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

Beyond the annotations (read-only, idempotent, non-destructive), the description adds critical behavioral details: it is rule-based not an LLM, specific language features are recognized, bare 'football' is ambiguous and left unrecognized, and the response includes interpreted filters, equivalent REST call, and unrecognized_terms.

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 every sentence contributes useful information. It opens with the primary purpose, provides illustrative examples, explains internal behavior, and closes with usage guidance—all without fluff.

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 tool's natural-language processing complexity, the description thoroughly covers usage scope, behavioral limitations, and response contents. The output schema handles return values, so no additional detail is needed there. The description is comprehensive for an AI agent to decide when and how to invoke it.

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?

Although the schema documents both parameters, the description enriches the 'query' parameter by detailing supported sports, statuses, date formats, and the ambiguity behavior. It also clarifies the 'limit' parameter's override semantics, adding value beyond the schema fields.

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's purpose: search events using natural-language queries instead of structured filters. It provides concrete examples and explicitly distinguishes it from sibling tools like list_events.

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 gives explicit guidance on when to use this tool versus the alternative, stating 'Prefer list_events when you already know the structured filters you want.' This directly addresses tool selection.

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