sales_snapshot

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

With no annotations provided, the description carries the full burden. It discloses that the tool performs server-side aggregation over sale.order records, computes deltas, includes a verdict, and mentions the optional trend series. It does not explicitly state side effects or permissions, but as a read-only report, the description gives sufficient behavioral insight beyond what is visible in the schema.

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 yet comprehensive: a one-sentence summary, a concise list of output components, then a clear Args block. No word is wasted; each sentence/parameter earns its place. The structure is front-loaded with the main purpose and flows logically.

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's complexity (7 parameters) and the presence of an output schema, the description provides all necessary context: what is produced, parameter semantics, and even edge-case behavior. The output schema covers return structure, so the description does not need to repeat it. The only minor gap is explicit usage alternatives, which is already accounted for in the Usage Guidelines dimension.

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 0%, so the description must fully compensate. It does: every parameter (period_days, stale_quote_days, top_n, timezone_offset, growth_threshold_pct, company, trend_weeks) is explained with its purpose, default, and in some cases the meaning of special values (e.g., trend_weeks=0 disables the query). This is exemplary.

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 opens with a clear, specific statement: 'Report how sales are going versus the previous period, in one call.' It then enumerates the exact outputs (deltas, top customers/products, stale-quote count, verdict), making it unambiguous what the tool does. This distinguishes it from sibling tools like pipeline_review or business_pulse, which focus on different metrics.

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 implies usage for sales performance comparisons with 'in one call' and lists the report components, but it does not explicitly state when to prefer this tool over alternatives. No exclusions or alternative tool names are mentioned, so the agent must infer the appropriate context from the description alone.

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