Whissle MCP Server

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation4/5
Most tools have distinct purposes: diarize_speech adds speaker identification to transcription, speech_to_text is basic transcription, list_asr_models provides metadata, summarize_text and translate_text handle text processing. However, diarize_speech and speech_to_text share significant overlap in core transcription functionality, which could cause confusion about when to use each.
Naming Consistency5/5
All five tools follow a consistent verb_noun naming pattern with snake_case throughout: diarize_speech, list_asr_models, speech_to_text, summarize_text, and translate_text. The naming is predictable and follows the same grammatical structure across all tools.
Tool Count4/5
Five tools is reasonable for a speech/text processing server, though it feels slightly thin for comprehensive coverage. The count is appropriate for core functionality but could benefit from additional tools for more advanced operations like text analysis or audio preprocessing.
Completeness3/5
The server covers basic speech-to-text and text processing workflows, but has notable gaps. There's no way to edit or delete generated files, no batch processing capabilities, and no tools for audio preprocessing or quality assessment. While core transcription and text transformation are present, the surface feels incomplete for production workflows.
Average 4/5 across 5 of 5 tools scored. Lowest: 3.2/5.
See the Tool Scores section below for per-tool breakdowns.
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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 carries the full burden of behavioral disclosure. It states it's a listing operation, implying it's read-only and non-destructive, but doesn't specify aspects like rate limits, authentication needs, pagination, or what 'capabilities' entail in the output. This leaves significant gaps for a tool that might return complex data.
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 a single, efficient sentence that directly states the tool's purpose without any fluff. It's front-loaded and appropriately sized for a simple listing tool, with every word earning 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's complexity is low (0 parameters, listing operation) and an output schema exists, the description is somewhat complete. However, with no annotations and the description lacking details on behavioral traits or usage context, it doesn't fully compensate for the missing structured data, leaving room for improvement.
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 tool has 0 parameters, and the input schema has 100% description coverage (though empty). The description doesn't need to add parameter semantics, so it meets the baseline of 4 for tools with no parameters, as there's nothing to compensate for.
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 verb ('List') and resource ('all available ASR models and their capabilities'), making the purpose immediately understandable. However, it doesn't explicitly differentiate this tool from sibling tools like 'speech_to_text' or 'diarize_speech', which might also involve ASR models, so it doesn't reach the highest score.
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 guidance on when to use this tool versus alternatives. It doesn't mention any prerequisites, context for usage, or comparisons to sibling tools, leaving the agent without explicit direction on when this listing operation is appropriate.
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 provided, the description carries the full burden of behavioral disclosure. It effectively adds value by warning about costs ('⚠️ COST WARNING: This tool makes an API call to Whissle which may incur costs') and specifying the API provider (Whissle). It also mentions the return type ('TextContent with the summary'), which is helpful context beyond basic functionality.
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 appropriately sized and front-loaded, starting with the core purpose and cost warning. The parameter and return sections are clearly labeled. It could be slightly more concise by integrating the cost warning into the purpose statement, but overall, it avoids unnecessary fluff and each 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 the tool's moderate complexity (3 parameters, no annotations, but with an output schema), the description is fairly complete. It covers purpose, usage warnings, parameters, and returns. The output schema exists, so the description doesn't need to detail return values. However, it lacks information on error handling or rate limits, which could be relevant for an API-based 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 description coverage is 0%, so the description must compensate. It lists all three parameters (content, model_name, instruction) with brief explanations, adding meaning beyond the bare schema. However, it doesn't provide details like format examples, model options beyond 'openai', or instruction usage scenarios, leaving some gaps in parameter understanding.
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 purpose: 'Summarize text using an LLM model.' This specifies the verb (summarize) and resource (text), making it distinct from sibling tools like translate_text or speech_to_text. However, it doesn't explicitly differentiate from potential non-sibling summarization tools, keeping it at a 4 rather than a 5.
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 context for when to use the tool: 'Only use when explicitly requested by the user.' This gives explicit guidance on user-driven usage. However, it doesn't mention when not to use it or name specific alternatives among the sibling tools, which prevents a perfect score.
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 provided, the description carries the full burden of behavioral disclosure. It effectively adds context about cost implications (API call to Whissle that may incur costs), which is valuable behavioral information not covered by the schema. It also mentions the return type (TextContent), though an output schema exists.
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 appropriately sized and front-loaded: the first sentence states the core purpose, followed by a critical warning, then parameter details, and finally return information. Every sentence earns its place with no wasted words, and the structure is logical and efficient.
