get_charging_cost_gapHow much more public EV charging costs per mile than home charging, on HMRC's advisory electric rates.
This tool declares no required parameters. Add optional parameters from the input schema as needed.
{
"id": 1,
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"arguments": {},
"name": "get_charging_cost_gap"
}
}| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish read-only and closed-world behavior. The description adds the data source (HMRC advisory electric rates) and the per-mile public-vs-home comparison, but it does not state return units, assumptions, or output format. With annotations carrying the safety profile, this is adequate but not rich.
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 front-loaded sentence with no redundant wording. Every element (public vs home, per mile, HMRC advisory rates) contributes to defining the metric.
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 no-parameter, read-only calculation with no output schema, the description gives enough to know what is measured and the rate source. It could be more complete by stating units or output shape, but it is largely sufficient.
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 tool takes no parameters, so the schema cannot add parameter meaning; the baseline for zero params is 4. The description appropriately focuses on the output metric rather than inventing parameter detail.
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 a precise metric—per-mile cost difference between public and home EV charging on HMRC advisory rates—so the resource and comparison are clear. It does not use an explicit verb or name the relevant sibling (e.g., get_cost_to_charge_at_home), which keeps it just short of top marks.
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 when-to-use or when-not-to-use guidance, and no alternative tools are named. The metric implies a comparison use case, but the agent must infer it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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