vocametrix

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool targets a distinct function or metric, from acoustic analysis to therapy planning. Even similar tools like assess_pronunciation and assess_pronunciation_with_pitch are clearly differentiated by the additional pitch feature. No overlapping purposes.
Naming Consistency5/5
All tools follow the 'vocametrix_verb_noun' snake_case pattern. Verbs like calculate_, assess_, generate_, get_ are used consistently for similar operations. No mixing of conventions.
Tool Count3/5
39 tools is high, reflecting a broad domain. While each tool is focused, some could be combined (e.g., multiple calculate_ tools under a single analyze_voice_quality). Still within acceptable range for a comprehensive server.
Completeness5/5
Covers the full speech therapy workflow: assessment, diagnosis, plan generation, exercises, transcription, synthesis, and tutoring. No obvious gaps for the stated domain.
Average 3.9/5 across 39 of 39 tools scored. Lowest: 2.9/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- Last stable release on May 10, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must fully disclose behavioral traits. It states the tool aggregates specific acoustic metrics but does not clarify if it is a read-only operation, whether it modifies data, or what error conditions exist. This leaves significant behavioral gaps.
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 two sentences with no redundant words. It front-loads the purpose and lists key aggregated metrics efficiently, earning its place with high information density.
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?
Despite listing many metrics, the description lacks context for interpreting the ABI (e.g., output range, clinical meaning) and does not explain return values. Among many sibling calculate tools, more detail on when to use ABI over others would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema provides 100% coverage with descriptions for both 'connectedSpeechPath' and 'sustainedVowelPath'. The description mentions these inputs but does not add semantic detail beyond the schema, meeting the baseline for full schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool calculates the Acoustic Breathiness Index (ABI) and specifies it combines connected speech and sustained vowel. However, it does not explicitly differentiate from sibling tools like vocametrix_calculate_avqi or vocametrix_calculate_dsi, which also compute voice indices.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like AVQI or DSI. The description lists aggregated metrics but does not explain the specific context or clinical purpose that distinguishes ABI from other calculations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, and the description lacks any mention of side effects, permissions, or whether the tool modifies data. It only describes the calculation, leaving behavioral traits undisclosed.
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?
Two sentences that efficiently convey purpose, reference range, and input requirements. No wasted words.
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?
Without output schema, the description omits return format. Given many sibling tools, it could provide more context on when DSI is appropriate. It adequately describes inputs but leaves completeness gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 86%, and the description adds context for some parameters (e.g., MPT meaning). However, it mentions 'F0 range' while the schema has 'maximumF0' only, causing minor ambiguity. Overall, it adds moderate value beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool calculates DSI and provides reference ranges. It distinguishes from siblings by naming the specific index, but doesn't explicitly contrast with other voice assessment tools.
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 lists required inputs (WAV file, MPT, F0 range, minimum intensity) but does not specify when to use this tool over other voice metrics or provide exclusions. Usage is implied but not explicitly guided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It adds behavioral context by noting the use of Azure neural TTS, return of timing data, and support for all voice names per locale. However, it lacks details on synchronous/asynchronous behavior, error handling, or rate limits.
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 extremely concise with only two sentences, yet it conveys the core function, output type, and a key feature (voice name support). No fluff or redundant 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 and no annotations, the description provides the essential purpose and outputs but lacks important context such as error conditions, prerequisites, or differentiation from the sibling tool that also returns timing data. Adequate but incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already explains each parameter. The description adds minimal extra value, only noting that all Azure Neural voice names are supported for the locale. Baseline 3 is appropriate as the description does not significantly enhance parameter understanding beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: 'Synthesize speech from text using Azure neural text-to-speech.' It also specifies outputs: audio URL and word-level timing data. However, it does not differentiate from the sibling tool 'vocametrix_synthesize_speech_with_timing,' which may cause confusion about their distinct purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives, such as the similar sibling or other synthesis tools. The description only states what the tool does, leaving the agent without contextual usage advice.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses the purpose and typical values but does not mention side effects, permissions, output format, or any behavioral constraints beyond calculation.
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 two concise sentences with no filler. The first sentence front-loads the purpose, and the second provides useful interpretive context.
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 adequately explains input and interpretive value, but lacks details on output format and behavioral context (e.g., side effects, prerequisites).
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, and the description adds interpretive value (normal range, clinical sensitivity) but does not add parameter-level details beyond what the schema already provides.
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 it calculates Cepstral Peak Prominence (CPP) from a sustained vowel. It is distinct from other sibling calculate_* tools by specifying the exact metric and input.
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?
Usage is implied by the metric's name and clinical context (e.g., breathiness, hoarseness), but the description does not explicitly guide when to use this tool versus alternatives like calculate_hnr or calculate_avqi.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose all behavioral traits. It only explains the output interpretation but fails to mention any side effects, file system interactions, or required audio properties (e.g., sample rate, duration). This leaves significant behavioral gaps.
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 two sentences, front-loading the core action and adding interpretive context. Every word serves a purpose, with no redundancy or filler.
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?
For a simple two-parameter calculation tool with no output schema, the description covers the essential purpose and output interpretation. Missing behavioral details (e.g., whether it modifies files) would be useful but are not critical for a read-only-like compute operation. The completeness is adequate for the tool's complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 50% (only sustainedVowelPath has a brief description). The description does not add meaning for patientGender or clarify format expectations for the path. It adds no parameter-specific context beyond the schema, so the score is at the baseline for moderate coverage.
