dog_age_calculatorDog Age Calculator (Human Years, Size-Adjusted) — Convert your dog age into human years the AKC way, adjusted for body size — not the old myth of multiplying by seven. Enter age and size for a real estimate.
| Name | Required | Description | Default |
|---|---|---|---|
| age | Yes | ||
| sizeClass | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses that the tool uses the AKC size-adjusted method rather than the multiply-by-seven myth, which is useful behavioral context. However, it does not describe the output format, assumptions, or limitations (e.g., age range or size class meanings), and no annotations are provided to cover these aspects.
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 concise and front-loaded, but the first clause repeats the tool title verbatim. The second sentence is a bit redundant but adds a direct call to action. Overall, it is efficient with minimal waste.
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, the description provides the core purpose and method, but it lacks details about the return value, output format, or edge-case behaviors. Given no output schema and no annotations, some additional context would be helpful.
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 0% description coverage, so the description must compensate. It mentions 'age' and 'size' but does not explicitly define age units or explain the sizeClass enum values. However, the parameter names and enum are self-explanatory, and the 'size-adjusted' concept adds context.
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: converting dog age to human years using the AKC method with size adjustments. It explicitly distinguishes itself from the common 'multiply by seven' myth, making it specific and different from sibling calculators like puppy_weight_calculator or pet_cost_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 by saying 'Enter age and size for a real estimate,' but it does not explicitly state when to choose this tool over alternatives or mention any exclusions. The context is clear but not explicitly guided.
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.