calc

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

No annotations are provided, so the description must fully disclose behavior. It lists pre-loaded functions but omits critical safety concerns (e.g., eval executing arbitrary code, sandboxing), side effects, error handling, or the exact return format beyond 'list'.

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 with two sentences, front-loading the core action and then listing capabilities. It is efficient but could be better structured with bullet points for readability.

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 nature (evaluating expressions) and the presence of an output schema, the description covers basic functionality but lacks details on error handling, security notes, and return schema specifics. It is adequate but not thorough.

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 adds meaning by stating that the parameter 'expressions' is an array of Python expressions and lists available functions. However, it does not specify per-expression evaluation or expected format, leaving gaps.

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 evaluates Python expressions and returns a list, listing pre-loaded functions like math and statistics. This distinguishes it from sibling tools like calc_convert (conversions) and calendar/now (time).

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 for mathematical computations but does not explicitly state when to use this tool versus siblings like calc_convert. It lacks explicit when-to-use and when-not-to-use guidance.

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