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 explicitly states that the tool calculates the formant-corrected H1*–H2* voice source measure from sustained vowels, and distinguishes it from other calculate_ tools by specifying the exact metric and its interpretation (breathiness). This provides a clear, specific verb+resource combination.
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 sustained vowel analysis but offers no explicit guidance on when to choose this tool over sibling measures like CPP or HNR. There is no mention of prerequisites, contraindications, or alternative tools, leaving the agent to infer suitability based on the metric definition.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses async processing and automatic polling, and lists return types. However, it omits details like required audio format constraints (beyond 'WAV' from schema), potential error conditions, or rate limits. Adequate but not thorough.
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 two sentences, front-loading purpose and output, then explaining behavioral trait (auto-polling). Every sentence adds value with no redundancy.
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 no output schema and average complexity (async polling), the description covers purpose, output, and auto-polling behavior. It lacks explicit constraints like audio length limits (implied 30-120s) and error handling, but is mostly sufficient for agent invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. The description adds no parameter-specific information beyond the schema; it does not explain polling interval or timeout semantics beyond what the schema already provides.
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 classifies stuttering disfluency patterns in speech recordings, listing specific output types (repetitions, prolongations, blocks) and metrics. It distinguishes from sibling tools which focus on other voice metrics or therapy tasks.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no explicit guidance on when to use this tool versus alternatives like vocametrix_assess_pronunciation or vocametrix_calculate_* tools. It implicitly describes the async behavior but does not specify conditions where this tool is preferable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavior. It states extraction but omits crucial details such as whether the file is modified, required permissions, output format, or error handling (e.g., missing file or non-WAV format).
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?
Two sentences: the first clearly states the core action and scope, the second adds examples and a use case. No unnecessary words; information is front-loaded and efficient.
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?
The tool is simple with one parameter and no output schema, yet the description fails to mention what the tool returns (e.g., feature values as JSON). This leaves the agent uncertain about the tool's output, which is a significant gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with a single parameter 'audioPath' described as an absolute WAV path. The description adds context about the feature set and use case but does not enhance parameter semantics beyond the schema's own description.
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 specifies extraction of the full eGeMAPSv02 feature set (88 acoustic features) from an audio file, listing example features and a clear use case for ML voice pathology classifiers. This distinguishes it from sibling tools that extract individual features or perform other audio analyses.
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 implies usage for obtaining a comprehensive acoustic feature set, especially for ML classifiers. However, it does not explicitly state when to use this tool versus the many single-feature siblings, nor does it provide exclusions or alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses the output (age/gender-normalized norms and classification), indicating a read-only operation, but does not elaborate on computational requirements, potential errors, or whether any data is modified.
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 extremely concise—two sentences—and front-loads the key output measures. Every word adds value, with no redundancy or irrelevant 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 the tool's complexity and absence of an output schema, the description provides a functional overview but omits important context such as expected audio format, sample rate prerequisites, or what the classification labels mean. It is adequate but leaves gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 75%, with three of four parameters described in the schema. The description adds no new parameter-level detail; it only reinforces that the input should be a sustained vowel. The 'version' parameter lacks any explanation beyond its default value.
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 identifies the tool's purpose: extracting advanced spectral measures from a sustained vowel. It specifies the exact measures (center of gravity, skewness, H1-H2, etc.), distinguishing it from sibling tools that focus on individual measures or other acoustic features.
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 the tool is for comprehensive spectral analysis of sustained vowels, but does not explicitly state when to use it over other spectral tools (e.g., vocametrix_calculate_h1_h2). No when-not-to-use guidance or alternative recommendations are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full responsibility. It states the tool computes metrics but does not disclose whether it modifies input files, has side effects, requires network access, or what happens on error. The description implies read-only behavior but does not confirm it.
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?
Two sentences, no redundancy. First sentence delivers the core function (compute specific metrics), second sentence provides relevant use contexts. Every word earns its place.
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 the tool has 3 parameters and no output schema, the description hints at the return values (composite scores) but does not specify structure, file format requirements beyond WAV, or any prerequisites. Adequate but not comprehensive for an AI agent to fully understand behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% (all three parameters have descriptions), so the bar is low. The tool description adds minimal extra meaning beyond listing output concepts; it does not detail parameter constraints or dependencies. Baseline 3 is appropriate.
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 computes intensity dynamics, pitch-intensity correlation, and composite scores for specific voice dimensions. It distinguishes itself from sibling tools like calculate_jitter_shimmer or calculate_hnr by specifying exactly what metrics it produces, making its purpose unmistakable.
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 lists use cases (voice training, coaching, fatigue assessment) but does not guide when to use this tool vs. alternatives among the many calculate_* siblings. No explicit when-not-to-use or comparison with other tools, limiting decision support.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses that the tool calculates from a glissando recording and returns specific outputs, but does not mention error handling, performance, or side effects. Adequate but not rich.
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 two sentences with no fluff. First sentence states the action and input, second states the output and use cases. Highly efficient and front-loaded.
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 should clarify the output structure. It mentions 'returns frequency range... and intensity range with interpretation' but is vague on format. Schema covers parameters well, but the description could better specify the return structure.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. The description repeats that age/gender are used for interpretation but adds no new meaning beyond the schema definitions. No additional parameter details are provided.
