integral_trapecio

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It adds meaningful context beyond the schema by explaining the role of epsilon for the truncation error bound, specifying the return structure (integral, error estimates, and table), and noting the constraint b > a. It does not detail edge cases (e.g., invalid n, non-integer n, or malformed func_str), but the core behavior is transparent.

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 extremely concise and well-structured, using a standard Args/Returns docstring format. Every sentence adds value, and the key purpose is front-loaded with a formula notation. There is no redundancy or 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?

The tool has moderate complexity (6 params, no output schema, no annotations). The description covers the method, parameter roles, return contents, and a key constraint (b > a). Given the context, it is fairly complete; minor gaps like validation of n or expected math expression syntax do not undermine usability significantly.

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 offers zero description coverage (only titles like 'A', 'B', 'N'), so the description must compensate. It defines all six parameters: func_str as a math expression in x, a and b as integration limits, n as the number of subintervals, epsilon as an optional point for error bound, and precision as rounding digits. This adds essential semantic meaning beyond the schema's bare property names, though it could include syntax examples for func_str.

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 'Approximate ∫_a^b f(x) dx with the composite trapezoidal rule' — a specific verb and resource that immediately distinguishes it from sibling integration tools like Simpson's rule or rectangular methods. The method is explicitly named, leaving no ambiguity about what the tool does.

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 explicit guidance on when to use trapezoidal integration versus other available methods (e.g., integral_simpson13, integral_simpson38, integral_rectangulo). It only implies usage by stating the method, leaving the agent to infer when this is appropriate relative to alternatives. There are no exclusion criteria or comparisons.

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