ev_charging_cost_calculatorEV Charging Cost Calculator — See what it really costs to charge your EV at home or on public chargers. Enter battery size, charge window, and rates for cost per charge and per mile.
| Name | Required | Description | Default |
|---|---|---|---|
| homeRate | Yes | ||
| targetPct | Yes | ||
| batteryKWh | Yes | ||
| currentPct | Yes | ||
| efficiency | Yes | ||
| publicRate | Yes | ||
| milesPerKWh | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It states the core behavior (computing charging costs) and the outputs (cost per charge and per mile), but it doesn't explain calculation assumptions, units, or limitations (e.g., charging losses, efficiency factors). It adds some context but not rich detail.
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 two concise sentences, front-loaded with the tool's title and immediately followed by actionable guidance on inputs and outputs. Every word earns its place with no redundancy.
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?
The tool has 7 required parameters and no output schema. The description high-levelises the key outputs but doesn't explain all inputs, their relationships, or calculation assumptions. It provides a decent overview but is incomplete for a tool of this complexity.
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 7 parameters with 0% description coverage. The description maps some parameters (battery size to batteryKWh, charge window to currentPct/targetPct, rates to homeRate/publicRate) but omits efficiency and milesPerKWh entirely. It also doesn't clarify units or how rates are used, so the compensation for the coverage gap is insufficient.
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 clearly states the tool's purpose: to calculate EV charging costs at home or on public chargers. It uses a specific verb ('See what it really costs') and resource (EV charging), and differentiates itself from sibling calculators like electricity_cost_calculator by focusing on EV-specific scenarios and outputs (cost per charge and per mile).
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?
The description provides clear context for when to use the tool: when calculating EV charging costs at home or on public chargers. It doesn't explicitly mention alternatives or exclusions, but the EV-focused framing makes its usage context obvious compared to generic cost calculators.
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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Many calculators occupy overlapping conceptual spaces, such as 'ai_roi_calculator' vs 'ai_automation_payback_calculator' and 'llm_self_host_vs_api_calculator' vs 'ai_build_vs_buy_calculator'. The boundaries between debt payoff, savings goal, and drawdown tools are also fuzzy, making it easy for an agent to select the wrong tool despite detailed descriptions.
Every tool follows the same <topic>_calculator pattern with lowercase snake_case, making the naming highly predictable and consistent. Even acronyms and numbers fit the pattern, so there is no mixing of conventions.
122 tools is an extreme number for a single MCP server, far exceeding the 50+ threshold for a severe mismatch. The tools span unrelated domains like AI costs, pet food, concrete, pizza dough, and turkey cooking, creating an unfocused kitchen-sink surface that overwhelms an agent's selection process.
The set covers many common calculator categories such as finance, construction, health, and AI costs, but several staple calculators are missing (e.g., BMI, tip, discount, simple interest, currency conversion). The AI cost cluster is over-saturated while other everyday calculations are absent, leaving minor but noticeable gaps.