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 it calculates the Voice Range Profile from a glissando recording, listing outputs (frequency and intensity range with interpretation) and use cases. It distinguishes from sibling calculate tools by specifying the unique VRP output and input type.
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 mentions it is 'useful for singers and voice rehabilitation assessment,' providing context for when to use it. However, it does not explicitly compare to other calculate tools or state when not to use it, missing explicit alternatives or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses one behavioral constraint (startSec must be > 0) but omits details on error handling, return format, or potential side effects. Adequate but not comprehensive.
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 efficient: two sentences plus a note, each serving a distinct purpose (purpose, use cases, behavioral note). No unnecessary words.
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?
The description covers purpose and one behavioral note, but lacks output format details (no output schema) and error handling context. Adequate for a simple tool but could be more complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, meeting the baseline. The description reinforces the startSec minimum with a practical note, but adds no new semantic meaning beyond the schema's existing constraints.
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 measures sound level in dB SPL over a time window, with explicit use cases (environmental noise, vocal loudness, calibration). This distinguishes it from sibling tools that focus on specific acoustic parameters.
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 lists useful applications but does not explicitly contrast with alternatives or state when not to use. The use cases imply context, but no exclusion criteria or alternative tools are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must cover behavioral traits. It notes the adaptation and HTML output but omits details on side effects, permissions, or error conditions. The description is minimal but not misleading.
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?
A single sentence that front-loads the verb and resource, followed by output format. Every word is necessary, no redundancy.
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?
With no output schema, the description adequately explains the return type (HTML-formatted adapted version). However, it could mention expected input format for 'exerciseText' or any limitations. Overall, sufficient for most agents.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema coverage is 100%, so the description adds little beyond what the schema already provides. It repeats the output format but does not explain parameter nuances or constraints.
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 verb 'adapt', the resource 'speech therapy exercise', and lists specific learner profiles. It distinguishes from sibling tools like 'generate_exercises' by focusing on adaptation of existing exercises rather than creation.
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 when an exercise needs to be tailored for a specific profile, but it does not explicitly state when to use versus alternatives like 'vocametrix_generate_exercises' or the workflow tool. No exclusion criteria are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. Mentions 'vowel-space stability and articulatory precision scores' but does not explain how these are derived or any prerequisites (e.g., audio quality, file format restrictions). Missing details on side effects or error conditions.
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 concise sentences front-load the core functionality. However, a structured list of statistics could improve scannability. No waste but minor room for optimization.
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?
Addresses purpose and usage context but lacks details on output format, error handling, and prerequisites (e.g., expected audio duration or sample rate). For a tool with no output schema and no annotations, more behavioral context would be beneficial.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema has 100% description coverage; each parameter already has a clear meaning. The description adds no extra context beyond what schema provides, meeting the baseline for high coverage without enhancement.
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 specifies that the tool computes F1–F4 formant statistics (mean, SD, range, CV, IQR) from sustained vowels. It lists specific clinical applications (dysarthria, vowel space analysis, cleft palate), making its purpose distinct from sibling calculate tools.
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?
Provides explicit use cases (dysarthria assessment, vowel space analysis, cleft palate evaluation) but does not contrast with alternative tools or state when not to use it. Lacks explicit exclusions, which is a minor gap.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description bears full responsibility. It mentions the tool computes jitter and shimmer and associates elevated values with dysphonia, but does not disclose if it is read-only, requires specific permissions, or has side effects. Basic transparency is present.
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?
Two concise, well-structured sentences: first defines the operation, second provides normal thresholds and clinical significance. No unnecessary information.
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 no output schema, the description should explain return format; it mentions normal ranges (e.g., 'normal jitter < 1.04%') but does not explicitly state that these are output fields. Otherwise, it covers input type and clinical context well. Slight gap in specifying output structure.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 100% coverage for the single parameter, describing it as 'Absolute path to a WAV file'. Description adds context about sustained vowel requirement and clinical meaning but does not significantly enhance the schema's parameter definition. Baseline score of 3 applied.
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?
Description clearly states it calculates jitter (PPQ5) and shimmer from a sustained vowel, using specific clinical terms. It distinguishes from sibling tools like calculate_cpp or calculate_avqi by naming the exact parameters.
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 on sustained vowel recordings and provides normal ranges for clinical context. However, it does not explicitly state when to use this tool over other acoustic measure tools or mention any prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description notes the use of a deep-learning classifier, output of labels with confidence scores, and language support, but does not disclose processing time, file integrity handling, or whether the tool is read-only. Without annotations, the description carries the burden but remains basic.
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?
Two sentences, each carrying essential information: purpose and output, then language support. No superfluous words, front-loaded with the verb 'detect'.
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 no output schema and only 2 parameters, the description provides good context: it explains what the tool does, what it returns, and the supported languages. It could mention that the audio file must be a local WAV file (implied by schema but not stated in description).
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?
Schema coverage is 100%, so parameters are already documented. The description adds value by explaining the output nature (labels with confidence scores) and specifying languages, which enhances understanding beyond schema types and enums.
