predict_match

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the prediction model and outputs, but lacks details on limitations (e.g., accuracy, data recency), error handling, or performance traits like rate limits. It adequately covers the core behavior but misses deeper operational context.

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 well-structured and front-loaded, starting with the core purpose and outputs, followed by a clear parameter breakdown. Every sentence adds value without redundancy, making it efficient and easy to parse for an AI agent.

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 complexity of a prediction tool with no annotations but an output schema, the description is mostly complete. It explains the model, outputs, and parameters, but could improve by addressing limitations or dependencies. The output schema likely covers return values, reducing the need for detailed output explanation here.

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 0%, so the description must compensate. It adds meaningful semantics by explaining each parameter's purpose with examples (e.g., 'Home team name (e.g. "Arsenal")') and specifying the default for 'league'. This goes beyond the bare schema, though it could provide more on format constraints or valid values.

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 specific action ('predict soccer match outcome'), the method ('using xG-based Poisson model'), and the exact outputs (probabilities for win/draw/loss, over/under 2.5 goals, both teams to score, and top 3 most likely scores). It distinguishes itself from sibling tools like 'get_league_table' and 'list_leagues' by focusing on match prediction rather than data retrieval.

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 usage for predicting soccer match outcomes, but it does not explicitly state when to use this tool versus alternatives or provide any exclusions. There is no guidance on prerequisites or scenarios where other tools might be more appropriate, leaving usage context somewhat vague.

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