ScienceShot science reference

Photodiode responsivity calculator

photodiode_responsivity

Read-onlyIdempotent

Converts external quantum efficiency and responsivity using CODATA 2018 h, c and e, and computes photocurrent and unity-gain shot noise following Hamamatsu. Returns dark and illuminated shot-noise NEP, sourced Si/Ge/In0.53Ga0.47As bandgap thresholds and LED photon energy; excludes thermal, amplifier and excess noise.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "photodiode_responsivity"
  }
}

Input Schema

Name	Required	Description	Default
`qe`	No	External quantum efficiency (0 to 1)
`mode`	No	Input mode	qe
`dark_a`	No	Dark current (A)
`power_w`	No	Optical power (W)
`material`	No	Bandgap reference material	si
`bandwidth_hz`	No	Noise bandwidth (Hz)
`wavelength_nm`	No	Wavelength (nm)
`responsivity_aw`	No	Measured responsivity (A/W)

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 7, 2026

TDQS

A3.6/5.0

Behavior4/5

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

Annotations already establish read-only, idempotent, non-destructive behavior. The description adds meaningful domain behavior beyond annotations: CODATA 2018 constants, Hamamatsu methodology, returned dark/illuminated NEP, and explicit exclusion of thermal, amplifier, and excess noise.

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?

Two sentences, front-loaded with the core conversion and computation; no filler. The second sentence is dense and slightly ambiguous in its 'sourced ...' construction, but overall appropriately sized.

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

Completeness3/5

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

With no output schema, the description must explain return values; it lists NEP, bandgap thresholds, and LED photon energy, but leaves units and exact return structure ambiguous. For an 8-parameter calculator, this is adequate but not fully self-contained.

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

Parameters3/5

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 some domain context (QE/responsivity conversion, material bandgaps, dark-current noise) but does not explicitly explain how `mode` switches between `qe` and `responsivity_aw` or map other parameters to calculations.

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

Purpose4/5

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

The description uses specific verbs ('Converts', 'computes', 'Returns') and names the physical quantities (external quantum efficiency, responsivity, photocurrent, shot-noise NEP), making the tool's function clear. It does not, however, contrast itself with siblings like detector_signal or photon_energy, so sibling differentiation is missing.

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

Usage Guidelines3/5

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

There is no explicit when-to-use or when-not-to-use guidance, nor mention of alternatives such as photon_energy. The description only implies usage by listing the calculations it performs, leaving the agent to infer the context.

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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