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 detects phonemes in audio using a deep-learning classifier and returns labels with confidence scores. It distinguishes itself from sibling tools by specifying phoneme detection and listing supported languages (French, Estonian).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like transcribe_audio or assess_pronunciation. The description implies use for phoneme detection but does not exclude other scenarios or reference sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full responsibility for disclosing behavioral traits. It does not mention idempotency, side effects, rate limits, or permissions required. For a tool that likely performs a read/interpretation operation, stating it is non-destructive would improve transparency. The description focuses only on functionality, not behavioral characteristics.
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 two sentences long and front-loads the core action ('Translate raw voice metrics...'). Every sentence provides essential information without redundancy. It is appropriately sized for the tool's simplicity.
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 no output schema, the description adequately explains what the tool produces: 'clinical-language interpretation with severity classification ... and actionable recommendations.' It could be slightly more detailed about the exact output structure (e.g., whether severity is a string or object), but it is sufficient for an AI agent to understand the tool's role in a workflow, especially with the context of sibling tools that generate the metrics.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 50% (metrics and languageCode have descriptions; patientAge and patientGender do not). The description adds value by listing example metrics and explaining the output (interpretation with severity and recommendations), which is not captured in the schema. However, it does not elaborate on patientAge or patientGender beyond the schema's built-in constraints.
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 specifies the tool's purpose: translating raw voice metrics into clinical-language interpretation with severity classification and recommendations. The mention of specific metrics (jitter, shimmer, etc.) and output types ('normal/mild/moderate/severe') distinguishes it from sibling tools that calculate these metrics (e.g., vocametrix_calculate_jitter_shimmer).
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 explicitly states when the tool is useful: 'when you have metric values from other tools and want a clinician-readable summary.' This provides clear context for use, though it does not explicitly mention when not to use it or list alternatives. However, the sibling tools are largely metric calculators, so the intended use case is well-differentiated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are supplied, so the description must cover behavioral aspects. It explains the ratio's clinical interpretation but omits the output format (e.g., numeric value) and potential error conditions.
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?
Two concise sentences with the most critical information front-loaded. No extraneous content.
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?
The description explains the concept well but lacks mention of the return value (the ratio itself). Given no output schema, this is a notable gap for completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 80%, and the description adds clinical context (threshold, requirement for two recordings). It does not elaborate on individual parameters beyond the schema, so baseline 3 is appropriate.
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 it calculates the S/Z phonation ratio, defines normal and pathological thresholds, and specifies the requirement for two recordings. This distinguishes it from sibling 'calculate_*' tools.
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?
It explicitly states the prerequisite of two separate recordings, giving clear context for usage. No explicit when-not-to-use or alternatives are provided, but the condition is well-defined.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. Discloses parallel execution and output format (JSON report with metrics and severity interpretation). Lacks details on side effects, error behavior, or prerequisites like audio format requirements.
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?
Three sentences, each serving a purpose: action + scope, output description, and usage context. No superfluous words; information is front-loaded.
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?
For a complex tool with no output schema, description covers main purpose and required inputs. However, it lacks specifics on output structure (exact metrics, severity scale) and does not address prerequisites like sample rate. Could be more complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, baseline 3. Description adds context for sustainedVowelPath and connectedSpeechPath (e.g., '/a/ held 3+ s'), but does not enhance understandability of patientAge and patientGender beyond 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?
Description clearly states 'Run a comprehensive clinical voice assessment' and lists the specific analyses executed (AVQI, CPP, etc.), distinguishing it from sibling tools that calculate individual metrics.
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?
Explicitly says when to use ('full voice quality screening') and specifies required recording types. However, it does not mention when not to use or alternatives like individual calculate_* tools for single metrics.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It states the tool generates a word list with hints and difficulty progression, which covers basic behavior, but does not disclose any side effects, permissions, or safety considerations.
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?
Two sentences, front-loaded with the main action, zero fluff. Every sentence serves a purpose: action + use cases.
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?
Tool has a nested object parameter and no output schema. Description explains purpose and use cases but lacks details on return format, number of words, or how pronunciation hints are presented, which would help the agent invoke correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 33%. The description adds no additional meaning to parameters beyond what is in the schema. For example, it doesn't explain how patientAge affects difficulty or what language codes are valid.
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?
Description clearly states the tool generates a word list targeting a specific phoneme with pronunciation hints and difficulty progression, and lists specific use cases (articulation therapy, phonological awareness drills, accent training). This distinguishes it from sibling tools that assess pronunciation or generate exercises.
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?
Description provides clear context for use (articulation therapy, phonological awareness drills, accent training) but does not explicitly state when not to use or compare to alternatives like vocametrix_generate_exercises.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It mentions returns (accuracy/fluency/prosody scores, pitch statistics) but omits side effects, authorization needs, file format constraints (beyond WAV), or typical response size. It is adequate but not thorough.
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?
Two concise sentences, front-loaded with the core purpose. Every word serves a purpose: the first sentence defines the action and enrichment, the second explains the benefit. No redundancy or unnecessary detail.
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 no output schema, the description provides the main return types (scores, pitch stats) and hints at use cases. It lacks detailed output structure but is sufficient for an assessment tool. Could mention that audio must be WAV (already in parameter) and optional defaults. Reasonably complete for its complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with each parameter having a clear business-level description. The tool description adds context about what the parameters enable (pitch analysis) but does not refine parameter syntax beyond the schema. Baseline 3 is appropriate as the description adds marginal value.
