compute_pi_nilakantha

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

No annotations are provided, so the description carries full burden. It states the computation method but lacks behavioral details such as precision limits, performance characteristics, error handling, or output format. The description is minimal and does not disclose important operational traits.

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. It efficiently states the method and formula in one sentence, with a brief domain/category note. There is no wasted verbiage, though it could be more informative.

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 no annotations, 0% schema coverage, and no output schema, the description is incomplete. It omits parameter details, behavioral context, and output information. For a computational tool with one parameter, this leaves significant gaps for an AI agent.

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%, and the description does not mention the 'terms' parameter at all. It fails to explain what 'terms' represents (e.g., number of series terms), its effect on accuracy, or valid ranges. The description adds no parameter semantics beyond the bare schema.

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 computes pi using the Nilakantha series, specifying the mathematical method and providing the series formula. It distinguishes itself from sibling tools like compute_pi_chudnovsky and compute_pi_leibniz by naming the specific algorithm. However, it does not explicitly contrast with these siblings in the description text.

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 like compute_pi_chudnovsky or compute_pi_leibniz. It mentions the domain and category, but offers no explicit usage context, prerequisites, or performance considerations.

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