Match detail

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

Without annotations, the description fully discloses behavioral aspects: it returns probability models, team ratings, and histograms. It clarifies input requirements (slug format) and the model is read-only (get), which aligns with the tool's purpose. No contradictions with missing 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 a single, well-structured sentence that efficiently conveys purpose, input format, and output contents with no wasted words.

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 description is complete for a single-parameter tool with a simple return model. No output schema is needed given the detailed description of what is returned. A minor gap: it does not explain return format or potential error conditions, but for this context it suffices.

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 coverage is 100% with one parameter. The description adds meaning by explaining the slug format and source (get_fixtures), but the schema already provides an example. This adds some value beyond the schema, justifying a 3 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 uses specific verbs and resources: 'get_match' retrieves a match by 'slug'. It details the return data (full probability block across markets, team ratings, histograms) and explains the slug format, distinguishing it from siblings like get_results or get_fixtures.

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 explains the required slug format (country/league-slug/date-home-vs-away) and references get_fixtures as the source for slugs, providing clear context for use. It does not explicitly state when not to use this tool or mention alternatives among siblings, but the specificity is strong.

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