compute_e_series

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

With no annotations provided, the description carries full burden but only states the mathematical formula. It doesn't disclose behavioral traits like precision limitations, performance characteristics (e.g., time/complexity for many terms), error handling for invalid inputs, or output format. The description is purely declarative without operational context.

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 in the first sentence. The additional domain/category information is brief and relevant. There's no wasted verbiage, though it could be more informative without sacrificing brevity.

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, no output schema, and 0% schema description coverage, the description is incomplete. It doesn't explain what the tool returns (e.g., a numeric approximation, error estimate), how accuracy scales with terms, or any limitations. For a computational tool, this leaves significant gaps for an AI agent to understand its behavior fully.

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 description doesn't mention the 'terms' parameter at all, and schema description coverage is 0%, so it adds no parameter semantics beyond the bare schema. However, with only one parameter and a straightforward tool, the baseline is 3 as the schema alone might be minimally sufficient, though the description fails to compensate for the lack of schema descriptions.

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 e using the infinite series formula, specifying the mathematical method and domain/category. It distinguishes itself from sibling tools like 'e' or 'compute_e_limit' by explicitly mentioning the series approach, though it doesn't explicitly contrast with those alternatives.

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 explicit guidance is provided on when to use this tool versus alternatives like 'compute_e_limit' or 'e'. The description mentions the domain and category but gives no practical context about accuracy trade-offs, computational cost, or typical use cases for series approximation versus other methods.

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