the core message.

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 generating Ford circles (a mathematical visualization tool), the description is insufficient. There are no annotations, no output schema, and minimal parameter guidance. The description does not explain what Ford circles are, what the output entails, or how to interpret results. For a tool that likely produces structured or graphical data, this lack of context makes it incomplete for effective 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 one parameter 'n' with 0% description coverage, and the description does not add any parameter-specific information. It mentions 'F_n' (Farey sequence of order n), which implicitly relates to the parameter, but does not explain the meaning of 'n' (e.g., that it's a positive integer defining the sequence order) or constraints. With low schema coverage, the description provides minimal compensation, resulting in a baseline score.

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: 'Generate Ford circles for fractions in Farey sequence F_n.' It specifies the verb ('Generate'), resource ('Ford circles'), and domain context ('fractions in Farey sequence F_n'), making the intent unambiguous. However, it does not explicitly differentiate from sibling tools like 'ford_circle_properties' or 'farey_sequence', which are related but distinct, 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 mentions the domain ('arithmetic') and category ('farey_sequences'), but does not specify prerequisites, typical use cases, or comparisons to siblings like 'ford_circle_properties' or 'farey_sequence'. This lack of explicit when-to-use or when-not-to-use information limits its utility for an AI agent.

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