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 explicitly states 'Pronunciation assessment enriched with per-word F0 (pitch) contours', which is a specific verb+resource combination. It distinguishes from the sibling 'vocametrix_assess_pronunciation' by highlighting the added pitch analysis, making it clear what this tool uniquely offers.
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 implies usage for tonal language analysis and prosody coaching, suggesting when pitch matters. However, it does not explicitly state when not to use it (e.g., basic pronunciation without pitch) or directly name the alternative sibling. The guidance is clear but could be more direct.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries full burden. It states the tool returns similarity scores, implying no side effects, but does not disclose read-only behavior, file format prerequisites (e.g., WAV specifics), or any limitations. This is adequate but not rich on 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences: first states the action and output, second lists similarity aspects and use cases. Every sentence is substantive, with no redundancy or fluff, achieving high information density.
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 full schema coverage, no output schema, and no annotations, the description covers purpose, parameter roles, and return types. It lacks file format constraints or further behavioral details, but for a comparison tool it is fairly complete; minor gaps in prerequisites prevent a higher score.
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?
Schema coverage is 100%, and descriptions are provided. The tool description adds value by clarifying roles (modelPath as reference/teacher, learnerPath as student) beyond the schema's generic descriptions, aiding correct parameter use.
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 compares prosodic patterns between model and learner recordings and returns specific similarity scores (pitch contour, intensity, duration, pause patterns). It also lists use cases (accent coaching, speech imitation training, L2 pronunciation), effectively distinguishing it from sibling tools like vocametrix_assess_pronunciation.
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 prosody comparison but does not explicitly state when to use this tool versus alternatives (e.g., segmental assessment tools). No direct comparisons or when-not-to-use guidance is provided, leaving the agent to infer from context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully convey behavioral traits. It explains what the tool returns (score, issues, corrected text, readability) but does not disclose side effects, authentication needs, rate limits, or error behavior. The output description is adequate but not comprehensive.
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 two sentences, front-loaded with the core functionality and output types. Every sentence adds value: first sentence defines action and output, second sentence provides usage context. No redundant or vague phrases.
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 no output schema, the description covers key return elements (score, issue breakdown, corrected text, readability statistics) and constraints (max length). It also ties to a domain (speech-language assessments). Slight gap: no mention of error handling or supported locales beyond the locale parameter. Still sufficient for typical use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with both parameters described. The description adds context about the tool's purpose but does not explicitly map parameters to their roles (e.g., how locale affects analysis). It adds marginal value beyond schema descriptions, so baseline 3 is appropriate.
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 uses a specific verb ('Analyze text') and noun ('grammar and syntax errors'), clearly distinguishes the tool from voice/pronunciation siblings by focusing on text analysis. It also specifies the output (severity classification, score, breakdown, corrected text, readability stats) and a concrete use case (speech-language assessments).
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 states it is 'useful for evaluating written language samples in speech-language assessments,' providing clear context. However, it does not explicitly mention when to avoid using it (e.g., non-text inputs) or suggest alternative tools for other needs, so it misses the highest bar.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses the ability to include syllable marks but does not detail behavior for invalid input, output format, or error handling.
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?
Two sentences, no redundant information. Every sentence adds value: first states purpose, second specifies input format and options.
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 simple tool (2 parameters, no nested objects, no output schema), the description adequately covers inputs and options. It misses explicit return value format, but 'IPA transcription per word' is sufficient for a straightforward conversion.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already describes both parameters thoroughly (single string/array limit, boolean). The description adds minimal value ('with optional syllable boundary marks'), so baseline 3 applies.
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 converts French words/phrases to IPA transcription, with a specific verb ('convert') and resource ('French words to IPA'). It differentiates from sibling tools as none directly target IPA conversion.
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 provides usage constraints: accepts single string or array up to 20 words, and optional syllable marks. However, it does not explicitly state when to use versus alternatives, but given the sibling list, no direct alternative exists.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description bears full behavioral burden. It mentions returned data (matches, explanation, guidance) but lacks details on output format, error handling, or constraints (e.g., handling of non-letter sounds). Moderate transparency.
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?
Two sentences, no fluff, front-loaded with purpose. Every sentence adds value.
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?
For a tool with no output schema and no annotations, the description adequately covers purpose and outputs. However, it could mention return type (e.g., JSON object) to improve completeness. Sibling context helps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, and descriptions already clarify 'transcription' and 'targetWord'. The description adds little beyond stating 'STT system heard (the spoken spelling)', so baseline of 3 is appropriate.
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?
Description clearly states the verb 'Interpret' and resource 'speech-to-text transcription of a spelling attempt'. It specifies what it returns (matches, explanation, correction guidance), and distinguishes from sibling tools like assess_pronunciation or transcribe_audio by focusing on spelling attempts.
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?
Explicitly states 'Useful for spelling therapy apps where children spell words aloud', providing context. However, it does not explicitly mention when not to use it or compare to alternatives like vocametrix_assess_pronunciation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden. It mentions returns audio and timing map, supports plain text or SSML. But it does not disclose potential cost implications, authentication requirements, or rate limits associated with ElevenLabs.
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?
Two sentences, front-loaded with key purpose and output, no filler. Every sentence adds value.
