klattsch-mcp

Server Quality Checklist

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Latest release: v2.0.0
Disambiguation5/5
Each tool has a clearly distinct purpose: listing phonemes, converting text, speaking, speaking to file, validating, and retrieving presets. No overlaps.
Naming Consistency4/5
Most tools follow a verb_noun pattern with underscores, but 'voice_presets' is a noun phrase rather than a verb, causing a slight inconsistency.
Tool Count5/5
Six tools is well-scoped for a speech synthesizer, covering all essential actions without being too many or too few.
Completeness5/5
The tool set covers the full workflow: creating phoneme strings (text_to_phonemes), validating them, rendering audio in two formats, referencing phonemes, and accessing presets.
Average 4.2/5 across 6 of 6 tools scored.
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
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
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Tools from this server were used 10 times in the last 30 days.
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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

Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description bears full responsibility for behavioral transparency. It discloses the read-only nature (parse without rendering audio) and describes the outputs. However, it does not clarify potential side effects or permissions, though none are expected for a validation tool. The term 'schedule info' is somewhat vague.
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 two sentences: one for the operation and one for outputs. It is front-loaded with the key information and contains no redundant text.
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 tool with one parameter and no output schema, the description covers the input, operation, and outputs adequately. However, 'schedule info' is vague and could be expanded for clarity. Overall, it provides sufficient context for an agent to understand the tool's purpose and 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 coverage is 100% for the single parameter 'utterance', and the description does not add significant new meaning beyond 'klattsch phoneme string'. It reinforces context but lacks format examples or constraints. Baseline of 3 is appropriate as the schema already describes the parameter.
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 core action (parse a klattsch phoneme string), what it does not do (without rendering audio), and its outputs (schedule info, duration, warnings). It effectively distinguishes it from siblings like speak (audio rendering) and list_phonemes (listing).
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 validation without audio, but it does not explicitly state when to use this tool versus alternatives like speak or text_to_phonemes. No exclusions or sibling names are mentioned, so guidance is only implicit.
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 output format (base64 WAV), control directives, stop consonant auto-burst behavior, and intonation patterns. It does not cover error handling or rate limits, but the behavioral detail is extensive.
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 long but well-structured with headers, tables, and examples. Every section adds value for the complex phoneme input. It is front-loaded with the purpose and quick-reference guide. Slightly verbose but justified by the domain.
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 complexity of speech synthesis and no output schema, the description is extremely thorough. It covers input format, voice presets, prosody patterns, phoneme categories, and even special behaviors like stop consonants. No significant gaps remain.
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 50% (only utterance has a description). The description greatly elaborates on the utterance parameter, but does not mention sampleRate at all, leaving its semantics to schema min/max/default. This partial coverage results in a moderate score.
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 states the tool synthesizes speech from a klattsch phoneme string and returns base64 WAV audio. It clearly identifies the specific verb and resource, and distinguishes from siblings like text_to_phonemes and speak_file by focusing on phoneme input.
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 step-by-step instructions on building phoneme strings, setting voice parameters, and adding prosody. It also suggests using text_to_phonemes first. However, it does not explicitly mention when to use alternatives like speak_file or list_phonemes, so it misses some exclusion guidance.
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 the full burden. It discloses that conversion is approximate, uses dictionary lookup with letter-by-letter fallback, and returns a phoneme string with control prefixes. It does not mention any destructive effects or authentication needs, which are not expected for a text conversion 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?
The description is concise at five sentences, front-loaded with purpose. Every sentence adds value: explaining the process, warning about unknown words, and advising editing. No unnecessary words or repetition.
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 explains the return value (phoneme string ready for speak) and mentions control prefixes. It provides sufficient context for an agent to use the tool effectively, though it could explicitly state the output format or include a brief example.
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?
All five parameters are described in the input schema with good detail. The description adds context about the output being editable and including control prefixes, but does not significantly enhance parameter understanding 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 converts English text to an approximate klattsch phoneme string via word-by-word dictionary lookup. It specifies the fallback spelling for unknown words and distinguishes itself from sibling tools like speak and speak_file by noting the output is ready for those 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?
The description advises editing the output before using it with speak and warns that unknown words sounded robotic. It provides context on when to use (conversion) and best practices, though it does not explicitly state when not to use it or compare to siblings.
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 but only states the output; no mention of performance, rate limits, or data sources is needed for a simple list.
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?
Single sentence, front-loaded with key action, no redundant words.
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?
For a simple list tool with no parameters or output schema, the description fully covers what the tool returns (39 codes, examples, tips).
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?
No parameters exist, baseline is 4; description does not need to add parameter info.
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 lists all 39 ARPAbet phoneme codes with examples and tips, distinguishing from sibling tools that perform speaking or 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 implies usage for reference purposes; sibling tools cover different functions, but no explicit when-not or alternatives are given.
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 correctly indicates a read-only operation (Get) and describes the return format. It does not mention edge cases or side effects, but for a simple retrieval 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with no filler. Every sentence adds value: one defines the tool's action and output, the second provides usage context. Highly efficient.
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?
For a simple tool with one parameter and no output schema, the description is fully sufficient. It explains the purpose, return format, and how to use the result in the broader phoneme workflow.
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 covers the single parameter with 100% coverage, including a description and enum values. The description does not add significant meaning beyond 'Get copy-paste ready voice presets,' so a baseline score 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?
The description clearly states the verb (get), the resource (voice presets), and the result (preset name → directive string). It distinguishes from siblings like list_phonemes and speak by specifying the return format and its use as prefixes for phoneme strings.
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 advises 'Use these as prefixes before your phoneme strings,' providing clear context for when to use the tool. It does not explicitly state when not to use it or mention alternatives, but the guidance is sufficient given the sibling tools.
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 the return value structure (filePath, byteLength, durationMs, warnings) and mentions writing to disk. However, it does not cover potential side effects like overwriting existing files or permissions, which would be useful. No annotations are provided, so the description carries the burden.
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, consisting of two sentences and a return list. Every sentence adds value, and the key differentiator is front-loaded.
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 tool with three parameters and no output schema, the description covers purpose, usage, return structure, and contrasts with sibling. It could mention the sampleRate default/range, but that is already in the schema. Overall, it is nearly 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 description coverage is 67%, and the description does not add significant new information beyond the schema for 'utterance' and 'filePath'. The 'sampleRate' parameter has constraints in schema but no description; the description does not elaborate on it. With moderate coverage, a score 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?
The description clearly states that this tool writes a WAV file to disk and returns the path, contrasting it with the sibling 'speak' tool. The verb 'write' and resource 'WAV file' are specific, and the distinction from 'speak' is explicit.
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 explicitly advises 'Use this when you need to attach or share the audio file', which guides the agent on when to choose this tool over alternatives like 'speak'.
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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