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

With no annotations provided, the description bears full responsibility for behavioral disclosure. It mentions 'recorded live results' but does not state whether the tool is read-only, requires authentication, has side effects, or any constraints. The absence of any behavioral disclosure is a significant gap.

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 a single sentence with a colon and list, conveying key information without fluff. However, the structure could be improved with clearer separation of purpose and output metrics.

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 the tool has no parameters and no output schema, the description provides some context by listing output types, but it omits details like data sources, caching behavior, or performance implications. It is adequate but not comprehensive.

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 has no parameters, so the schema coverage is 100% vacuously. Baseline for 0 parameters is 4, and the description adds value by specifying the output metrics (Spearman rankings, median-split deltas, dead inputs), giving the agent context beyond the empty 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 clearly states the tool's purpose: it determines which engine inputs predict outcomes using specific metrics (Spearman rankings, median-split deltas, dead inputs). It distinguishes itself from sibling tools like alphalabs_explain_decision by focusing on input attribution rather than explanation of a single decision.

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?

No guidance is provided on when to use this tool versus alternatives such as alphalabs_evaluate_signal or alphalabs_explain_decision. The description does not mention prerequisites, limitations, or typical use cases.

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