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 2 params and no output schema, the description covers the core behavior and return type adequately. Could mention authentication or costs but not required for functional understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. Description adds value by clarifying that isSSML parameter supports SSML markup, which complements the schema description. No further detail beyond 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?
Description clearly states the tool synthesizes speech with per-character timing alignment via ElevenLabs v2, distinguishing it from the sibling vocametrix_synthesize_speech which likely lacks timing support.
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?
Indicates the tool is useful for lip-sync, subtitles, and karaoke, implying when to choose this over the sibling without timing. However, it doesn't explicitly state when not to use or mention alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses adaptive learning, spaced repetition, and conversation context maintenance via threadId. It does not mention any destructive actions or permissions, but the behavioral traits are well explained for a non-mutating tool.
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?
Two sentences with no superfluous text. The first sentence communicates the core purpose and adaptation; the second adds spaced repetition and context. Ideal length and front-loading.
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?
The description covers adaptation, spaced repetition, and conversation context. No output schema exists, so the return value is implied as conversational response. It is sufficiently complete for an AI agent to select and invoke correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the input schema already documents all parameters. The description adds that the tool adapts to nativeLanguage, targetLanguage, ageGroup, topic, and maintains context via threadId. This adds marginal value beyond the schema, resulting in a baseline score of 3.
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 it is a 'conversational vocabulary tutor' that adapts to learner profile, using spaced repetition. This specific verb+resource combination distinguishes it from sibling tools like assessment or exercise generation tools.
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 vocabulary practice conversations but does not explicitly state when to use this tool versus alternatives like 'generate_exercises' or 'assess_pronunciation'. No when-not-to or alternative guidance is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses irreversible approval, locking and delivery, rejection, and modification with feedback. No annotations provided, so description carries burden. Adequately covers behavior for a simple workflow gate.
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?
Two sentences with bulleted actions. No unnecessary words. Efficiently conveys essential 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?
No output schema, and description does not explain return value or result format. For a simple approval action, this is a mild gap. Otherwise complete for the core function.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 100% description coverage. Description adds that feedback is required for modify, which is already implied by schema enum and conditional field. Minimal additional value beyond 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?
Description clearly states it is an approval gate for generated therapy plans, listing three distinct actions with effects. This distinguishes it from sibling tools like generate_therapy_plan and get_therapy_status.
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?
Implies usage after generating a therapy plan. Mentions irreversibility of approve. Lacks explicit when-not-to-use or alternative tools, but context is reasonably clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the full burden. It implies a read-only calculation without destructive side effects, but does not explicitly state safety or computational properties. The thresholds are provided, but behavioral traits like statelessness are assumed.
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 two sentences, front-loaded with the core purpose, and contains no unnecessary information. Every sentence earns its place.
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?
The description covers input requirements and score interpretation well, but without an output schema, it does not specify the return format (e.g., numeric score). For a calculation tool, this is a gap that could be improved.
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?
Schema coverage is 100% with parameter descriptions already present. The description adds value by explaining the optionality of connected speech and the default version, as well as clinical thresholds beyond 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 the tool calculates the Acoustic Voice Quality Index (AVQI), a specific clinically validated dysphonia score. It distinguishes from sibling tools by specifying the metric and provides thresholds for dysphonia indication.
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 explicitly requires a sustained vowel recording and mentions connected speech as optional for improved accuracy. However, it does not provide explicit when-not or alternative tools for other voice metrics, though the context of siblings implies differentiation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It explains the meaning of HNR (higher = cleaner voice) and provides a normal reference value (>20 dB for 500 Hz band). It also notes age/gender norms, which are relevant. No side effects or destructive actions are mentioned, but the tool is a read-only analysis.
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 consists of two sentences, front-loading the core purpose and adding a normal value and requirement in the second. No unnecessary information is present.
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?
The description lacks information about the return format (e.g., a map of frequency bands to HNR values). Given no output schema, the output should be described to ensure the agent knows what to expect. While the description is adequate for a simple metric, it is incomplete without output details.
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 100%, and the description adds value by connecting patientAge and patientGender to age- and gender-specific norms, which justifies their inclusion. It does not provide additional format details beyond 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 the verb 'Calculate' and the specific metric 'multi-band Harmonics-to-Noise Ratio (HNR)' across frequency bands with norms. It distinguishes from sibling tools like vocametrix_calculate_avqi or vocametrix_calculate_cpp, which focus on different voice metrics.
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 mentions the requirement for a sustained vowel, which provides context for when to use the tool. However, it does not explicitly state when to use this tool versus alternatives, nor does it specify 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.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses behavioral traits such as maintaining conversation context via threadId for multi-turn dialogue, and adapting answers based on role. No annotations are provided, so the description carries the full burden. It adequately informs the agent about the tool's adaptive and stateful behavior.
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 three sentences long, front-loaded with the main purpose. It is efficient and contains no fluff. However, it could be slightly more concise by combining some ideas, but overall it's well-structured.
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 tool's complexity (chat assistant with state and role adaptation) and the lack of output schema or annotations, the description is fairly complete. It covers the main functionality, statefulness, and role-based adaptation. No gaps are apparent for the intended use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all three parameters. The description adds context about threadId for multi-turn dialogue and accountType for role, but does not add significant new meaning beyond what the schema provides. Baseline 3 is appropriate.
