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

Annotations already indicate this is read-only, idempotent, and non-destructive. The description adds useful behavioral detail: results are Monte Carlo estimates, iterations are clamped, meta.estimated is true, and completed matches are locked in as conditions. This goes beyond the annotations to explain the approximate nature of the outputs.

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 well-organized with Args and Returns sections, and the key behavioral point about Monte Carlo estimation is front-loaded. It is compact and information-dense, though the phrase 'Monte Carlo one group' is slightly awkward.

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 output schema and annotations already cover return structure and safety traits. The description adds the important context of grouping within the 12-group qualification, the 'truthful combined p_advance' nuance, and the fact that completed matches are conditioned. This is complete for the tool's moderate complexity.

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 100%, so the schema already documents both parameters fully. The description repeats the group and iterations meanings without adding substantially new semantic detail beyond the schema, so the baseline of 3 is appropriate.

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 states a specific operation: Monte Carlo simulation of exactly one group within the full 12-group qualification context. It clearly scopes the resource to a single group letter A–L and distinguishes it from the sibling football_simulate_bracket, which covers the bracket-level simulation.

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 implies the tool is for simulating a single group's advancement probabilities, and it mentions the 'full 12-group qualification context.' However, it does not explicitly say when to prefer this tool over football_simulate_bracket or other football tools, nor does it provide exclusions or alternative routing.

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