apisports_football_predictions

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

The description adds substantial value beyond the annotations (readOnly, openWorld, idempotent). It warns that the return shape is from vendor docs and unverified, notes that percent values are strings with '%', explains the response structure, requires an API key, and explicitly states it's a vendor model rather than a market price. This is rich behavioral context that goes well beyond the annotations.

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 lengthy but well-structured: it starts with the core purpose, then presents the return shape, then notes caveats (unverified shape, string percent, auth), and an example. Each sentence earns its place; the front-loading of the purpose and the clear separation of the notes make it easy to scan.

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 tool with only one parameter and no output schema, the description is quite complete: it provides a detailed return shape, a warning about its unverified nature, auth requirements, and an example. The only minor gap is that it doesn't describe error or empty-response behavior, but that's not essential for basic invocation.

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 schema covers the single parameter 'fixture' with a clear description, so baseline is 3. The description adds a concrete example input ('{

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 provides 'The vendor's own model prediction for a fixture, with the comparison data behind it.' This is a specific, distinct purpose that differentiates it from siblings like apisports_football_odds (market prices) and squiggle-style models, and it even explicitly contrasts it with a bookmaker's line.

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 gives context on when to use this tool by clarifying it's a model prediction, not market price, and suggests comparing it against 'squiggle'-style model output. It lacks an explicit 'use this when' or 'don't use for X' statement, but the context is clear enough for an agent to select it appropriately.

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