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 is an 'Expert speech therapy assistant providing role-based guidance.' It specifies the target users (therapist, patient, parent/caregiver) and the adaptive nature. This distinguishes it from sibling tools like vocametrix_generate_exercises or vocametrix_assess_pronunciation, which are more specific tasks.
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 the tool (for role-based guidance) and implies alternatives by its chat nature. However, it does not explicitly state when not to use it or name sibling alternatives. The context signals show many other tools, so some guidance on when to prefer this over others would be beneficial, but the description still provides clear usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully shoulders behavioral disclosure. It describes the output structure (instructions, target phonemes, difficulty, tips) and that exercises are tailored. However, it does not mention any side effects, error handling, prerequisites, or constraints (e.g., parameter validation behavior). This is adequate but not comprehensive.
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 consists of two concise sentences. The first sentence clearly states the action and tailoring criteria, and the second describes the output. Every word serves a purpose, no fluff, and it is front-loaded with the primary function.
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 lack of output schema, the description adequately explains the return value (structured exercises with specific elements). It covers the key input categories (patient profile, pathology, language) and outputs. For a tool with 4 parameters and no output schema, this is sufficient for an agent to understand usage.
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?
Schema description coverage is 100% with individual parameter descriptions. The tool description adds value by specifying the output format (structured exercises with specific components) beyond the schema. While not all parameter nuances are elaborated, the description enhances understanding of the tool's purpose and output.
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 it generates personalized speech therapy exercises tailored to patient profile, pathology, and language. It mentions the output includes instructions, target phonemes, difficulty level, and tips, which differentiates it from sibling tools like vocametrix_generate_therapy_plan (which generates a broader plan) and vocametrix_adapt_exercise (which adapts existing exercises).
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 use when needing tailored exercises for a patient, but does not explicitly state when to use this tool versus alternatives like vocametrix_generate_therapy_plan or vocametrix_adapt_exercise. No when-not-to-use guidance is provided, and the context must be inferred from sibling names.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description details what the tool returns (accuracy, fluency, completeness, prosody scores 0–100 plus per-word and per-phoneme breakdowns) and supports 30+ locales, which adds behavioral context beyond the input schema. However, it does not mention side effects, authentication, or whether the tool is read-only, but given the assessment nature, the provided information is substantial.
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 three sentences, front-loaded with the core purpose and return values, then provides locale support and audio guidance. Every sentence adds value without redundancy or unnecessary detail.
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 tool's moderate complexity (3 parameters, no output schema), the description covers purpose, return values, and a key usage condition. It does not explicitly compare with similar sibling tools, but the detail on outputs and locales provides sufficient 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 input schema already describes all parameters with 100% coverage, so the baseline is 3. The description adds practical guidance: audioPath should be a 'clear reading' and speakerLocale supports 30+ locales with examples, enhancing semantics without repeating schema details.
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 scores pronunciation accuracy at the phoneme level against a reference text, listing specific output scores and breakdowns. It distinguishes itself from siblings like batch or pitch-based tools by focusing on phoneme-level analysis and returning multiple dimensions (accuracy, fluency, completeness, prosody).
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 provides a usage condition ('Audio should be a clear reading of the reference text') but does not explicitly differentiate when to use this tool over siblings like vocametrix_assess_pronunciation_with_pitch or vocametrix_batch_pronunciation. The intended use case is implied but not clearly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, but the description discloses processing order (sequential), output format (Markdown + JSON with specific scores), and the shared reference text requirement. Could mention error handling if folder is empty or files are not valid WAV.
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?
Two concise sentences that front-load the core function and output. Every word earns its place; no fluff.
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 no output schema, the description specifies the return format and score dimensions. It covers the main behavioral aspects. Could be more explicit about error cases or file validation, but adequate for a batch tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the schema already describes parameters. The description adds minimal additional context beyond the schema (e.g., 'common reference text' for referenceText). Baseline 3 is appropriate.
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 it assesses pronunciation for batch WAV files in a folder against a common reference text, returning per-file scores. It distinguishes itself from single-file alternatives like vocametrix_assess_pronunciation.
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?
Provides explicit use cases (classroom assessments, research cohorts, batch L2 evaluation) and mentions sequential processing to handle rate limits. Does not explicitly state when not to use or list alternatives, but context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses the algorithm ('native Praat algorithm') and interpretation, but does not cover edge cases (e.g., invalid files), permissions, or idempotency. Adequate but not fully transparent.
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 two sentences, front-loaded with the main purpose and result, followed by interpretation and algorithm reference. No redundant information.
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 single parameter and no output schema, the description covers purpose, interpretation, and algorithm. It does not explicitly state the return format (likely a numeric value), but that is inferable. Could mention handling of invalid inputs or prerequisites.
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?
Schema coverage is 100%, so baseline is 3. The description adds the context that the audio should be a 'sustained vowel', which is specific and not in the schema description alone. This adds meaningful guidance for parameter selection.
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 specifies the verb 'calculate', the resource 'Glottal-to-Noise Excitation (GNE) ratio', and the input 'from a sustained vowel', clearly distinguishing it from sibling tools like vocametrix_calculate_hnr or vocametrix_calculate_jitter_shimmer.
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 implies use for sustained vowels and interprets GNE values (e.g., <0.5 suggests breathiness), providing clinical context. However, it lacks explicit guidance on when to use this versus alternatives or 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.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses the polling nature, status lifecycle, and the result_available flag. It does not mention rate limits or read-only behavior, but for a status poller, the key behaviors are covered.
