MCP Server Whisper

Server Quality Checklist

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Latest release: v1.1.0
Disambiguation4/5
Most tools have clearly distinct purposes (chat, compress, convert, create, get, list, transcribe, enhanced transcribe), but there is some overlap between 'transcribe_audio' and 'transcribe_with_enhancement'—the latter could be seen as a specialized version of the former, which might cause confusion about when to use each. Otherwise, the tools are well-differentiated by their core functions.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern using snake_case (e.g., chat_with_audio, compress_audio, convert_audio). This uniformity makes the tool set predictable and easy to understand, with no deviations in naming conventions across the eight tools.
Tool Count5/5
With 8 tools, the count is well-scoped for an audio processing server, covering a range of operations from creation and conversion to transcription and management. Each tool appears to serve a specific, necessary function without redundancy, fitting within the typical 3-15 tool range for such a domain.
Completeness4/5
The tool set covers key audio workflows comprehensively, including creation, conversion, compression, transcription, and file management. Minor gaps exist, such as the lack of tools for editing or deleting audio files, but agents can likely work around this by using other methods or the provided tools effectively for most common tasks.
Average 3.3/5 across 8 of 8 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- 0 of 1 community issues answered or closed in the last 6 months
- 0 commits in the last 12 weeks
- Last stable release on October 16, 2025
- 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.
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{
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}
```
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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?
No annotations are provided, so the description must cover behavior. It only discusses model limitations; it omits important traits like processing time, authentication needs, or that the tool generates a response.
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 brief (three sentences) and front-loaded with the core purpose. It avoids unnecessary words, but could be better structured by grouping model advice separately.
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 having an output schema and four parameters, the description does not explain the output or the optional parameters (system_prompt, user_prompt). The advice on models is helpful but overall incomplete for effective tool invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so description must explain parameters. It covers the model parameter with recommendations, but input_file_name, system_prompt, and user_prompt receive no explanation beyond what the schema provides.
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 chats with audio files, which distinguishes it from sibling tools like compress or transcribe. However, it does not specify what type of response is returned (e.g., text or audio), leaving slight ambiguity.
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?
Only recommends a default model and warns about the mini variant, but does not explain when to use this tool versus alternatives or provide context on prerequisites like audio file format.
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?
With no annotations, the description should disclose behavioral traits like output format, file storage behavior, or rate limits. It only states it creates audio, lacking details on whether it saves to disk, returns a URL, or any potential side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, but it is too terse to be informative. It omits important details about parameters, behavior, and usage, making it under-specified rather than concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 6 parameters and no schema descriptions, the description is severely incomplete. It does not mention the output schema, return value, or any context about the API being called (e.g., authentication, latency). An agent would lack critical information to invoke this tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must add meaning to parameters. It only mentions model and voice selection, ignoring text_prompt, instructions, speed, and output_file_name. No parameter-level details are provided beyond the schema names.
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 creates text-to-speech audio using OpenAI's TTS API, specifying model and voice selection. This distinguishes it from sibling tools like transcribe_audio or compress_audio.
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 on when to use this tool versus alternatives like transcribe_audio or chat_with_audio. It does not mention prerequisites, such as API key requirements, or scenarios where this tool is preferred.
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?
Without annotations, the description implies a read-only operation (list/filter) but does not explicitly state that no modifications occur. It also lacks information about required permissions, error handling, or behavior when the audio path is empty or invalid.
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 a single sentence that front-loads key actions and capabilities. It is efficient but could be slightly more structured with bullet points for clarity.
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?
Given the many filter parameters and the existence of an output schema, the description does not explain the return structure or pagination. It also omits context about the 'audio path' (fixed or parameter).
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 10 parameters and 0% schema description coverage, the description only lists high-level categories (regex, metadata filters, sorting) without detailing what each parameter does. For instance, 'pattern' is not explained as a regex, and range parameters lack format hints.
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 uses clear action verbs ('List, filter, and sort') and specifies the resource ('audio files from the audio path'). However, it does not differentiate this tool from siblings like 'get_latest_audio' which could also list files.
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 its siblings or alternatives. There is no mention of prerequisites, limitations, or when not to use this tool.
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 provided; the description only mentions model recommendations and prompt usage. It does not disclose behaviors like rate limits, data handling, or side effects.
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 relatively short with three sentences. It is front-loaded with the core purpose, but the opening sentence is somewhat generic.
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?
An output schema exists but is not referenced. For a tool with 5 parameters and multiple enum options, the description lacks detail on response formats and required fields.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, and the description adds little value beyond the schema. Only 'prompt' is briefly mentioned; parameters like 'input_file_name', 'response_format', and 'timestamp_granularities' are not explained.
