mc_convergencia_1d

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

With no annotations, the description carries the full burden. It does disclose the return shape clearly (a dict with the cumulative running average, or [dict, Image] when plot=True), which is genuinely useful. However, it says nothing about cost, sample-size tradeoffs, or failure modes for malformed expressions, leaving meaningful behavioral gaps.

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?

Uses an Args/Returns structure that is front-loaded with the purpose and wastes little. Each parameter line is brief and functional; a bit more economy could be achieved but nothing is gratuitous.

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 seven-parameter tool with no output schema and zero schema description coverage, the description supplies the return format and documents all parameters, so an agent can call it correctly. The main gap is routing guidance relative to its many siblings, which the description never addresses.

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, and it does: every one of the seven parameters is explained (func_str as a math expression in x, a/b as integration limits with the b > a constraint, N as total samples, seed as optional RNG seed, precision as rounding digits, plot controlling the PNG). The b > a constraint is value the schema lacks.

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?

States a specific verb and resource: it shows how the mean-value Monte Carlo estimate converges as samples accumulate. This clearly distinguishes a convergence-tracking tool from a plain integral estimator, though it never names or contrasts with the closely related siblings like mc_valor_promedio_1d or mc_estadistico_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 when-to-use guidance, no prerequisites, and no mention of alternatives despite a large family of Monte Carlo and integral siblings. The agent must infer that this is for studying convergence rather than for computing a single integral value.

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