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

Annotations already establish readOnly, idempotent, non-destructive, closed-world behavior, so the description need not repeat them. It adds value beyond them by disclosing that results carry coverage limitations and by explaining that data_version, where returned, signals a changed release. It does not describe pagination or response shape, but an output schema exists to cover returns.

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 purpose is front-loaded in the first clause, and every subsequent sentence adds operational information rather than restating the name. Minor waste remains in brand framing ("Korzy") and vague phrases like "standardized attributes" and "coverage limitations" that are not cashed out.

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?

For a three-parameter, read-only lookup with an output schema, the description supplies the ID-provenance workflow and version semantics an agent needs to call it correctly. The residual gap is that "coverage limitations" is named but never characterized, which matters for a domain (utility coverage) where incompleteness is likely.

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 burden and mostly does: it enumerates the layer domain and links each layer to the source of its feature_id, and it explains that data_version is a release-change detector rather than an input filter. It leaves feature_id's opaque format and the data_version hash pattern unaddressed, but the operational meaning of all three parameters is conveyed.

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 uses a specific verb ("Fetch") plus a clearly bounded resource (a Korzy utility record by feature_id for water/sewer/treatment/gas/fiber). It also states what comes back conceptually (standardized attributes and coverage limitations). It does not explicitly differentiate itself from siblings, though the sibling list is domain-distinct enough that confusion is unlikely.

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?

It gives real usage context by specifying where each ID type originates: water/sewer/treatment IDs from point context results, gas/fiber IDs from infrastructure search results. That tells an agent the precondition for calling it. It stops short of naming explicit alternatives or when-not-to-use cases.

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