Upcoming fixtures

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

With no annotations, the description carries the full burden of behavioral disclosure. It mentions the probabilities are model-based and not betting advice, but does not describe what the output looks like (e.g., list structure, fields, ordering, any potential anomalies).

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 two concise sentences, front-loaded with the core purpose. The disclaimer about not being betting advice is brief and adds clarity without unnecessary verbosity.

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 tool has no output schema, so the description should compensate by explaining the return value. It does not specify what fields are returned (e.g., team names, match times, probability values) or the shape of the response. This is a significant gap for an agent aiming to invoke the tool correctly.

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 provides a complete description of the single parameter, including examples and a pointer to list_leagues. The tool description adds no additional parameter meaning, but with 100% schema coverage, a baseline score of 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 returns upcoming matches for a league, including model-based probabilities for specific betting markets. It distinguishes from siblings like get_results (past matches) and get_match (single match) by focusing on upcoming fixtures with probabilities.

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?

No explicit guidance is provided on when to use this tool versus alternatives such as get_results or get_league_table. The only usage hint is within the parameter description (use list_leagues to discover league keys), which is about parameter discovery, not tool selection.

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