noisy-coding
OfficialServer Quality Checklist
Latest release: v2.17.0
- Disambiguation4/5
The set is mostly distinct: speak delivers a blocking utterance, announce is fire-and-forget, change_voice and list_voices have clearly separate roles. speak and announce both produce speech, so their overlap could cause an agent to misselect when the blocking behavior matters, but the descriptions offer strong guidance.
Naming Consistency4/5Naming is simple and readable with all verbs as commands, but it mixes one-word verb names (speak, announce) with verb_noun patterns (change_voice, list_voices). This minor inconsistency is not confusing and the style remains predictable.
Tool Count5/5Four tools is well-scoped for a voice/speech server: each tool covers a necessary function—speaking, quick updates, voice switching, and voice enumeration. There is no bloat or obvious missing core capability for the stated purpose.
Completeness4/5The tool set covers the main lifecycle of spoken interaction: speak, gently tell what you're doing, switch voices persistently, and discover available voices. One possible gap is lack of a way to query the currently active voice, but this is a minor issue that does not block common workflows.
Average 4.8/5 across 4 of 4 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 387 commits in the last 12 weeks
- Last stable release on
- 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.
Tools from this server were used 6 times in the last 30 days.
This repository includes a glama.json configuration file.
This server has been verified by its author.
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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
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so description carries full burden. 'List' clearly signals a read-only operation and adds the scope 'for the speak tool'. It does not disclose return format or dynamic behavior, but for a simple listing tool this is adequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
A single concise sentence with a clear verb and object. No redundant information, every word adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple with zero parameters and an output schema present. The description fully covers purpose and intended use, making it complete for its complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the baseline score of 4 applies. The description correctly adds no unnecessary parameter details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
Specific verb 'List' with resource 'Grok TTS voices' and clarifies they are for the speak tool, distinguishing from sibling tools that speak or change voice.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Implies use before speak to discover available voices, and the phrase 'available for the speak tool' gives context. No explicit exclusions or alternatives, but the purpose is clear enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full responsibility for behavior. It discloses that the change persists across restarts, affects the listener daemon, applies to future speak/announce calls, and that duplicate voices are refused to prevent indistinguishable speakers. This dramatically exceeds baseline explanation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is organized in clear sections with the proactive sentence first, followed by practical details and then argument semantics. Every sentence adds relevant information; there is no redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
This is a persistent state-changing tool with a lightness of given structured metadata. The description covers side effects, persistence, usage context, the valid arguments, and the duplicate-owner failure behavior. There is enough to invoke intentionally and correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides no parameter descriptions (0% coverage), so the description is the only source of meaning. It explains voice_id as the voice to switch to and elaborates on speaker, including its use for named SPEAKER-defined personas and the duplicate-refusal behavior. It could be slightly stronger on voice_id's allowed values, but overall it compensates well.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb and resource: 'Deliberately switch this agent's speaking voice from now on' and details the state change. It distinguishes itself from the sibling speech tools by explicitly stating that speak carries no voice information and that this is the only way to change how one sounds.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives clear context: use this call when the user asks for a voice change, use list_voices to see options, and don't expect speak/announce to carry voice information. This effectively tells the agent when to use this tool versus its siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden and does so thoroughly: it discloses serialized/concurrent speech queuing, interrupt semantics, that only text is sent (voice/language controlled by daemon), and speaker-role constraints for subagents. This is rich behavioral context beyond the schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is long but well-structured: purpose first, then usage guidelines, then behavioral notes, then parameter details. Every section earns its place, though the opening sentence and the second sentence partially overlap in saying it's a short spoken message.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (queueing, interrupt, subagent speaker, formatting tags), no annotations, and an output schema, the description covers all necessary context: when to use, exclusions, behavior, parameter semantics, and related tools. It is a complete standalone guide.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate and does. It explains text formatting (markdown bold, [pause], [laugh], <soft> tags), the exact meaning of interrupt, and the speaker parameter's subagent-only usage with dashboard implications.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description immediately states 'Speak a short message aloud to the user through their speakers,' a specific verb+resource pairing. It further scopes usage to 'a spoken TL;DR alongside (not instead of) your written answer' and explicitly contrasts with change_voice, distinguishing it from sibling tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit when-to-use guidance: deliver 1-3 conversational sentences summarizing outcome/finding/question, never read code/paths/long explanations. It also names the alternative change_voice for switching voices and explains interrupt behavior, giving clear conditions for interrupt=True.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full behavioral burden. It discloses that the tool returns immediately, plays in the background, queues behind current speech, and carries only text. This is thorough and gives the agent a clear model of runtime behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and well-structured, front-loading the core behavior in the first sentence and building on it with usage guidance. Every sentence adds value, with no redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter tool with an output schema present, this description covers purpose, usage, behavior, and parameter meaning fully. It is complete and leaves no significant gaps for an agent to operate correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The single 'text' parameter has zero schema documentation, but the description compensates by stating the tool 'carries only text' and that voice/speed/language live in the daemon, clarifying that the text parameter is the complete content with no hidden options.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb and resource: 'Speak a quick spoken update WITHOUT waiting for it to finish.' It directly distinguishes the tool from its sibling 'speak' by emphasizing the fire-and-forget behavior, making the 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 Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit guidance is provided: use this for quick updates while continuing work, and 'Use `speak` instead when you are asking a question or otherwise waiting for the user's reply.' This clearly states when to use this tool versus an alternative.
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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