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

Annotations already establish it as a safe, idempotent, open-world read, so the description only needs to add context. It adds temporal coverage (2008 onward, annual), the semantic distinction between license holders and registered vehicles, and the fact that statewide totals and ID-card counts come back alongside county rows. Missing only return shape/pagination nuance, which the annotations don't cover but which is minor here.

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?

Front-loads the operation and scope, then routes to example questions and the sibling tool. Multi-sentence but each sentence carries information; only slight redundancy in restating the count semantics.

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?

Despite having no output schema, the description discloses what comes back (county counts, statewide total, ID card count) plus the time coverage and the person-vs-vehicle distinction. An agent has everything needed to call and interpret this correctly.

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% and each parameter already documents its format and defaults, so the baseline is 3. The description adds no parameter-level syntax or semantics beyond what the schema states (year range, limit cap, substring matching).

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?

States a specific verb and resource ('Count driver licenses outstanding in California by county of residence') with scope (annual series from 2008 onward) and even the output contents (statewide total, ID card count). It explicitly distinguishes itself from the sibling ca_dmv_vehicle_registrations, so an agent can route correctly without opening a schema.

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?

Provides concrete question exemplars ('how many licensed drivers are in Los Angeles County', 'which county has the most', trend over time) and names the alternative tool with the condition that selects it (people vs. vehicles). Nothing is left to inference.

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