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

With no annotations, the description carries the full burden. It discloses the `{matched:false}` return on misses, a cached corpus with RDAP fallback, and typical sub-300ms latency. This adds behavioral depth beyond simply stating 'returns data', though it does not cover auth or error states.

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-organized into purpose, usage, inputs, returns, and latency sections. It is front-loaded and efficient, though the 'long-term moat' phrase adds a touch of flourish without being harmful.

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 one-parameter lookup with no output schema, this description is comprehensive: it documents return fields, input format, usage context, latency, and the not-found sentinel. No critical gaps are apparent.

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?

The input schema has zero description coverage for the `asn` parameter, so the description compensates by specifying the format ('AS#### or a bare AS number') and that it is a required path parameter. This is more meaningful than the bare schema.

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 opens with a specific verb ('Returns') plus the resource ('GhostRoute's ownership-graph record for an autonomous system') and enumerates the exact data fields returned. It clearly distinguishes this from sibling lookups by focusing on ASN ownership and jurisdiction rather than AI or domain-specific data.

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?

An explicit 'Use this tool when' section provides two concrete scenarios: needing corporate owner/jurisdiction from an origin ASN, or assessing whether an ASN belongs to a cloud front vs. real operator. It lacks explicit exclusions or alternative tool names, but the context is sufficiently clear.

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