Run Monte Carlo yield analysis by varying design parameters within tolerances and evaluating pass/fail criteria to estimate manufacturing yield.
Set up a Monte Carlo yield analysis to estimate manufacturing yield. Randomly varies parameters according to their tolerances and evaluates pass/fail criteria.
| Name | Required | Description | Default |
|---|---|---|---|
| parameters | Yes | ||
| num_samples | No | Number of Monte Carlo samples. | |
| pass_criteria | Yes |
Changes observed during successful MCP inspections.
v1.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses core behavior: random parameter variation and pass/fail evaluation. However, it does not clarify whether the tool runs simulations or merely sets up the analysis, nor does it mention side effects or requirements. With no annotations, more detail on execution and output would improve transparency.
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 concise, consisting of two sentences that front-load the core purpose and method. No extraneous information is present.
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?
Given the complexity of Monte Carlo analysis, the lack of an output schema, and low schema coverage, the description is insufficient. It does not explain how results are returned or what the output of the tool is, leaving a significant gap for an AI agent to correctly invoke and process outcomes.
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 description adds meaning by explaining that parameters are varied according to tolerances and that pass/fail criteria are evaluated, which complements the schema. However, it does not elaborate on the distribution or num_samples parameters, and schema coverage is low (33%), so the description only partially compensates.
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 performs a Monte Carlo yield analysis, specifying it varies parameters based on tolerances and evaluates pass/fail criteria. This directly conveys the purpose and distinguishes from general simulation tools, though it does not explicitly differentiate from other analysis tools like sensitivity analysis.
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 tool versus alternatives such as parameter sweep or sensitivity analysis. The description lacks context for appropriate usage scenarios or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.