triangular_numbers

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 tool generates numbers and gives the formula, but does not disclose behavioral traits such as input validation (e.g., handling of negative n), output format (e.g., list, array), performance characteristics, or error handling. The description is minimal and misses key operational details.

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, stating the core purpose in the first sentence. The formula and domain/category tags add value without redundancy. However, it could be slightly more structured by explicitly listing the parameter, but overall it avoids unnecessary verbosity.

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 explains what the tool does mathematically but lacks details on parameter usage, output format, error conditions, and behavioral context. For a tool with one parameter but no structured documentation, 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%, so the description must compensate. It mentions 'n' implicitly in the formula and context, but does not explicitly define the parameter, its constraints (e.g., positive integer), or semantics beyond the mathematical relation. This leaves gaps in understanding how to use the parameter effectively.

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 with a specific verb ('Generate') and resource ('first n triangular numbers'), provides the mathematical formula for clarity, and distinguishes it from sibling tools like 'triangular_number' (which likely checks if a number is triangular) and 'triangular_sequence' (which might generate sequences differently). The domain/category tags add helpful context.

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 implies usage by specifying what the tool generates, but does not explicitly state when to use it versus alternatives like 'triangular_sequence' or 'triangular_number'. It provides the mathematical context (arithmetic/general) which helps infer applicability, but lacks direct guidance on tool selection among siblings.

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