Model track record

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

With no annotations provided, the description carries the full burden for behavioral disclosure. It reveals that this is a read operation (track record), provides scoping (settled predictions only) and states data transparency (no cherry-picking). However, it does not mention whether the data is aggregated across all markets or if there are pagination/rate limits, leaving some behavioral aspects implicit.

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 extremely concise—two sentences with no wasted words. Every sentence adds value: the first states the purpose and scope, the second clarifies the data type and integrity. Information is front-loaded effectively.

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 that this is a single-parameter tool with no output schema and no nested objects, the description is largely complete. It covers what the tool returns (prediction grades, hit rates, P/L) and the scoping constraint (settled predictions only). A minor gap is that it doesn't mention whether the date range is configurable beyond the days parameter, but the input schema handles this. Overall, it is sufficient for an agent to understand usage.

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%, so the baseline is 3. The description adds context by stating the default lookback (90 days) implicitly via the 'default 90' mention in the schema, but does not add new meaning beyond what the schema already provides. The parameter is simple and well-documented in the schema.

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 a specific verb ('get') and clearly identifies the resource ('public, settled prediction track record'). It distinguishes itself from siblings by explicitly stating what it covers (model signals graded against real results) and what it does not (no cherry-picking). The mention of 'FC' and specific data types (hit rates, P/L) adds further specificity.

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 implies this is for reviewing model accuracy and performance, but it does not explicitly state when to use this tool versus alternatives like get_season_projection or get_rankings. No clear when-not-to-use guidance or alternative names are provided, leaving the agent to infer context from the sibling list.

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