limit_expression

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

No annotations are provided, so the description carries the burden of behavioral disclosure. It adds context about the default point (oo) and provides a working example with a return value, but doesn't disclose edge cases (e.g., divergent limits, multiple variables, error handling for undefined limits). Acceptable but not rich.

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?

Two sentences: the first defines the operation precisely, and the second provides a concrete example with a humorous but relevant closing phrase. Every word earns its place; no fluff.

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?

For a simple 3-parameter tool with no output schema, the description is reasonably complete: it explains inputs via example, default behavior (limit to infinity), and outputs (a value). It doesn't mention failure modes or handling of symbolic constants, but given the low complexity, this is acceptable.

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%, but the description's example ('limit_expression("n*log(n)/n**2", "n")') clarifies the positional order (expr, var) and the default point. It adds meaning by showing the expression and variable usage, though 'point' remains implicit as the default oo. Good compensation for the schema gap.

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 the asymptotic limit of an expression as a variable approaches a point (default infinity), with a concrete example. It distinguishes itself from siblings like solve_expression and simplify_expression, though it doesn't explicitly name them as 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?

The description implies usage via the example ('settles complexity arguments faster than arguing') but doesn't explicitly state when to use this over alternatives like analyze_complexity or compare_execution. No exclusions or prerequisites are mentioned, making the guidance adequate but implicit.

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