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physbound

by JonesRobM
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

shannon_hartley

Calculate maximum data rate for a given bandwidth and SNR, and validate whether throughput claims exceed the Shannon limit.

Instructions

Calculate Shannon-Hartley channel capacity and validate throughput claims.

Computes the theoretical maximum data rate C = B * log2(1 + SNR) for an AWGN channel. If a claimed throughput is provided, validates it against this limit. Any claim exceeding the Shannon limit is a physical impossibility.

Use this tool when you need to:

  • Calculate maximum achievable throughput for a given bandwidth and SNR

  • Validate whether a throughput claim is physically possible

  • Determine spectral efficiency limits

  • Check if a modulation/coding scheme claim is realistic

Returns a PhysicalViolationError dict when a claim exceeds the Shannon limit.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
snr_dbNoSignal-to-noise ratio in dB. Provide this OR snr_linear.
snr_linearNoSignal-to-noise ratio (linear, not dB). Provide this OR snr_db.
bandwidth_hzYesChannel bandwidth in Hz (must be > 0)
claimed_throughput_bpsNoOptional throughput claim to validate in bits/sec

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed5 schema fields changedv0.2.1
    • addedInput schema / additionalProperties
      Added value: +false
    • addedInput schema / properties / bandwidth_hz / description
      Added value: +"Channel bandwidth in Hz (must be > 0)"
    • addedInput schema / properties / claimed_throughput_bps / description
      Added value: +"Optional throughput claim to validate in bits/sec"
    • addedInput schema / properties / snr_db / description
      Added value: +"Signal-to-noise ratio in dB. Provide this OR snr_linear."
    • addedInput schema / properties / snr_linear / description
      Added value: +"Signal-to-noise ratio (linear, not dB). Provide this OR snr_db."
  2. First observedv0.2.0

TDQS

A4.4/5.0
Behavior5/5

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

With no annotations, the description carries the full burden and meets it: it discloses the mathematical model, the validation behavior, the judgment that exceeding the Shannon limit is physically impossible, and the PhysicalViolationError dict returned in that case. This is strong behavioral disclosure for a pure calculation tool.

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 well-structured and front-loaded with the core purpose and formula. The use-case list is compact and each line adds distinct value, and the error behavior is stated efficiently. No filler.

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 calculation tool with full schema coverage and an output schema, the description is nearly complete. It covers calculation, validation, use cases, and error behavior. Minor gap: the formula writes 'SNR' without explicitly clarifying that it must be linear, though the schema's snr_linear parameter mitigates this.

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 schema already documents all parameters. The description references bandwidth, SNR, and claimed throughput, but adds little semantic detail beyond the schema. Baseline 3 is appropriate.

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 names a specific verb and resource: 'Calculate Shannon-Hartley channel capacity and validate throughput claims', and includes the governing formula. This clearly distinguishes the tool from sibling RF tools like rf_link_budget or noise_floor.

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 explicit use cases ('Use this tool when you need to:') covering calculation, validation, spectral efficiency limits, and plausibility checks. It lacks explicit exclusions or references to sibling tools, but the context is clear enough for an agent to select it appropriately.

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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