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

With no annotations, the description carries the transparency burden. It discloses that picks are database-frozen at publication, includes losses intentionally, and provides specific metrics. This adds meaningful behavioral context beyond the name, though it omits details like default ordering or pagination behavior.

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 tight, front-loaded, and every phrase adds value. It communicates essence, content, and a key nuance (losses) in just two sentences without waste.

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 simple one-parameter tool with no output schema, the description fully covers what the tool returns and its key characteristics. No further context seems necessary for an agent to use it correctly.

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% for the single limit parameter, which already includes a clear description (default 50, newest first). The tool description adds no additional parameter information, so the baseline of 3 applies.

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 identifies the tool as Racing Alpha's public tips ledger, with specific details on content (picks, results, P/L, CLV). This distinguishes it from sibling tools like get_today_races or get_race_signals, which focus on different data.

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?

Usage context is implied by the description but not explicitly stated. It does not explain when to choose this over siblings or when not to use it, but the ledger-specific language provides reasonable inference for a tips history tool.

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