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ethan-hub26

embedcalc-mcp

by ethan-hub26

E-Series Nearest Standard Value

embedcalc_eseries_nearest
Read-onlyIdempotent

Snap a computed electronic component value to the nearest E-series standard value and get the resulting percentage error. Choose from E6, E12, E24, or E96 series.

Instructions

Snap a computed resistor/capacitor/inductor value to the nearest purchasable E-series standard value (E6/E12/E24/E96) with the resulting error.

Args: value (any unit — ohms, farads, henries), series ('E24' default; E96 for 1% resistors). Returns (structured): { standard, error_percent, series }. Example: 4.67e-6 H, E24 -> 4.7e-6 (+0.64%). LLMs frequently pick non-existent "standard" values — always snap computed values with this tool before choosing a part.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
valueYesComputed value in base units (ohms/farads/henries)
seriesNoE-series (E24=5%, E96=1% resistors)E24

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
seriesYes
standardYes
error_percentYes
Behavior5/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds further detail: 'Returns (structured): { standard, error_percent, series }' and an example showing output. No contradictions. The description provides all necessary behavioral context beyond the annotations.

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

Conciseness5/5

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

The description is succinct: three sentences covering purpose, arguments, return structure, example, and usage note. Front-loaded with the core action. No unnecessary words.

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

Completeness5/5

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

Given the simplicity of the tool (2 parameters, output schema exists), the description is complete. It explains what the tool does, when to use it, how parameters map, what output looks like, and includes a practical example. Nothing is missing.

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

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds value beyond schema: 'value (any unit — ohms, farads, henries)' clarifies unit flexibility, and 'series ('E24' default; E96 for 1% resistors)' explains the enum meaning. This extra context helps the agent use parameters correctly.

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 the function: 'Snap a computed resistor/capacitor/inductor value to the nearest purchasable E-series standard value.' This is a specific verb-resource pair and distinguishes the tool from sibling tools like embedcalc_find_component or embedcalc_resistor_code.

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 includes explicit guidance: 'LLMs frequently pick non-existent standard values — always snap computed values with this tool before choosing a part.' This tells the agent when to use the tool (after computation, before part selection) and implies not to use it when already having a standard value. While it does not list alternatives, the context is clear.

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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