puppy_weight_calculatorPuppy Adult Weight Predictor — Predict your puppy adult weight from its current weight and age in weeks, with a size-class note. Enter the numbers to see the grown-up size to expect.
| Name | Required | Description | Default |
|---|---|---|---|
| ageWeeks | Yes | ||
| sizeClass | No | ||
| currentWeightKg | 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 mentions the inputs and that a size-class note is included, but does not disclose that predictions are estimates, any assumptions about breed, or the role of the optional sizeClass parameter. It adds some context beyond the tool name but lacks caveats.
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, front-loaded with the purpose and a brief 'Enter the numbers' instruction. It is concise with no unnecessary words, though the second sentence adds minimal value beyond the first.
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 calculator with no output schema, the description covers the main purpose and output type (grown-up size and size-class note). However, it omits the unit for weight, the optional sizeClass input, and any information about accuracy or limitations, leaving some ambiguity.
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 schema has no descriptions (0% coverage), so the description must compensate. It mentions current weight and age in weeks but does not specify the unit for weight (kg) even though the parameter is named currentWeightKg. It also omits the optional sizeClass parameter entirely, leaving the agent without guidance on when to provide it.
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 the tool predicts a puppy's adult weight from its current weight and age, with a size-class note. This is a specific verb (predict) and resource (puppy adult weight), clearly distinguishing it from sibling calculators like dog_age_calculator or ideal_weight_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 clearly indicates when to use the tool: when you have a puppy's current weight and age in weeks and want to predict its adult weight. It does not explicitly compare with alternatives or state exclusions, but the context is unambiguous.
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.
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