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Print Resolution & Max Print Size Calculator

print_resolution_calculator

Print Resolution & Max Print Size Calculator — Find the largest quality print from your photo pixels, or the DPI a chosen print size lands at. Enter pixel size and a target size or DPI to check the fit.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dpiYes
modeYes
pixelHYes
pixelWYes
printWidthInYes
printHeightInYes

TDQS

A3.6/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the burden of explaining behavior. It conveys the two core calculation directions ('find the largest quality print' or 'the DPI a chosen print size lands at'), but does not disclose output format, rounding, or how invalid inputs are handled. This is adequate but not rich.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise, with a useful second sentence. However, the first sentence repeats the tool title verbatim ('Print Resolution & Max Print Size Calculator'), which is redundant but not severely wasteful. Overall it is efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given six required parameters, no output schema, and no annotations, the description should explain the mode selection and what results are returned. It does not mention the 'mode' enum values or any output details, leaving significant gaps for an agent to use the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

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 'pixel size', 'target size', and 'DPI' but does not explain the 'mode' parameter, the specific parameter names (pixelW, pixelH, printWidthIn, printHeightIn), or units. This partial mapping is insufficient for six required parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states a specific verb ('Find') and resource ('largest quality print from your photo pixels, or the DPI a chosen print size lands at'). It distinguishes itself from sibling photography calculators by focusing on print resolution and print size, rather than aspect ratio or depth of field.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context: use this tool to determine max print size from pixels or DPI at a chosen size. It implicitly tells the user what inputs are needed ('Enter pixel size and a target size or DPI'), but does not explicitly mention exclusions or alternatives among the many sibling 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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TDQS

B3.1/5.0
Disambiguation2/5

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.

Naming Consistency5/5

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.

Tool Count1/5

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.

Completeness3/5

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.

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