racing_next_to_go

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

With no annotations, the description carries the burden of behavioral disclosure. It goes beyond the obvious by explaining that each race reports data_age_seconds and each price its own age, allowing the agent to accurately describe freshness instead of implying live-to-the-second data. This is meaningful added 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?

Two sentences, both dense with purpose and usage. The first defines the exact output, the second provides example queries and a behavioral note. No wasted words, and the key differentiation ('primary tool') is front-loaded.

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?

For a read-only tool with no output schema, the description covers the core: what races are returned, the bookmaker/runner format, and data freshness. It could optionally mention that results are sorted by start time or include pagination, but for a simple 'next to go' endpoint, this is sufficient.

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 100%, and the schema already provides rich guidance (e.g., the country parameter explains why passing AU is important and gives bookmaker count statistics). The description adds no parameter-level semantics beyond what the schema already offers, so the baseline 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 the next Australian horse, greyhound, and harness races with every bookmaker's price for every runner. It then gives concrete example questions ('what's racing next', 'who is favourite in the next race at <venue>') that distinguish it from price-comparison or historical-result tools.

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?

Explicitly says 'This is the primary tool' and lists precise use cases for current Australian racing prices. It stops short of naming sibling tools as alternatives or stating when not to use it, so it misses the explicit exclusion guidance seen in the top calibration example.

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