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waveform-MCP

waveform-MCP

by jarmstrong158
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

automation_clear

Clears automation lanes from a track or a specific target, removing all automation data.

Instructions

Remove all automation lanes from a track, or just the lane on a given target.

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": {
      "track_id": "<track_id>"
    },
    "name": "automation_clear"
  }
}

Input Schema

NameRequiredDescriptionDefault
targetNoOptional — if omitted, clears all lanes
track_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.7/5.0
Behavior2/5

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

No annotations are present, so the description carries the full burden. It states the removal action but does not disclose that this is destructive, whether it is undoable, or whether it affects anything beyond automation lanes. This is a meaningful gap 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?

A single sentence conveys the full action and the optional scoping with no wasted words. The key behavior is front-loaded.

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

Completeness3/5

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

For a simple two-parameter destructive tool, the description covers the core behavior but omits details such as what values target accepts, return behavior, and safety notes. Since no annotations or output schema exist, some additional context would be helpful.

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?

The schema describes target as optional and track_id as a bare string. The description adds meaning by explaining that target selects a single lane and omission clears all lanes, and that track_id refers to the containing track. This goes beyond the schema's minimal coverage.

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 uses a specific verb ('Remove') and resource ('automation lanes') and clearly distinguishes the scoped vs. full-clearing behavior. This differentiates it from siblings like automation_list or automation_add, which do different operations.

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 implies when to use the tool: whenever automation lanes need to be removed. However, it does not explicitly compare against alternatives or state when not to use it, leaving usage to inference.

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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