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

Despite no annotations, the description fully discloses behavioral traits: it explains that in-play odds are unbackfillable, that it computes per-match tick counts by source, and that live dataset totals are computed from the table (never hardcoded). This provides sufficient transparency for safe invocation.

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 fairly concise for the amount of information it conveys. It front-loads the primary purpose and specific outputs. Minor redundancy (e.g., mentioning 'live dataset totals' twice) prevents a perfect score.

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 no output schema and 3 optional parameters with full schema coverage, the description provides comprehensive context about what the tool returns, its limitations (unbackfillable), and how it relates to sibling tools. It is complete enough for an agent to use effectively.

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%, so the description does not need to add extra parameter details. The description does not provide additional semantics beyond the schema's parameter descriptions. 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 explicitly states the tool's function: discovering matches with prediction-market odds coverage. It details specific outputs (tick counts by source, in-play vs kickoff, etc.) and distinguishes itself from the sibling tokenintel_match_odds by stating that tool fetches probability curves for covered matches.

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 provides clear context for when to use this tool (before fetching odds to check coverage) and explicitly directs the agent to tokenintel_match_odds for fetching covered matches' probability curves. It also notes the limitation that in-play odds are unbackfillable, guiding appropriate usage.

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