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genpark-voice-turn-taking-endpoint-detector-skill

genpark-voice-turn-taking-endpoint-detector-skill

Official
by Alpha-Park
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

analyze_turn_status

Determine if a speaker has finished their turn or is just pausing mid-sentence by analyzing audio frame energies, elapsed silence, and transcript content.

Instructions

analyze turn status

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": {
      "frame_energies_db": "<frame_energies_db>",
      "transcript_fragment": "<transcript_fragment>",
      "elapsed_silence_ms": "<elapsed_silence_ms>"
    },
    "name": "analyze_turn_status"
  }
}

Input Schema

NameRequiredDescriptionDefault
frame_energies_dbYes
elapsed_silence_msYes
transcript_fragmentYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.1

TDQS

D1.3/5.0
Behavior1/5

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

With no annotations, the description carries the full burden of behavioral disclosure, and it provides none. It says nothing about authorization, cost, latency, side effects, or what the analysis returns.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

It is short, but the brevity reflects under-specification rather than disciplined conciseness. There is no structure, front-loading, or meaningful content to compress.

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

Completeness1/5

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

For a 3-required-parameter tool with no annotations and no output schema, the description is completely inadequate. An agent has no way to know what inputs to prepare or what the tool returns.

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

Parameters1/5

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

All three parameters (frame_energies_db, elapsed_silence_ms, transcript_fragment) have 0% schema description coverage and the description adds no explanation of their meaning, units, or expected formats. The description fully fails to compensate for the schema gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose2/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'analyze turn status' merely restates the tool name in lowercase, adding no specificity about what is analyzed (speech turns? silence? conversation state?) or what the analysis produces. It is a tautology rather than a stated purpose.

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

Usage Guidelines1/5

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

There is no indication of when to use this tool versus the siblings (calibrate_acoustic_thresholds, predict_semantic_closure, run_benchmark_turn_detection) or when not to. No context, prerequisites, or alternatives are given.

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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