football_simulate_bracket

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

Annotations indicate readOnlyHint, openWorldHint, idempotentHint, and non-destructive. The description adds extensive behavioral context: it simulates 12 groups, advances top 2 + 8 best third-placed teams to knockout, and plays to a champion. It also details clamping, seed use, and output structure, exceeding annotation coverage.

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, front-loaded with a clear summary, followed by structured details (Args, Returns, Example). Every sentence adds value without redundancy.

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 tool's complexity and the existence of an output schema, the description fully explains the simulation scope, input parameters, output structure, and provides an example. It also notes that completed matches are locked, covering edge cases.

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 coverage is 100%. The description adds value by explaining the iteration clamping range and that ~10000 gives stable ±2% probabilities. For seed, it repeats the schema description. This additional context justifies a score above baseline 3.

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 Monte Carlo simulation of the full World Cup 2026 bracket, computing per-team round and title probabilities. It distinguishes from sibling tools like football_simulate_group by focusing on the entire tournament.

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 explains when to use the tool (to simulate the full bracket) and provides usage details like iteration clamping and recommended values for stability. It does not explicitly state when not to use it or list alternatives, but the context of sibling tools implies its specific role.

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