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assess

Evaluates a speech recording against a reference text, providing word-level phoneme feedback, prosody analysis, and alignment. Without reference, returns transcript and prosody.

Instructions

Assess the last recording (or a specific audio file) without re-recording.

When reference_text is provided, the assessor:

  • Aligns the user's speech to the reference word-by-word (Needleman-Wunsch; single deletions/insertions no longer cascade into phantom substitutions).

  • Runs wav2vec2 CTC forced alignment to verify which reference words the user actually produced — mitigates Whisper-bias mistranscriptions on rare proper nouns and domain terms by checking acoustic evidence against the reference directly.

  • Surfaces per-word phoneme-level feedback (expected vs produced IPA, weak phonemes) from CMUdict.

  • Surfaces learner-profile pronunciation hints and drills. The bundled rule pack currently includes Korean-L1 patterns such as r/l, th→s, final cluster deletion, and intrusive onset vowel.

  • Adds prosody notes: word-stress placement, sentence-final rising intonation on declaratives, intra-clause hesitation pauses.

Without a reference, only the transcript and prosody run.

Args: reference_text: Expected text the user was trying to say (optional). audio_path: Path to a WAV file. Uses the last recording if not specified.

Returns: Detailed pronunciation assessment report (markdown).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
reference_textNo
audio_pathNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

Despite no annotations, the description is highly transparent, detailing algorithms (Needleman-Wunsch, wav2vec2 CTC), bias mitigation, phoneme feedback, learner profiles, and prosody notes. It fully discloses behavioral traits.

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?

The description is well-structured with bullet points, each providing unique information. It front-loads the main purpose and is appropriately sized for the tool's complexity.

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

Completeness5/5

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

Given the tool has an output schema, the description adequately covers inputs and output format (markdown report). It is complete for agent invocation, covering optional parameters and behavior differences.

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

Parameters5/5

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

The description adds significant meaning beyond the schema (which has 0% coverage). It explains reference_text as expected text and audio_path as optional WAV path defaulting to last recording, providing context and usage examples.

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 the tool assesses the last recording or a specific audio file without re-recording, using the verb 'assess' and resource 'audio'. It distinguishes from sibling tools like 'record' and 'practice' by focusing on evaluation rather than creation or interactive practice.

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 explains when to use reference_text (for alignment and phoneme feedback) and what happens without it (only transcript and prosody). However, it does not explicitly exclude usage scenarios or mention alternative tools like 'converse' or 'check_mic'.

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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