list_picks

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

No annotations are provided, so the description carries the burden of behavioral disclosure. It adds valuable context that picks are 'computed live from upcoming fixtures' and 'volumes vary by day', and it lists the data fields included. It doesn't discuss side effects (not needed for a list) or auth/rate limits, which keeps it at a solid mid-range.

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: three sentences with clear logical flow (purpose → return contents → usage cue). No redundant filler.

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 description covers the tool's function, the pick data contents, and the usage context. With 5 optional params and no output schema, it sufficiently describes what the agent needs to know. It omits pagination/sorting details, but these are not critical for a simple list 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 description doesn't need to explain parameters. It mentions the filter dimensions (league, market, min edge, quality tier) but doesn't add details beyond the schema. 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 clearly states the tool's purpose with a specific verb ('List') and resource ('Statos's current betting suggestions'). It distinguishes from siblings like get_match_picks by emphasizing 'current' suggestions and the filter dimensions.

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 provides an explicit usage cue: 'Use this for "what does Statos suggest today?" type queries.' It doesn't explicitly name alternatives or mention when not to use it, but the context is clear.

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