pet_food_portion_calculatorDog & Cat Food Portion Calculator — Estimate how much to feed a dog or cat from ideal weight and activity using the vet RER formula. Enter weight and food calories to get daily cups to serve.
| Name | Required | Description | Default |
|---|---|---|---|
| weightKg | Yes | ||
| foodKcalPerCup | Yes | ||
| lifestyleFactor | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full transparency burden. It discloses the calculation method (vet RER formula) and output (cups), but does not mention limitations like accuracy for all breeds, not being a substitute for veterinary advice, or the approximate nature of the estimate despite the word 'estimate'.
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 sentences long and front-loaded with the tool's name and purpose. Every sentence earns its place, providing the formula, inputs, and output without redundancy or unnecessary detail.
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 simple 3-parameter calculator with no output schema, the description covers purpose, inputs, and output sufficiently. It could be improved by explaining lifestyleFactor values or adding a disclaimer, but the core information is present for an AI agent to invoke the tool 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?
The description adds meaning for weightKg ('ideal weight') and foodKcalPerCup ('food calories'), and links lifestyleFactor to 'activity', but does not clarify the lifestyleFactor scale or units. With schema coverage at 0%, this partial compensation is helpful but leaves parameter selection ambiguous.
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 function: estimating dog/cat food portions from ideal weight and activity using the vet RER formula. It specifies the inputs (weight, food calories) and output (daily cups), distinguishing it from siblings like pet_cost_calculator or dog_age_calculator.
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 implies usage for calculating daily feeding amounts but does not explicitly compare to alternatives or provide exclusions. It lacks guidance on when not to use this tool, such as for medical conditions or life stages, and does not distinguish it from other pet-related 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.