arithmetic_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 states the calculation method but does not cover critical aspects such as error handling (e.g., for invalid inputs like negative 'num_terms'), performance characteristics, or output format. For a tool with no annotations, this leaves significant gaps in understanding its behavior.

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 clause. The formula and domain/category tags are relevant but could be slightly more integrated. There is no redundant information, and every sentence contributes to understanding the tool's function, though minor improvements in flow could enhance structure.

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 (mathematical calculation with three parameters), no annotations, and no output schema, the description is incomplete. It lacks details on error conditions, output format, and practical usage examples. While the formula provides some context, it does not fully compensate for the absence of structured metadata, leaving the agent with insufficient information for reliable invocation.

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 input schema has 0% description coverage, so the description must compensate. It implicitly defines the parameters through the formula 'n/2 * (2a + (n-1)d)', where 'a' corresponds to 'first_term', 'd' to 'common_diff', and 'n' to 'num_terms'. This adds meaningful context beyond the schema's type definitions. However, it does not explicitly map formula variables to parameter names or explain constraints (e.g., 'num_terms' must be positive), limiting the score to baseline adequacy.

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 the sum of an arithmetic sequence' using a specific formula. It specifies the verb ('calculate'), resource ('sum of an arithmetic sequence'), and provides the formula for clarity. However, it does not explicitly distinguish this tool from its sibling 'arithmetic_series', which likely serves a similar purpose, preventing a perfect score.

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 a domain ('arithmetic') and category ('general'), but these are too vague to inform usage decisions. There is no mention of prerequisites, limitations, or comparisons to sibling tools like 'arithmetic_series', leaving the agent without practical usage instructions.

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