binet_formula

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 'calculate' but doesn't specify whether this is a read-only computation, what the output format might be, or any constraints like input validation or error handling. For a tool with three parameters and no annotations, 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 concise with a single sentence and includes domain/category tags, which are efficient. However, it could be more front-loaded with critical details; the tags might be better integrated or omitted if not essential. Overall, it's brief but under-specified rather than perfectly structured.

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 (involves mathematical sequences with three parameters), lack of annotations, 0% schema coverage, and no output schema, the description is incomplete. It fails to explain the tool's behavior, parameter meanings, or expected results, making it inadequate for an AI agent to use effectively without additional context.

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%, meaning none of the parameters (coeffs, initial, n) are documented in the schema. The description does not add any meaning to these parameters—it doesn't explain what coeffs represent (e.g., recurrence coefficients), what initial conditions are expected, or what n signifies (e.g., term index). This leaves the parameters largely unexplained.

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 states the tool calculates a Binet-style formula for sequences using characteristic roots, which gives a general purpose. However, it's vague about what specific sequence or recurrence it handles, and it doesn't differentiate from sibling tools like 'solve_linear_recurrence' or 'fibonacci', which might be related. The domain and category tags add some context but don't fully clarify the exact function.

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?

No guidance is provided on when to use this tool versus alternatives. The description lacks any mention of prerequisites, typical use cases, or comparisons to sibling tools such as 'solve_linear_recurrence' or 'fibonacci_sequence'. This leaves the agent without clear direction on appropriate contexts for invocation.

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