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

Annotations already declare readOnly, idempotent, non-destructive, closed-world behavior, so the safety profile is covered. The description adds genuine beyond-schema behavior: results are grouped by issuer, ordered verified-first, and normalized via the same issuer-suffix peel as taxonomy integrity (ON / x / B / Reality R). It does not discuss rate limits or failure modes for an unknown ticker, but the ordering and grouping disclosure is substantive.

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?

Three sentences, front-loaded with the core behavior before the normalization detail and the exclusion rule. The 'issuer-suffix peel as taxonomy integrity' phrasing is jargon-heavy but carries real meaning; nothing is wasted.

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?

An output schema exists and annotations cover the safety profile, so the description only needs to explain selection and ordering semantics, which it does. The one remaining ambiguity is how limit interacts with the issuer grouping, but that is minor for a three-parameter read 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 description coverage is 100%, with limit, underlying, and verified_only each documented in the schema, so the baseline is 3. The description's mention of 'verified-first ordering' adds a little color relevant to verified_only but does not explain parameter interactions, such as how limit truncates within or across issuer groups.

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 gives a specific verb and resource ('Lists every tokenized deployment of one spot underlying ticker') with concrete examples (QQQ, SPY, NVDA) and names the grouping and ordering behavior. The exclusion of leveraged and inverse products implicitly separates it from a general asset search like find_assets, but no sibling tool is named explicitly.

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?

Usage context is clear: reach for this when you want all wrappers around a single spot underlier. The exclusion clause ('Excludes leveraged and inverse products of that underlier') acts as a when-not rule, telling the agent this is not a leveraged/inverse discovery tool. It stops short of naming an alternative tool for those cases.

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