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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds useful provenance by naming the Department for Transport and source datasets (EVCI0102, VEH0142), which helps the agent trust and contextualise the figures, but says nothing about freshness, coverage gaps, or error behavior for unknown councils.

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?

A single sentence that front-loads the resource and enumerates the returned facts without filler. The dataset-code parenthetical is slightly technical but earns its place as provenance.

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 usefully enumerates what comes back (counts, per-capita rate, rank, vehicles), which is exactly what the agent needs since no output schema exists to document the response shape.

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 the sole parameter is fully documented in the schema with example name and slug formats. The description adds no parameter-level guidance, so the baseline of 3 applies — the schema does the work.

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 names the resource (EV charging facts for a UK council area) and enumerates the specific measures returned — public/rapid chargers, chargers per 100,000, UK rank, plug-in vehicles. That is concrete enough to distinguish it from siblings like charger_size or charging_time, though the verb is implicit rather than stated.

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?

There is no guidance on when to use this tool versus alternatives, nor any stated prerequisites beyond the required council input. The agent must infer from the dataset content alone that this is a per-council statistics lookup.

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