get_season_stats

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

With no annotations, the description carries the full burden of behavioral disclosure. It reveals important constraints: sample-size floors (≥30 balls for strike rate, ≥15 balls bowled for economy), which directly affect result reliability. It also identifies the data source as 'canonical aggregate'. This goes beyond what structured fields alone convey, but still omits details like data freshness, pagination, or read-only nature.

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 extremely concise: two sentences that front-load the main purpose and immediately list key options and constraints. Every sentence adds information without redundancy, making it efficient for an agent to parse.

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 tool has 3 parameters and no output schema. The description covers the primary purpose, sortable metrics, and sample-size floors. However, it fails to describe the structure of the returned data (e.g., player names, numerical aggregates) or how results are ordered. Given no output schema, this omission leaves the agent uncertain about what to expect, making the description incomplete for a leaderboard 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?

The input schema covers 67% of parameters with descriptions (limit and teamCode). The description adds value by listing the sortBy options (though the schema already has an enum) and attaching the sample-size floors to specific metrics. However, it does not explain limit's default/max in any new way beyond the schema. Overall, the description supplements but does not significantly transform parameter understanding.

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 explicitly states the tool provides an 'IPL 2026 season leaderboard from CricketStudio canonical aggregate', clearly specifying the verb (get/retrieve), resource (season leaderboard), and scope (IPL 2026). It lists sortable metrics and optional filters, which distinguishes it from sibling tools like get_ipl_leaderboard (which may be for different seasons or broader).

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 implies usage for retrieving season stats with specific sort options and a team filter, but it does not explicitly state when to choose this tool over similar siblings like get_ipl_leaderboard or get_bbl_leaderboard. The mention of sample-size floors provides some context for data reliability, but no direct guidance on use cases or exclusions.

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