Season projection

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

With no annotations provided, the description must carry the behavioral disclosure burden. It states the simulation count (10,000) and refresh frequency (daily), but does not mention data sources, processing time, or whether the results are cached. This is decent but could be richer for a computational tool.

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 a single, information-dense sentence that front-loads the core concept (Monte Carlo season projection) and lists outputs concisely. Every part 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 has a single parameter with a well-described example, no output schema, and no nested objects, the description provides sufficient context about output content (probabilities, points, percentile range) for an agent to understand what to expect. It could mention typical result structure explicitly, but the current completeness level is high for a simple 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?

Since schema description coverage is 100%, the baseline is 3. The description does not add parameter-specific details beyond what the schema provides, but the league parameter is well-documented inline. The tool has only one required parameter, so no additional compensation is needed.

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 provides a Monte Carlo season projection with 10,000 simulations and daily refresh. It lists the specific outputs: title, top-4, relegation probabilities, mean final points, and percentile range, making the purpose highly specific and distinguishable from siblings.

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 clear context that the tool should be used for season projections based on Monte Carlo simulations. It does not explicitly exclude alternatives like get_league_table for current standings, but the statistical nature implies this is for forward-looking analysis, which is adequate guidance given the sibling tool context.

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