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

Annotations already mark it read-only, open-world, and idempotent, and the description adds meaningful context beyond that: it relies on a live internal call to football_find_value_bets, selects the strongest legs, and returns risk_flag and independence_warning alongside meta.estimated=true. This gives an agent a solid model of execution behavior and output caveats without contradicting the annotations.

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 compact and well-organized: a one-sentence purpose, a two-sentence mechanism, then clearly labeled Args and Returns. Every sentence contributes useful information, and the internal dependency is front-loaded alongside the purpose.

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?

For a read-only combinatorial modeling tool, the description is complete: it explains how legs are sourced, what parameters control the behavior, what the return object contains, and that the result is estimated. The presence of an output schema further reduces the burden, and no critical operational gap remains.

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 baseline is 3. The Args section largely repeats the schema's parameter descriptions ('Number of legs (2-8). Default 3.' and 'Minimum edge threshold per leg. Default 0.05.'), adding no notably deeper semantic context beyond what the structured schema already provides.

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 opens with a specific verb-resource pair: 'Model the joint probability of several match outcomes from the top model-vs-market gaps.' It then clarifies the workflow by stating it calls football_find_value_bets internally and combines selected legs, which clearly distinguishes it from the single-value-bet sibling and the cross-sport accumulator.

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 intended context is clear: build an accumulator from strong model-vs-market edges using live odds fetched via football_find_value_bets. It does not explicitly state when not to use it or name alternatives such as cross_sport_build_accumulator, so it stops short of full exclusion guidance.

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