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FASTMEAN MCP

FASTMEAN MCP

by maxewka

fastmean_evaluate

Evaluates native post-analysis functions and FFT expressions for selected signals, returning actual native summary or FFT tables instead of fabricated transient data.

Instructions

Evaluate native post-analysis functions or the native FASTMEAN FFT.

signals may use last/AVG/RMS or native harmonic functions. FFT mode uses explicit fft settings and FFT expressions; a separate native time writer proves the simulated interval. Returns the actual native summary/FFT table, not a fabricated transient time axis. No arbitrary script is accepted.

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": {
      "path": "<path>",
      "signals": "<signals>"
    },
    "name": "fastmean_evaluate"
  }
}

Input Schema

NameRequiredDescriptionDefault
epsNo
fftNo
pathYes
stepNo
stopNo
signalsYes
timeoutNo
overridesNo
definitionsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.6.0

TDQS

B3.1/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=false, destructiveHint=false, openWorldHint=false, and idempotentHint=false. The description adds meaningful behavioral context by stating that no arbitrary script is accepted and that the tool returns the actual native summary/FFT table rather than a fabricated transient time axis.

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?

The description is reasonably compact and front-loads the core purpose before adding mode-specific details. The sentence about a 'separate native time writer' is slightly opaque, but there is little wasted text.

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

Completeness2/5

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

For a tool with nine parameters, no schema descriptions, and no output schema, the description is incomplete. It explains the return object at a high level and some mode semantics, but omits most parameter meanings and does not give enough guidance to select this tool confidently over siblings.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must carry the burden for nine parameters. It gives partial semantics for signals (last/AVG/RMS or harmonic functions) and fft mode settings, but says nothing about path, eps, step, stop, timeout, overrides, or definitions.

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 and resource: evaluating native post-analysis functions or the native FASTMEAN FFT. It distinguishes signal-mode evaluation from FFT-mode evaluation, but does not explicitly differentiate this tool from siblings like fastmean_spectrum or fastmean_harmonics.

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

Usage Guidelines2/5

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

No explicit when-to-use guidance, prerequisites, or alternatives are provided. The mention of signal modes and FFT mode implies different usage contexts, but the agent is left to infer when this tool should be selected over sibling tools such as fastmean_spectrum or fastmean_transient.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.