Get model picks for a league

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

With no annotations, the description carries full burden. It transparently states it is model output only and what is excluded. It could mention additional context like data freshness or auth requirements, but it sufficiently covers the key behavioral trait of not including betting prices.

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?

Two concise sentences, no extra words. First sentence states what it returns, second clarifies a critical limitation. Every sentence is essential and front-loaded with the most important 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?

Despite no output schema, the description mentions the return fields (probability, odds, confidence). It also sets expectations by clarifying it does not contain bookmaker prices. Could be more complete about the ordering or selection logic, but overall adequate for a simple data retrieval 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 coverage is 100%, so the schema documents parameters well. The description adds value by summarizing the overall purpose (model picks) but does not elaborate on parameter details beyond the schema. It meets the baseline expectation for high-coverage schemas.

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?

Description clearly states the tool returns the model's rated selections with specific attributes (win probability, fair decimal odds, confidence word). It distinguishes from siblings like find_fixture and list_leagues by focusing on model picks, not fixtures or league lists.

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?

Explicitly notes the tool contains no bookmaker prices and cannot tell where to bet, advising when not to use it. However, it does not provide guidance on when to use this tool versus siblings (e.g., for model ratings vs. fixture lookup).

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