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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, and the description is consistent with them. It adds valuable behavioral context beyond annotations: vintages are write-once and cannot be edited/backfilled, projections never frozen are lost, and the dataset is fully public and ungated. This greatly informs agent expectations.

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 detailed but deliberately structured: a definition paragraph, explicit use/non-use sections, and a filter/access summary. Each sentence adds a distinct fact — data lineage, immutability, grading, alternatives, access control, citation. It is long because the tool warrants it, not because of redundancy.

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 supplies a clear mental model of returned rows: model, scenario, series, target period, predicted mid plus low-high band, realized value, resolution category, and error percentage. It also covers filters, access restrictions (none), and citation. Nothing needed to call and interpret the tool correctly is missing.

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 carries the full burden. It compensates well by enumerating model allowed values (hbm-pricing-model|dram-ddr4-model|cowos-capacity-model), explaining include_assumptions (include_assumptions=true for frozen assumption-set auditing), and giving a concrete series example (hbm3e). It adds meaning to every parameter.

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-resource pair: retrieving Silicon Analysts' own forecast track record, with detailed row semantics. It explicitly contrasts with get_forecasts (third-party forecasts) and other market-data tools, so an agent can distinguish it from siblings without opening any schema.

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 provides explicit 'USE THIS for' and 'DO NOT USE for' sections, naming exact alternative tools (get_forecasts, get_wafer_pricing, get_hbm_market_data) and the conditions that route to each. This is the strongest possible usage guidance.

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