arithmetic_series

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 computation action but does not describe output format, error handling, performance characteristics, or limitations (e.g., numerical precision, input constraints). For a mathematical tool with no annotation coverage, 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 and front-loaded, consisting of a single sentence that directly states the tool's function. There is no wasted verbiage, and it efficiently communicates the core purpose. However, the inclusion of domain and category in parentheses, while informative, slightly disrupts flow but does not significantly detract from clarity.

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 complexity of a mathematical computation tool with 3 undocumented parameters, no annotations, and no output schema, the description is incomplete. It lacks details on parameter meanings, formula used, output format, and error conditions. The description does not provide enough context for reliable tool invocation without external knowledge.

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 3 parameters (a, d, n) with 0% description coverage, meaning no parameter details are documented in the schema. The description does not explain what these parameters represent (e.g., first term, common difference, number of terms), their units, or valid ranges. It fails to compensate for the schema's lack of documentation, leaving parameters semantically unclear.

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: 'Compute arithmetic series sum of terms in arithmetic progression.' It specifies the verb ('compute'), resource ('arithmetic series sum'), and domain/category context. However, it does not explicitly differentiate from sibling tools like 'arithmetic_sum' or 'arithmetic_sequence', which appear related, leaving room for ambiguity.

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 the domain ('numerical') and category ('series'), but does not specify prerequisites, exclusions, or compare it to sibling tools such as 'arithmetic_sum' or 'geometric_series'. This lack of contextual usage information limits its effectiveness for an AI agent.

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