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radar_range

Calculate maximum monostatic radar detection range and validate whether claimed ranges are physically achievable.

Instructions

Calculate maximum monostatic radar detection range and validate range claims.

Computes the radar range equation R_max = [P_t * G^2 * lambda^2 * sigma / ((4*pi)^3 * S_min * L)]^(1/4) for a monostatic radar (same antenna for transmit and receive). Validates that claimed detection ranges do not exceed the theoretical maximum. Catches the common fourth-root fallacy where LLMs incorrectly state that doubling transmit power doubles radar range (it only increases range by a factor of 2^(1/4) = 1.19x).

Use this tool when you need to:

  • Calculate the maximum detection range of a radar system

  • Validate whether a claimed radar detection range is physically achievable

  • Determine minimum detectable signal power for a radar receiver

  • Check if radar performance claims account for the R^4 path loss

  • Verify that RCS assumptions are reasonable for the target class

Returns both human-readable summary and machine-readable JSON with all intermediate values. Returns a PhysicalViolationError dict if any input violates physics or the claimed range exceeds R_max.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
rcs_m2YesRadar cross section of the target in m^2 (must be > 0)
losses_dbNoTotal system losses in dB (default: 0)
min_snr_dbNoMinimum required SNR in dB for detection (default: 13 dB, Swerling I)
num_pulsesNoNumber of integrated pulses for integration gain (default: 1)
frequency_hzYesOperating frequency in Hz (must be > 0)
peak_power_wYesPeak transmit power in watts (must be > 0)
claimed_range_mNoOptional claimed detection range to validate against R_max (meters)
antenna_gain_dbiYesAntenna gain in dBi (same antenna for TX and RX)
noise_bandwidth_hzNoReceiver noise bandwidth in Hz (default: 1 MHz)
system_noise_temp_kNoSystem noise temperature in Kelvin (default: 290K)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed11 schema fields changedv0.2.1
    • addedInput schema / additionalProperties
      Added value: +false
    • addedInput schema / properties / antenna_gain_dbi / description
      Added value: +"Antenna gain in dBi (same antenna for TX and RX)"
    • addedInput schema / properties / claimed_range_m / description
      Added value: +"Optional claimed detection range to validate against R_max (meters)"
    • addedInput schema / properties / frequency_hz / description
      Added value: +"Operating frequency in Hz (must be > 0)"
    • addedInput schema / properties / losses_db / description
      Added value: +"Total system losses in dB (default: 0)"
    • addedInput schema / properties / min_snr_db / description
      Added value: +"Minimum required SNR in dB for detection (default: 13 dB, Swerling I)"
    • addedInput schema / properties / noise_bandwidth_hz / description
      Added value: +"Receiver noise bandwidth in Hz (default: 1 MHz)"
    • addedInput schema / properties / num_pulses / description
      Added value: +"Number of integrated pulses for integration gain (default: 1)"
    • addedInput schema / properties / peak_power_w / description
      Added value: +"Peak transmit power in watts (must be > 0)"
    • addedInput schema / properties / rcs_m2 / description
      Added value: +"Radar cross section of the target in m^2 (must be > 0)"
    • addedInput schema / properties / system_noise_temp_k / description
      Added value: +"System noise temperature in Kelvin (default: 290K)"
  2. First observedv0.2.0

TDQS

A4.7/5.0
Behavior5/5

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

With no annotations present, the description carries the full burden and succeeds: it discloses the monostatic same-antenna assumption, the fourth-root relationship, return formats (human-readable plus machine-readable JSON), and the PhysicalViolationError dict behavior on invalid physics or exceeded ranges. This is far more behavioral detail than is typical.

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 front-loaded with the purpose and formula, followed by a compact bulleted use-case list and a brief return/error summary. It is moderately long but every segment earns its place, and there is no filler.

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?

For a 10-parameter physics-heavy tool with a nullable validation input, the description supplies the equation, assumptions, use cases, return behavior, and error handling. Since an output schema exists, it correctly avoids over-explaining return fields, leaving no major gap for correct invocation.

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?

Input schema covers all 10 parameters at 100%, so the baseline is 3. The description adds value by showing how parameters combine in the governing equation and explicitly linking claimed_range_m to the validation behavior, plus explaining integration gain and Swerling I defaults. Most individual parameter semantics remain in the schema, but the equation adds meaningful physical context.

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 opening sentence states a specific action and resource: 'Calculate maximum monostatic radar detection range and validate range claims.' It also differentiates the tool from siblings by anchoring it to the radar range equation and fourth-root fallacy, making its scope unmistakable.

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 an explicit 'Use this tool when you need to' list with concrete scenarios such as validating claimed ranges and checking R^4 path loss. However, it does not explicitly mention sibling tools like rf_link_budget or state when not to use this tool, so it falls just short of full routing guidance.

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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