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

Annotations already mark this as read-only and non-destructive. The description adds useful behavioral context beyond that: it names the data source, reveals the per-call cost ($0.02 USDC), and gives an example of benchmark values returned. This is helpful supplementary information despite not covering pagination or data freshness.

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 information-dense but well-organized: use cases first, then returned metrics, an illustrative example, source, and cost. Each sentence earns its place, though the example and cost could arguably be trimmed without losing core 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?

With no output schema, the description usefully conveys what the tool returns and gives a concrete sample output. It covers source, cost, and typical usage scenarios. It does not explain edge cases like unsupported ARR bands or how optional parameters alter results, but the core information an agent needs to invoke it is present.

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 documents arr_usd, burn_multiple, and growth_rate_pct. The description adds the useful notion of 'by ARR band' and provides an example band ($10-50M ARR), but it does not explain how the optional parameters influence the returned benchmark comparison. This stays at the baseline 3.

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 and resource: it 'Returns Rule of 40, burn multiple, CAC payback, NRR, gross margin, and ARR growth targets by ARR band.' This clearly differentiates it from sibling tools like get_cac_benchmark or get_saas_market_intelligence by listing the exact benchmark metrics it provides.

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 usage context: 'Use when assessing SaaS company financial health, preparing investor reporting, or benchmarking KPIs before a fundraise or board presentation.' It does not name alternatives or state when not to use it, but the context is specific enough for an agent to select it appropriately among the many benchmark siblings.

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