ScienceShot science reference

Semiconductor bandgap and carriers

semiconductor_carriers

Read-onlyIdempotent

Calculate Varshni bandgap, intrinsic and doped equilibrium carriers, Fermi level above the valence edge and conductivity for Si, Ge or GaAs. Assumes fully ionized dopants and non-degenerate statistics; conductivity uses fixed 300 K low-field mobilities and the result flags degeneracy.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "material": "<material>",
      "temperature_k": "<temperature_k>",
      "net_doping_cm3": "<net_doping_cm3>"
    },
    "name": "semiconductor_carriers"
  }
}

Input Schema

Name	Required	Description
`material`	Yes	si, ge or gaas.
`temperature_k`	Yes	Temperature, 100 to 600 K.
`net_doping_cm3`	Yes	Signed ND minus NA in cm-3, between -1e20 and 1e20.

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 7, 2026

TDQS

A3.8/5.0

Behavior4/5

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

Annotations already declare read-only, idempotent, non-destructive, and closed-world behavior. The description adds substantive model assumptions beyond that: fully ionized dopants, non-degenerate statistics, fixed 300 K low-field mobilities for conductivity, and degeneracy flagging in the result. It does not describe error handling or the exact return shape, but the added context is meaningful.

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?

Two tightly written sentences front-load the computed outputs and then qualify the model assumptions. Every clause earns its place with no redundant or filler wording.

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

Completeness4/5

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

For a three-parameter calculator with no output schema, the description names the key returned quantities and the validity assumptions, which is enough to call it correctly. Minor gaps remain around return units and the exact meaning of the degeneracy flag, but the definition is substantially complete.

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 description coverage is 100%, so the input schema already documents material, temperature range, and doping sign/range. The description reinforces the material set (Si, Ge, GaAs) but adds no new syntax or format details beyond what the schema provides, making the baseline 3 appropriate.

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 states a specific verb ('Calculate') and enumerates the exact quantities returned: Varshni bandgap, intrinsic and doped carriers, Fermi level, and conductivity, for three named materials. It is clear what the tool does, but it does not distinguish itself from siblings like material_properties or other semiconductor calculators.

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?

The description gives applicability conditions ('Assumes fully ionized dopants and non-degenerate statistics') and notes that degeneracy is flagged, which helps an agent decide when the model is valid. However, it offers no explicit when-to-use guidance against alternatives or when-not-to-use cases.

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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