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

No annotations are provided, so the description carries the full burden. It discloses the Monte Carlo method, strength-awareness, reproducibility via seed, and simulation count with a cap at 50000. This is good behavioral information for a read-only data retrieval tool, though it could explicitly state it is non-destructive.

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 two sentences. The first sentence states the purpose and method. The second gives clear usage instructions. No redundant or superfluous words; front-loaded with key information.

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 adequately explains the output (chances for qualification, final, win) and the input (competition slug, optional trials/seed). With no output schema, it provides enough context for an agent to understand return shape. It also mentions the simulation cap. For a Monte Carlo tool, this is complete.

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% (all three parameters described), so baseline is 3. The description adds value by explaining that trials and seed control simulation and reproducibility, and that competition is identified by slug with an example. This provides context beyond the schema descriptions.

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 returns each team's chances of qualifying from group stage, reaching final, and winning, using a seeded strength-aware Monte Carlo simulation. It specifies the required input (competition slug) and optional parameters for control/reproducibility, distinguishing it from sibling tools like get_match_probabilities which focus on match-level odds.

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 provides clear usage instructions: pass competition slug, and control trials/seed for reproducibility and simulation control. It gives an example slug ('world-cup-2026') and mentions default and cap values. However, it does not explicitly state when to use this tool vs alternatives like get_qualification_scenarios, missing some exclusion guidance.

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