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

Annotations already declare readOnly, idempotent, and non-destructive behavior. The description adds valuable context by specifying the data source ('public BankFind quarterly financials'), the scope ('executive snapshot metrics, trend series, community-bank comparison data'), and explicit exclusions ('non-public or non-BankFind QBP items'). This goes beyond the safety profile and clarifies what the tool will and will not include, though it does not detail internal processes or error behavior.

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?

Two sentences, highly efficient. The first sentence front-loads the main purpose, and the second succinctly enumerates the inclusions and exclusions. No filler or redundant phrasing—every clause carries meaningful information.

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 has only three optional params, a clear read-only/idempotent annotation profile, and an output schema (as per context), the description covers the essential aspects: what is built, its contents, and its constraints. It doesn't mention fallback behavior for missing repdte (though the schema does) or output structure (covered by output schema), leaving only minor gaps such as detail on how exclusions are determined. Overall adequate for a straightforward data generation 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 100% with descriptive parameter docs, so baseline is 3. The description adds some semantic connection by mentioning 'trend series' (aligns with trend_quarters) and 'community-bank comparison data' (aligns with include_community_banks), but it does not add new syntax or format details beyond the schema. It reinforces the mapping but doesn't significantly enhance parameter understanding.

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 purpose with a specific verb ('Build') and resource ('chart-ready data for a concise QBP Lite report') and adds scope details ('from reproducible public BankFind quarterly financials'). It also lists included components and exclusions, distinguishing it from sibling analysis/search tools which focus on insights rather than generating a data bundle.

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 by defining the output type (chart-ready data for QBP Lite), but it does not explicitly state when to prefer this tool over alternatives or provide exclusionary guidance. No sibling tools are mentioned, so the user must infer from the specific report scope that this is for generating a bundle rather than analyzing or fetching single records.

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