apisports_football_predictions

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

Annotations already declare readOnlyHint and idempotentHint, and the description adds valuable context: it requires an API key in API_SPORTS_KEY, warns the response shape is 'from the vendor's documentation and has NOT been verified', and notes that 'percent values are STRINGS with a % sign'. These disclosures go beyond annotations and alert the agent to data reliability and formatting nuances.

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 front-loaded with a clear one-line summary, followed by a detailed return shape that is necessary because no output schema exists. It includes a caveat about the unverified shape, an example, an auth note, and comparison guidance in a logical order. The return-shape block is long but each part serves a purpose, making the overall structure effective.

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 absence of an output schema, the description compensates with a full vendor-documented response shape and highlights important caveats (unverified shape, percent-as-string, auth requirement). It also provides guidance on how the output should be compared with squiggle tools. For a simple one-parameter read-only tool, this is highly complete and covers the main complexities.

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 has 100% coverage for the single 'fixture' parameter, which is described as 'Fixture id'. The description adds an example value (1035037) but does not explain how to obtain fixture ids or interpret them. Since the schema already documents the parameter, the description provides minimal additional semantic value, keeping it at baseline.

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 it returns 'the vendor's own model prediction for a fixture, with the comparison data behind it' and lists the detailed response structure (winner, advice, percent, comparison, h2h). It distinguishes itself from market odds by noting it is 'a vendor model, not a market price', differentiating it from sibling tools like apisports_football_odds.

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?

It provides explicit context for comparison: 'compare it against squiggle-style model output, not against a bookmaker's line', which tells when to use this tool versus market-price tools. It also notes 'Also answers this: squiggle_tips, squiggle_ladder', offering alternative query paths. However, it does not explicitly explain when to prefer this over other apisports tools or provide exclusions.

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