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

Beyond the readOnly/idempotent annotations, the description discloses important behavioral limits: NANPA rate centres are matched to cities, so results are city-level rather than legal boundaries, and mobile numbers keep their code after moving. This is precisely the kind of non-obvious context an agent needs before relaying results.

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 cover output contents, usage examples, and caveats with no filler. The most decision-relevant information (what the tool returns) is front-loaded, and the caveat is explicitly marked for the agent to relay.

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, read-only lookup with no output schema, the description covers all essential context: input semantics (via schema), return contents, accuracy caveats, and indirect sibling routing. Nothing an agent needs to invoke or explain this tool is missing.

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 single parameter is fully described in the schema ('Three-digit NANPA area code, e.g. "310"'), so the description need not repeat it. It adds example query strings, but no additional format or validation semantics beyond the 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 states exactly what the resource yields — serving state(s), top cities by ZIP count, total ZIP/city counts, and overlay_codes — and anchors it with concrete queries like 'where is area code 310' and 'what area code is Denver'. This clearly differentiates it from siblings such as lookup_postal_code, which handles postal-code geography rather than area-code geography.

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 explicit when-to-use triggers ('where is area code 310', sanity-checking a phone number’s geography) and a clear routing hint for city-to-code questions ('via the city’s ZIPs instead'). It does not name the sibling tool explicitly, so the agent must infer that lookup_postal_code is the alternative.

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