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voice-analysis-mcp

voice-analysis-mcp

by Patience-dot-devl
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

render_waveform

Create waveform images showing per-channel audio activity to identify silences, speaker turns, clipping, and level imbalance in recordings.

Instructions

Render a waveform image (max 600s), one row per channel.

On stereo call recordings the two rows show each speaker's activity at a glance — useful for spotting long silences, who talks when, clipping, and level imbalance between sides.

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": "render_waveform"
  }
}

Input Schema

NameRequiredDescriptionDefault
pathYes
end_timeNo
start_timeNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3/5.0
Behavior2/5

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

Discloses max duration and per-channel rows, but lacks details on error handling (e.g., what happens if duration exceeds 600s), output format (e.g., PNG, dimensions), supported file types, or performance implications. No annotations provided.

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?

Two sentences, front-loaded with core action and constraint. Second sentence adds value with example use cases, but could be more concise by integrating key details about parameters.

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?

Given the absence of output schema and annotations, description should cover return format, error behavior, and parameter usage. It only partially addresses duration limits and channel layout, leaving significant gaps for an agent to use correctly.

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?

Does not explain the three parameters (path, start_time, end_time) beyond what is in the schema. Schema coverage is 0%, so description should clarify their purpose and defaults, but it omits any parameter-specific guidance.

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?

Clearly states the tool renders a waveform image with specific constraints (max 600s, one row per channel). Distinguishes from sibling tools like render_spectrogram by focusing on waveform visualization.

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?

Describes use cases (spotting silences, clipping, level imbalance) and implies utility for stereo recordings, but does not explicitly say when not to use it or mention alternatives like render_spectrogram for frequency analysis.

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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