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 extremely concise—two sentences with no filler. The first sentence immediately states the purpose, and the second explains the statuses and next action. Every word earns its place.
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?
For a one-parameter polling tool without output schema, the description sufficiently covers the workflow: statuses and result availability. It could mention the response structure slightly more, but the mention of result_available implies Boolean flag. Overall adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema already provides 100% coverage for the single parameter (sessionId) with a clear description of its origin. The description adds no additional semantic meaning to the parameter beyond what the schema already states.
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 polls status for async therapy plan generation or stuttering classification. It explicitly lists status progression (pending → processing → pending_approval → complete/failed) and distinguishes from sibling vocametrix_get_therapy_result by noting when result_available is true.
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 tells when to use the tool (polling status) and provides a clear next step (call vocametrix_get_therapy_result when result_available is true). It does not explicitly state when not to use it, but the context is clear enough for an AI agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
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. It adequately explains the streaming mechanism (SSE), progress events, polling, and time estimates. It does not mention authentication or file size limits, but the overall transparency is good.
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 three sentences, front-loaded with the core purpose, and each sentence adds value. No redundant or extraneous information.
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?
The description explains the return values (transcriptionId, progress events, final transcript, word-level timing) despite the lack of an output schema. It gives a time estimate and polling advice. Minor gap: no mention of file size limits or supported formats beyond WAV, but overall sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, so the baseline is 3. The description does not add meaning beyond the schema's existing parameter descriptions (audioPath and speakerLocale). It does not elaborate on format requirements or locale nuances.
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's function: transcription of an audio file using Azure Speech-to-Text with streaming progress. It specifies the output (transcriptionId, progress events, final transcript with word-level timing), distinguishing it from sibling tools like vocametrix_assess_pronunciation or vocametrix_synthesize_speech.
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 provides context by mentioning that transcription may take 30–120 seconds and suggests polling progress events for long recordings. However, it does not explicitly state when to use this tool versus alternatives or when not to use it, which would raise the score to 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses the asynchronous polling behavior, the involvement of human approval, and the possible return values ('approved plan' or 'pending plan awaiting approval'). It does not mention permissions or side effects, but covers the main behavioral traits sufficiently.
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 two sentences long, efficiently covering the tool's purpose, process, output, and required follow-up action. Every sentence adds value without repetition or fluff.
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 tool's complexity (5 parameters, async with polling, human-in-the-loop) and no output schema, the description adequately explains the input requirements and return types. It could elaborate on the structure of a 'therapy plan', but the mention of sibling tools for approval partially compensates.
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?
Although schema coverage is 100% with descriptions, the description adds context: it specifies that sessionMetadata must include 'patient_id' and wav2vecOutput must include 'summary_statistics', which are additional constraints not fully detailed in the schema. It also explains the polling and timeout parameters as controls for the async process.
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 it is an 'end-to-end therapy plan generation' tool that 'generates a therapy plan from session data, polls until complete, and presents it for approval.' This specific verb-resource pairing differentiates it from sibling tools like vocametrix_generate_therapy_plan and vocametrix_approve_therapy_plan by combining both steps.
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 provides clear guidance: 'After reviewing, call vocametrix_approve_therapy_plan with approve, modify, or reject.' It indicates the post-use action and implies when to use this full workflow versus separate tools. However, it does not explicitly state when to avoid this tool in favor of individual generation or approval.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided. The description discloses asynchronous execution, ~30-120 second duration, and prerequisite steps, adding value beyond the tool name.
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?
Three sentences: core action, workflow guidance, prerequisite. No redundancy, front-loaded with key information.
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?
Adequately covers async pattern, time estimate, and prerequisite. Missing explicit statement about output format beyond ID, but subsequent tools handle retrieval, and no output schema exists.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%; the description does not add new parameter details beyond the schema (e.g., 'must include patient_id' is already in schema). It provides high-level workflow context but not parameter-level semantics.
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 launches an asynchronous therapy plan generation from audio embeddings, returns a session ID, and is distinct from sibling tools like approve or get result.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says to poll status with vocametrix_get_therapy_status and retrieve result with vocametrix_get_therapy_result, plus prerequisite of extracting embeddings first.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It describes the return content ('full therapy session with exercise plans, clinical narrative, and HTML report path'). It would benefit from stating if the tool is idempotent or what happens if called before result is ready, but overall it discloses key behavior.
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?
Two sentences front-load the purpose and the critical usage condition. Every sentence adds value with no redundancy.
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 a simple tool with one parameter and no output schema, the description covers purpose, usage condition, and return contents. It provides sufficient context for correct invocation and understanding of what it does, though more detail on the structure of the returned data would elevate it.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Only one parameter with 100% schema coverage. The description adds minimal value by noting the session ID comes from vocametrix_generate_therapy_plan, which is helpful context but not essential beyond the schema itself.
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 'Retrieve the completed therapy plan result' with a specific verb and resource. It distinguishes from sibling tools like vocametrix_get_therapy_status by explicitly referencing the precondition involving that tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance: 'Only call when vocametrix_get_therapy_status returns result_available = true or status = ''complete''.' This clearly tells the agent when to use this tool versus alternatives, leaving no ambiguity.
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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