football_xg_model

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, so the safety profile is clear. The description adds useful context about the return values (expected_home_goals, expected_away_goals, probabilities) and that meta.estimated is true. No contradictions.

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 (two lines plus Args/Returns), front-loaded with the main purpose. Every sentence contributes value. No wasted words.

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 has 3 parameters (2 required, 1 optional with default), a rich annotation set, and an output schema, the description fully covers what the tool does and returns. It is complete for an xG model estimation tool.

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 all parameters. The description repeats the parameter names with examples, but adds no additional meaning beyond what the schema provides. Baseline 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 uses a specific verb 'Estimate' and clearly identifies the resource: expected goals and win/draw/loss probabilities. It also provides a clear return structure. Among siblings like football_match_predictor, this tool is distinct as it focuses on xG model output.

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 gives usage through the Args section (home_team, away_team, neutral), but does not explicitly state when to use this tool versus alternatives like football_match_predictor or football_simulate_group. The context is implied but not clearly differentiated.

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