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assess_urgency

Determine urgency level of speech audio by analyzing prosodic features like speaking rate, volume, pitch, and pauses. Provides score, level, and reasoning.

Instructions

Assess urgency level from audio.

This tool evaluates the urgency level of speech based on prosodic features like speaking rate, volume, pitch variation, and pause patterns.

Args: audio_path: Path to the audio file (WAV format supported) text: Optional transcription text for keyword-based urgency detection

Returns: Dictionary containing: - score: Urgency score (0.0 to 1.0) - level: Urgency level ("low", "medium", "high", "critical") - reasoning: List of factors contributing to the urgency assessment - factors: Detailed breakdown of contributing factors

Example: { "score": 0.75, "level": "high", "reasoning": ["Fast speaking rate detected", "High volume variation"], "factors": { "speaking_rate": "fast", "volume_level": "high", "pitch_variation": "high", "pause_pattern": "few_pauses" } }

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
audio_pathYes
textNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

With no annotations provided, the description carries the full burden. It describes the analysis and return values but does not disclose behavioral traits such as required permissions, rate limits, or side effects. The description is functional but lacks deeper behavioral context.

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 purpose and includes a structured docstring with parameter details and an example. It is slightly lengthy but each sentence adds value, though some redundancy could be trimmed.

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?

Given the presence of an output schema (inferred), the description covers inputs and outputs well, including an example. However, it does not discuss how this tool relates to sibling tools, which would enhance completeness.

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 coverage is 0%, so the description compensates by explaining audio_path as a WAV file path and text as an optional transcription. This adds meaningful context beyond the basic type information in the schema.

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 that the tool assesses urgency from audio based on prosodic features, distinguishing it from siblings like analyze_audio or detect_sarcasm through the specific focus on urgency and prosodic analysis.

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 usage for urgency assessment but does not explicitly state when to use this tool over alternatives like analyze_audio or full_analysis, nor does it provide conditions for when not to use it.

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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