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 annotations, but with an output schema), the description is fairly complete. It covers purpose, usage warnings, parameter semantics, and return type. The output schema reduces the need to explain return values in detail, though more behavioral context (e.g., error handling) could enhance 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 0%, so the description must compensate. It adds meaning by explaining each parameter's purpose and providing examples (e.g., 'en' for English, 'es' for Spanish), which clarifies semantics beyond the bare schema. However, it doesn't detail constraints like valid language codes or text length limits.
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 purpose with a specific verb ('translate') and resource ('text'), specifying language conversion. It distinguishes from siblings like 'summarize_text' by focusing on translation rather than summarization, though it doesn't explicitly mention sibling differentiation.
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 context for when to use the tool ('when explicitly requested by the user') and includes a cost warning that implies when not to use it unnecessarily. However, it doesn't explicitly name alternatives or provide detailed exclusion criteria beyond the cost consideration.
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 provided, the description carries the full burden of behavioral disclosure. It effectively describes key behaviors: it performs transcription with speaker diarization, saves output files (with default directory behavior), and makes external API calls with potential costs. It also specifies the return format ('TextContent with the diarized transcription and path to the output file'). The main gap is lack of information about error handling, rate limits, or authentication 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?
The description is well-structured and appropriately sized. It starts with the core purpose, includes a critical warning prominently, then provides parameter documentation in a clear format. Every sentence earns its place - the warning is essential, and the parameter explanations are necessary given the 0% schema coverage. No wasted words.
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, external API calls, file operations) and the presence of an output schema, the description is mostly complete. It covers purpose, critical warnings, parameter semantics, and return format. The main gaps are: no information about supported audio formats, no mention of processing time or limitations, and no explicit error scenarios. However, with output schema handling return values, this is reasonably comprehensive.
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 0%, so the description must compensate. It provides meaningful semantics for all 5 parameters: explains what 'audio_file_path' is for, clarifies defaults for 'model_name' and 'output_directory', describes the purpose of 'max_speakers' and 'boosted_lm_words/score'. The description adds substantial value beyond the bare schema, though it doesn't explain parameter constraints (e.g., valid ranges for 'boosted_lm_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 clearly states the tool's purpose: 'Convert speech to text with speaker diarization and save the output text file to a given directory.' This is specific (verb: convert and save, resource: speech/audio file) and distinguishes it from sibling tools like 'speech_to_text' (which likely lacks diarization) and 'list_asr_models' (which lists models rather than processing audio).
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 includes explicit usage guidance with the cost warning: '⚠️ COST WARNING: This tool makes an API call to Whissle which may incur costs. Only use when explicitly requested by the user.' This provides clear context about when to be cautious. However, it doesn't explicitly differentiate when to use this tool versus alternatives like 'speech_to_text' (e.g., when speaker identification is needed vs. simple transcription).
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 provided, the description carries the full burden of behavioral disclosure. It effectively describes key behaviors: the tool makes an external API call to Whissle (implying network dependency and potential latency), incurs costs, saves output files to disk (with default directory behavior), and returns both transcription text and file path. It doesn't mention error handling or rate limits, but covers the essential operational traits.
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 well-structured with clear sections (purpose, warning, args, returns) and front-loaded key information. Every sentence earns its place: the first states the core function, the second explains default behavior, the warning is critical, and parameter explanations are necessary given 0% schema coverage. It could be slightly more concise in parameter explanations but remains 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?
Given the tool's complexity (external API call, file output, multiple parameters) and lack of annotations, the description provides complete context. It covers purpose, cost implications, parameter semantics, default behaviors, and output format. The presence of an output schema means the description doesn't need to detail return values, and it adequately addresses all other aspects needed for effective tool use.
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 0%, so the description must compensate. It provides meaningful semantics for all parameters: explains what 'audio_file_path' is for, clarifies 'model_name' default and purpose, defines 'timestamps' as word-level inclusion, explains 'boosted_lm_words' and 'boosted_lm_score' for recognition boosting, and details 'output_directory' default behavior. This adds substantial value beyond the bare 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's purpose with specific verbs ('convert speech to text', 'save the output text file') and distinguishes it from siblings like 'diarize_speech' (which focuses on speaker separation) and 'list_asr_models' (which lists available models). It specifies both the core transformation and the file-saving behavior.
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 usage guidance: 'Only use when explicitly requested by the user' due to cost implications, and distinguishes when to use this tool (for transcription) versus siblings like 'summarize_text' or 'translate_text' (which process text rather than audio). The cost warning serves as a clear when-not-to-use criterion.
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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