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

Annotations already declare read-only, idempotent, non-destructive. The description adds valuable behavioral context beyond that: currency is never converted, includes source URL and verbatim quote, access tiers ('latest slice all tiers, full history Pro'), and an explicit note about never returning 403. This enriches the agent's understanding of what to expect.

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 well-structured with clear sections: introduction, row format, usage guidance, filters, and access notes. Every sentence carries meaningful information without fluff. It is appropriately sized for the tool's complexity.

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 no output schema, the description compensates by detailing the row structure (company, fiscal_period, metric, value(s), unit, as_of, source URL, verbatim quote) and the overall purpose of as a leading indicator. It covers access tiers and citations. Minor gaps remain regarding combining filters and date range semantics, but overall it is quite 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 coverage is 0%, so the description must compensate. It explains the key domain filters (company, metric, segment, fiscal_period) with explicit enum values for company and metric examples. However, generic parameters like to, from, since, all, and limit are not explained, leaving gaps in understanding how to query date ranges or pagination. The description adds value but is incomplete for all 9 parameters.

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 clearly states the tool's function: retrieving WFE order-book signals from wafer-fab-equipment makers (ASML, AMAT, Lam, KLA, TEL) including bookings, backlog, segment revenue, and guidance. It specifies the data scope, currency behavior, and relationship to fab capacity, distinguishing it from the sibling tools via the 'USE THIS' and 'DO NOT USE' sections.

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?

Explicit usage guidance is provided: 'USE THIS for' lists concrete use cases and example questions, while 'DO NOT USE for' names specific alternatives (get_fab_capacity, get_foundry_economics). This gives the agent clear decision criteria for tool selection.

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