time_lapse_calculatorTime-Lapse Calculator (Interval, Clip, Storage) — Plan a time-lapse: enter shoot length, shot interval, playback fps, and file size to see how many frames you shoot, your clip length, and storage needed.
| Name | Required | Description | Default |
|---|---|---|---|
| fps | Yes | ||
| avgFileSizeMB | Yes | ||
| intervalSeconds | Yes | ||
| shootDurationSeconds | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses the inputs and outputs of the calculation but does not explain formulas, units, assumptions, or edge cases (e.g., rounding, limits). This is adequate for a simple calculator but lacks deeper behavioral context.
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, readable sentence that front-loads the purpose and then details inputs and outputs. It repeats the title at the start, which is slightly redundant, but the rest is efficient and informative.
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?
Given the absence of annotations and output schema, the description covers the essential input-output relationship. It describes what the user enters and what they get back. It does not specify formulas or formatting, but for a simple calculator this is reasonably complete.
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 identifies all four input types ('shoot length, shot interval, playback fps, and file size') and explains their role in producing outputs. It does not repeat exact parameter names or units, but the mapping is clear enough.
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 purpose: planning a time-lapse by calculating frame count, clip length, and storage size. It distinguishes itself from sibling calculators by naming the specific domain and outputs, using a specific actionable framing ('Plan a time-lapse').
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 makes it clear when to use the tool: when planning a time-lapse shoot. It does not explicitly mention alternatives or exclusions, but the context is strong enough for an agent to select it among the many calculator siblings.
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.