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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds the data source ('Stratalize go-to-market composite') and an example output, which is useful but does not disclose additional behaviors like pagination or data aggregation. This aligns with the midpoint.

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?

Three sentences plus an example and source attribution. Each part earns its place, but the example is somewhat verbose. Overall well-structured with front-loaded usage guidance.

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 3-parameter benchmark tool with no output schema, the description is thorough. It states the returned metrics, the key filters, provides a concrete example, and cites the source. An agent can confidently select and invoke this tool.

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 67% (industry and ACV have descriptions, gtm_motion does not). The description compensates by explicitly mentioning 'by industry and GTM motion,' giving semantic meaning to the undocumented parameter. The example further illustrates how parameters combine.

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 specific verbs and resources: 'Returns CAC payback ranges, LTV/CAC guardrails, and channel efficiency benchmarks by industry and GTM motion.' It clearly identifies the tool's purpose and differentiates it from siblings like get_saas_metrics_benchmark by focusing on CAC and GTM benchmarks.

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?

Opens with 'Use when evaluating sales and marketing efficiency, setting CAC targets, or benchmarking GTM performance before a board review,' giving explicit context for when to invoke. It does not mention alternatives or exclusions, but the context is clear.

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