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 discloses that the tool returns a dictionary containing the cumulative running average, and mentions optional seed for RNG and precision for rounding. However, it does not specify the exact structure of the returned dict (e.g., keys, whether final estimate is included), nor does it explain error handling for constraints like b > a. The behavior is partially transparent but leaves important details uncovered.

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 appropriately concise and well-structured. It opens with a one-sentence purpose, followed by a clean list of arguments and a return description. There is no redundant or extraneous text. The information is front-loaded and easy to scan, making it efficient for an AI agent to parse.

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 description explains the core computation logic and all parameters, but given there is no output schema, the return structure is incomplete: it only says 'Dict with the cumulative running average' without detailing keys or whether it includes additional metadata. The 'convergence' aspect is described but not elaborated (e.g., whether sample indices are included). This leaves some ambiguity, though the tool is relatively simple.

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?

Since the input schema has no descriptions (0% coverage), the description compensates by defining all parameters: func_str as a math expression in x, a and b as integration limits with b > a, N as total samples, seed as optional RNG seed, and precision as rounding digits. This adds substantial meaning beyond the raw schema. However, the syntax for func_str is not specified (e.g., Python math syntax), slightly reducing clarity.

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's function: 'Show how the mean-value Monte Carlo estimate converges as samples accumulate.' It uses a specific verb ('Show'), names the resource ('mean-value Monte Carlo estimate'), and describes the method (accumulating samples). This differentiates it from sibling tools like 'mc_valor_promedio_1d' or 'mc_hit_or_miss_1d' which likely compute a final estimate or use a different approach.

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 provide any when-to-use guidance or comparison with alternatives. It doesn't mention that this tool is appropriate when studying convergence behavior, nor does it exclude it from other contexts. No explicit exclusions or preferred sibling tools are indicated, leaving the agent without clear selection criteria.

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