farey_sum

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 'applications to modular arithmetic', which adds some context, but fails to describe critical behaviors such as input constraints (e.g., denominators must be positive, fractions must be in lowest terms), error handling, or output format. For a mathematical tool with 4 parameters, this is a significant gap.

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 that efficiently conveys the core purpose and domain. It is front-loaded with the main action and avoids unnecessary details. Every word earns its place, making it easy to parse quickly.

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 (4 parameters with 0% schema coverage, no annotations, no output schema), the description is incomplete. It does not compensate for the lack of structured data by explaining parameter meanings, behavioral traits, or output expectations. For a tool performing a specialized mathematical operation, this leaves too many gaps for reliable 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?

Schema description coverage is 0%, so the schema provides no parameter descriptions. The description does not explain what p1, q1, p2, q2 represent (e.g., numerators and denominators of two fractions), their constraints (e.g., integers, non-zero denominators), or their relationship. This leaves parameters undocumented, making it difficult for an agent to use the tool correctly.

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: 'Calculate Farey sum of two fractions with applications to modular arithmetic.' It specifies the verb ('Calculate'), resource ('Farey sum of two fractions'), and provides domain context. However, it does not explicitly differentiate from sibling tools like 'farey_mediant_path' or 'farey_neighbors' which might involve similar concepts, so it's not a perfect 5.

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 'applications to modular arithmetic' but does not specify scenarios, prerequisites, or compare it to related tools like 'farey_sequence' or 'mediant'. This lack of explicit usage context limits its helpfulness 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.