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

With no annotations, the description carries the full disclosure burden. It discloses the per-call price, x402 payment settlement vs API-key fallback, data sources, the 'Model-owned fields only' constraint, and the educational disclaimer. It does not describe response shape or rate limits, but the operational transparency is well above average.

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 dense but front-loaded with the core purpose and output contract before moving into pricing and payment details. The HOW TO PAY section is long, but each detail is operationally necessary for correctly invoking an x402-priced API.

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 2-parameter read tool with no output schema or annotations, this description is nearly complete: it covers output fields, target sports, optional filters, pricing, authentication, and usage caveats. The main gaps are limit semantics and a more explicit statement of response structure.

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 0%, so the description must compensate. It adds meaning to the sport parameter by listing allowed values (mlb/nba/nfl) and marking it optional. However, the limit parameter is never explained, leaving only its default value in the schema.

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 names a specific resource ('multi-sport prop-bet edge scan') and the exact output fields (edge_pct, confidence, reasoning, recommended_action) with an explicit enum of action values. It also scopes the tool to MLB, NBA, and NFL props, making it clearly distinguishable from siblings like get_sports_props and get_mlb_top_picks.

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 when to use the tool: when you want multi-sport prop edge scans with model-derived recommendations. It documents the optional sport filter and payment methods, but it never explicitly contrasts this tool with sibling alternatives or states conditions for choosing it over related tools.

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