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

With no annotations, the description carries the full burden, and it does disclose the report's content (backtest results, live results, two comparison targets), which implies a read-only, non-mutating operation. However, it says nothing about access requirements, cost/delay, or how the comparison is presented, so behavioral context is only partial.

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?

A single front-loaded sentence with no filler; the two evaluation streams and two comparison targets are packed in without redundancy.

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?

There is no output schema and no annotations, so the description must convey the return shape, and it only gestures at contents ('past-season tests', 'live results vs a baseline and FPL's own') without indicating format, metrics, or time granularity. Adequate hint, incomplete for an agent that must decide how to use the output.

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?

The tool takes zero parameters, so there is nothing for the description to disambiguate; baseline score 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 names a specific resource (model accuracy) and details the two evaluation streams it reports: past-season tests and this season's live results, benchmarked against a baseline and FPL's own figures. No sibling tool reports accuracy, so it is distinguishable, though it never states an explicit verb like 'returns' or 'reports'.

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?

There is no explicit when-to-use guidance, no prerequisites, and no mention of alternatives. The purpose of consulting model accuracy (e.g. before trusting predict_points) is only inferable, not stated.

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