turkey_cooking_time_calculatorTurkey Cooking Time & Thaw Calculator — Estimate turkey roasting time by weight, plus refrigerator and cold-water thaw times. Stuffed or unstuffed, always cook to 165°F verified with a thermometer.
| Name | Required | Description | Default |
|---|---|---|---|
| minPerLb | Yes | ||
| weightLbs | 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 adds useful context (stuffed/unstuffed, 165°F safety check) but does not explain the calculation method, output format, or edge cases. It says 'estimate' which implies approximation, but lacks detail on assumptions.
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, front-loaded, and uses an em dash for clarity. The second sentence adds critical safety information. Every word earns its place, with no wasted text.
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 is a simple calculator with no output schema, so the description should explain what the tool returns. It mentions estimating times but does not specify units (minutes/hours) or that both roasting and thaw times are provided as outputs. This leaves some ambiguity for the agent.
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%, so the description must compensate. It explains that calculation is 'by weight' and mentions stuffed/unstuffed, which hints at how minPerLb might be used. However, it does not explicitly define minPerLb as 'minutes per pound' or clarify parameter interplay beyond the schema.
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 with a specific verb ('Estimate') and resource ('turkey roasting time by weight, plus refrigerator and cold-water thaw times'). It distinguishes itself from sibling tools like brine_calculator by focusing on cooking and thaw times.
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 turkey cooking by mentioning weight and stuffed/unstuffed, but it does not explicitly state when to use this tool versus alternatives or provide exclusions. No when-not-to-use guidance is given.
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.