mc_valor_promedio_1d

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

With no annotations, the description carries the full burden. It discloses key behavioral traits: the method is stochastic (mean-value MC) with an optional seed for reproducibility, and it returns a confidence interval and sample statistics. This gives the agent a clear sense of randomness and output structure. It does not mention convergence rate or potential accuracy pitfalls, but the core traits are covered.

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 compact and well-structured. It leads with a one-sentence purpose statement, followed by a tight Args list and a Returns line. Each item is meaningful and adds value without unnecessary elaboration.

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 stochastic numerical integration tool with 7 parameters and no output schema, the description provides sufficient information to invoke it correctly: parameters, constraints, and return contents. It does not explain when to use this MC method over alternatives or discuss limitations, but the core invocation context is complete.

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 schema has 0% description coverage, but the Args block in the description defines every parameter: func_str as math expression, a/b as limits with b>a, N as number of samples, seed for reproducibility, precision for rounding, and nivel_confianza with range (0,1). This adds essential meaning beyond the raw schema.

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 opens with a precise statement: 'Estimate ∫ₐᵇ f(x) dx with the mean-value Monte Carlo method.' This clearly identifies the action (estimate), the resource (definite integral), and the specific method, distinguishing it from sibling tools like mc_hit_or_miss_1d or integral_simpson.

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 does not explicitly state when to use this tool versus alternatives. It names the method but lacks guidance on when MC is preferred over Simpson/trapezoid or when to use the 1D variant over 2D/3D. Usage must be inferred from the tool name and method description.

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