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Betaflight MCP

Betaflight MCP

by bvandevliet
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TypeScript
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set_feedforward_averaging

Reduce RC link quantization noise by averaging stick input samples before feedforward calculation. Select OFF for snappiest response or 4_POINT for maximum smoothing.

Instructions

Set feedforward_averaging: Averages stick input samples before FF calculation to smooth out RC link quantization. OFF = no averaging (snappiest); 4_POINT = heaviest smoothing. [UINT8, default: 2_POINT]

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": {
      "value": "<value>"
    },
    "name": "set_feedforward_averaging"
  }
}

Input Schema

NameRequiredDescriptionDefault
valueYesValue for feedforward_averaging (UINT8)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0-alpha

TDQS

A3.8/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the effect on smoothing but does not disclose side effects, persistence, whether changes take effect immediately or require a reboot, or any limitations. This is minimal transparency for a mutation 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 extremely concise: two short sentences plus a default note. Every sentence is informative and earns its place. The purpose is front-loaded, and the structure is efficient.

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 simple parameter-setter with one value, the description covers the purpose, the possible values, their effects, and the default. However, it omits details like whether the change requires a restart or if there are any prerequisites. Given no output schema and no annotations, it is fairly complete but not fully exhaustive.

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?

Although the schema already describes the parameter (100% coverage), the description adds significant value by listing the allowed values (OFF, 2_POINT, 4_POINT), explaining their numerical representation (UINT8), and clarifying the behavioral effect of each option (no averaging vs. heaviest smoothing) as well as the default. This goes beyond what the schema provides.

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 clearly states the tool sets feedforward averaging, explains its function (smooths RC link quantization by averaging stick input samples), and lists the possible values (OFF, 4_POINT) with their effects, including the default (2_POINT). It is a specific verb+resource combination that distinguishes itself from its get_ sibling and other set_ tools.

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 explains the trade-off between 'snappiest' (OFF) and 'heaviest smoothing' (4_POINT), which implicitly guides usage, but it does not provide explicit when-to-use or when-not-to-use guidance, nor does it mention alternative tools or contexts where this setting is appropriate.

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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