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

The description adds substantial behavior context beyond the annotations: it reveals the exact output structure (ranked list, severity categories, findings, recommendations, estimated impact) and the analytical dimensions considered (cache leverage, velocity, SNR, cache creation ratio, input bloat, 10xDEV compounding). Annotations already declare read-only, idempotent, and non-destructive behavior, so the description's added transparency about outputs and analysis scope earns a high score.

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 compact and front-loaded: purpose, output, checks, and usage guidance each get one efficient sentence. No filler or repetition of schema information. Every sentence contributes actionable information.

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?

Despite having no output schema, the description tells the caller what to expect in the response. It covers purpose, invocation timing, input concept, and output format. It does not detail edge cases or error behavior, but for a read-only diagnostic tool with clear annotations, the description is effectively complete.

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 schema fully documents each parameter's meaning as a token count. The description adds the context that these four values form '4 token pillars' and mentions derived metrics, but it does not substantially deepen per-parameter semantics beyond the schema. Baseline 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 states a specific verb ('Analyzes') and resource ('your token cascade') and precisely defines the output: a ranked list of efficiency leaks with severity levels, findings, recommendations, and estimated impact. It also enumerates the specific checks performed, making the tool's purpose unambiguous and distinguishable from analysis siblings.

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 gives explicit placement guidance: 'Use this before simulate_change to understand what's wrong.' This clearly indicates when to invoke this tool relative to a key sibling. It could go further by stating when not to use it or naming alternatives like suggest_improvements, but the provided context is sufficient for basic routing.

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