Estimate the double integral of a two-variable function over specified x and y bounds using the mean value Monte Carlo method.
Estimate the double integral of f(x, y) over [x_a,x_b]×[y_a,y_b] by mean value.
| Name | Required | Description | Default |
|---|---|---|---|
| N | No | ||
| x_a | Yes | ||
| x_b | Yes | ||
| y_a | Yes | ||
| y_b | Yes | ||
| seed | No | ||
| func_str | Yes | ||
| precision | No | ||
| nivel_confianza | No |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits. It does not mention that the result is stochastic, that N controls sample count, or that the output is approximate. The phrase 'Estimate... by mean value' only hints at the algorithm without explaining the random sampling nature or return format.
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 a single, front-loaded sentence that directly states the core function. Every word earns its place, with no wasted text.
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 tool with 9 parameters, no output schema, and no annotations, this description is far too minimal. It lacks details on return values, expected function string syntax, the role of N and seed, or how 'precision' and 'nivel_confianza' influence the result. The abundance of sibling tools also demands more behavioral context to avoid misuse.
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 implicitly explains the integration bounds (x_a, x_b, y_a, y_b) via 'over [x_a,x_b]×[y_a,y_b]' but leaves N, seed, precision, and nivel_confianza entirely unexplained. The description adds minimal meaning beyond the parameter names.
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 that the tool estimates a double integral over a rectangular domain using the mean value method. This specific verb+resource formulation distinguishes it from siblings like mc_valor_promedio_1d and mc_hit_or_miss_1d.
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?
No guidance is provided on when to use this Monte Carlo mean value method versus alternatives like Simpson or other MC variants. The description only states what it does, leaving the agent to infer appropriate usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.