solar_system_size_calculatorSolar System Size Calculator (kW Needed) — Find the solar system size you need: enter yearly kWh usage, peak sun hours, and panel wattage to get the required kW and the number of panels it takes.
| Name | Required | Description | Default |
|---|---|---|---|
| derate | Yes | ||
| panelWatts | Yes | ||
| peakSunHours | Yes | ||
| annualUsageKWh | Yes | ||
| offsetFraction | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations are absent, so the description carries the full burden. It discloses the basic input-output relationship but not calculation assumptions, the role of `derate` and `offsetFraction`, or any edge-case behavior.
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 a single front-loaded sentence with no filler, but it repeats the title and crams multiple inputs/outputs into one clause. It is compact yet slightly unstructured.
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?
With five required parameters, no output schema, and no annotations, this description is insufficient. It explains only the main inputs and general output, omitting important parameter context and any return-value details.
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 0%, and the description clarifies only three of five parameters (yearly kWh usage, peak sun hours, panel wattage). It provides no meaning for `derate` or `offsetFraction`, leaving the agent without enough information to populate them correctly.
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 uses the verb 'Find' with the specific resource 'solar system size' and states the output (required kW and number of panels). It is clear but does not explicitly differentiate from the related `solar_panel_savings_calculator` sibling, so it lacks direct comparison.
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?
No guidance on when to choose this tool over other calculators, and no exclusions or alternatives are mentioned. The intended use is only implied by the phrase 'Find the solar system size you need.'
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.