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voice-analysis-mcp

voice-analysis-mcp

by Patience-dot-devl
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

analyze_conversation

Measure conversation dynamics from audio: track talk time, turns, interruptions, dead air, and overlap per speaker to identify dominance and response patterns.

Instructions

Measure conversation dynamics: talk time per channel, turns, dead air, overlap.

On stereo call recordings (one speaker per channel) this also reports
interruption counts per side, overlap ratio, and response latency —
who dominates, who interrupts, how quickly each side responds. On mono
audio only overall speech/silence dynamics are available. Cheap to run
on a whole recording; a good early step for call review.

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>"
    },
    "name": "analyze_conversation"
  }
}

Input Schema

NameRequiredDescriptionDefault
pathYes
end_timeNo
start_timeNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.9/5.0
Behavior4/5

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

With no annotations, the description carries the transparency burden. It reveals that the tool is cheap to run, and clarifies that stereo recordings yield interruption counts while mono only provides overall dynamics. No destructive or restricted behaviors are hinted at, which is reasonable given the analysis nature.

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 front-loaded with the core purpose in the first sentence, followed by additional details. It avoids redundancy but could be slightly more concise; the second paragraph could be merged with the first. Overall, it efficiently conveys key information.

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?

Given no output schema, the description lists expected metrics (talk time, turns, etc.) but does not specify return format or structure. It also omits parameter semantics. For a tool with 3 parameters and moderate complexity, it is reasonably complete but has gaps.

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%, and the description does not mention any of the three parameters (path, start_time, end_time). It adds no meaning about required input or optional time constraints, leaving the agent to infer parameter usage from context alone.

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 'Measure conversation dynamics: talk time per channel, turns, dead air, overlap', specifying the precise verb and resource. It distinguishes between stereo and mono capabilities, and its focus on conversation dynamics differentiates it from siblings like 'analyze_prosody' and 'analyze_quality'.

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 notes 'Cheap to run on a whole recording; a good early step for call review', providing context on when to use it. However, it does not explicitly exclude cases or compare with alternatives for similar tasks, falling short of full usage guidance.

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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