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?
Clearly states the tool transcribes audio files. The verb 'transcribe' and resource 'audio files' are specific, but it does not differentiate from sibling tools like 'transcribe_with_enhancement'.
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?
Provides model selection guidance (default, performance, rare use) but does not address when to use this tool over alternatives like 'chat_with_audio' or 'transcribe_with_enhancement'.
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 cover behavioral details. It only states the conversion action without mentioning side effects (e.g., file overwriting), input/output constraints, or required permissions. Essential transparency for a mutation tool is missing.
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, concise sentence (18 words) that conveys the core purpose without unnecessary words. It is front-loaded and efficient.
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 simplicity of the tool (3 parameters, conversion task) and the presence of an output schema, the description is partially complete. However, it omits details on input file format requirements, error scenarios, and output file naming conventions, which are needed for full autonomy.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description needs to explain parameters thoroughly. It implies that input_file_name is the source audio, target_format is mp3/wav, and output_file_name is optional, but does not provide specific format details, default behaviors, or constraints 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's purpose: converting audio files to mp3 or wav formats, which are compatible with gpt-4o. It specifies the verb 'convert' and the resource 'audio files', and distinguishes it from sibling tools like transcribe_audio or compress_audio.
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 does not mention when conversion is appropriate, nor does it exclude cases where other tools like compress_audio or transcribe_audio might be better. The only implied usage is for format conversion.
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 carry full burden. It discloses enhancement behavior and model usage (GPT-4), but does not mention side effects like cost, latency, authentication needs, or prerequisites (e.g., file upload). Critical operational details are missing.
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?
Description is well-structured with a summary, enhancement type list, Args, and Returns sections. It is front-loaded with purpose. Some redundancy in repeating parameter names from schema, and the 'Returns' line is vague. Overall efficient but not maximally concise.
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 5 parameters, no annotations, and a sibling transcribe tool, the description covers the main functionality and enhancement types. However, it lacks output schema details, prerequisites, and explanation of defaults (e.g., enhancement_type defaults to 'detailed'). Adequate for basic use but incomplete for nuanced decisions.
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 0%, requiring the description to add meaning. The 'Args' section gives brief explanations (e.g., 'Name of the input audio file to process'), which is adequate but not rich. With 5 parameters and enums partially conveying meaning, the description adds marginal value but does not fully compensate for the coverage gap.
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 'Transcribe audio with GPT-4 using specific enhancement prompts', specifying verb and resource, and distinguishes from siblings like transcribe_audio by adding enhancement capability. Lists four distinct enhancement types with their purposes.
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?
Implies usage when enhanced transcription is needed through the list of enhancement types, but lacks explicit guidance on when to use this tool versus the transcribe_audio sibling. No exclusions or alternatives are mentioned beyond the implied difference.
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 burden. It reveals the action (compression) and trigger condition (>25MB), but does not detail output format, quality impact, or reversibility. However, the parameter output_file_name hints at a new file, so it's fairly transparent.
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 sentences, no waste. Could combine the conditional into a single sentence, but it remains efficient and front-loaded with purpose.
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?
Provides core purpose and usage conditions, but lacks detail on parameters and output schema. Since output schema exists, return value explanation is not required, but parameter explanations are missing for a tool with 3 parameters.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 0% description coverage, so description must explain parameters. It mentions file size >25MB but does not explain max_mb (target size) or output_file_name, missing a chance to clarify their roles.
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 compresses audio files larger than 25MB, with a specific verb ('compress') and resource ('audio files'), and distinguishes from sibling tools by its compression focus.
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 states when to use: only when user requests compression or when other tools fail due to file size. This provides clear context and exclusions, guiding the agent appropriately.
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 behavioral traits. It only states 'from the audio path' without defining the path, how 'most recent' is determined, or what the output format is. This lacks sufficient detail for an agent to understand the tool's 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?
The description is extremely concise with two sentences, no wasted words, and a clear directive. Every sentence adds value.
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 tool has no parameters and an output schema (unknown content). The description is minimal and lacks context about the 'audio path' and how the latest is determined. While simple, it leaves gaps for an agent that may need more context about the environment.
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?
There are no parameters, so schema coverage is 100%. The description does not need to add param info, and the baseline for no parameters is 4. No additional semantics are required.
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 retrieves the most recent audio file, using specific verb 'get' and resource. It differentiates from sibling tools by specifying a unique action (getting the latest file) and includes an explicit usage condition.
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 provides a usage guideline: 'ONLY USE THIS IF THE USER ASKS FOR THE LATEST FILE.' This tells the agent exactly when to invoke the tool and implicitly when not to, which is sufficient for decision-making.
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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