whipscribe-mcp

by neugence

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Server Quality Checklist

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Latest release: v0.1.3
Disambiguation5/5
Each tool targets a distinct action: submitting jobs (file, URL, batch), checking status, fetching transcripts, and listing recent jobs. There is no overlap or ambiguity between tool purposes.
Naming Consistency5/5
All tool names follow a consistent 'verb_noun' pattern using snake_case (e.g., transcribe_file, get_job_status). The prefixes 'get_', 'list_', and 'transcribe_' are used uniformly.
Tool Count5/5
With 6 tools, the scope is well-scoped for a transcription MCP server. Each tool serves a distinct function without being overly granular or insufficient.
Completeness4/5
The core workflow of submitting transcription jobs (single file, single URL, batch) and retrieving results is covered. Missing operations like canceling or deleting jobs are minor gaps but not critical for the primary use case.
Average 4.1/5 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
- Last stable release on April 26, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is failing
This repository is licensed under Apache 2.0.
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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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?
Annotations already declare readOnlyHint=true, so the read-only nature is covered. Description adds 'poll' implying repeated calls are safe and stateless. However, it does not specify whether the call blocks or returns immediately, or describe possible status values. With annotations, bar is lower; a 3 is appropriate.
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?
Single sentence, front-loaded with purpose. Efficient and no wasted words. Could be slightly improved with bullet points or explicit status list, but concise enough.
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 a simple tool with 1 parameter and annotations present, the description is minimal but missing details like possible return statuses or whether it's a long poll. No output schema, so some behavioral info would help. Adequate but not thorough.
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 a clear description for job_id. The tool description adds context that job_id comes from a previous transcribe_* call, which is useful beyond the schema. No additional parameter details needed.
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 the specific verb 'poll' and clearly identifies the resource: current status of a transcription job by job_id. It distinguishes from siblings like get_transcript (getting content) and transcribe_* (starting jobs). No ambiguity.
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?
Description implies usage for checking status after a transcribe call, but does not explicitly state when to use this vs alternatives like get_transcript or list_recent_jobs. Lacks exclusions or direct sibling comparison, though context from sibling names helps.
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?
Annotations indicate the tool is not read-only and not destructive, but the description adds valuable context: it polls until completion (blocking call) and explicitly states the file path is never sent to telemetry. This goes beyond the annotations, though it could mention other side effects like network usage.
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 that cover the action, scope, and key behavioral traits (privacy, polling). Every word adds value with no fluff.
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?
While the description covers the main purpose and blocking behavior, it does not explain what the tool returns (e.g., transcript text, object). Since there is no output schema, the agent lacks information about the function's output format, making it incomplete for confident 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 coverage is 100% with all parameters described. The description adds no additional information about parameters beyond what is already in the schema, so it meets the baseline 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 the tool transcribes local audio or video files, which distinguishes it from siblings like transcribe_url (for remote URLs) and batch variants. The polling behavior is also mentioned, providing a complete picture of the tool's function.
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 for local files via the word 'local', but it does not explicitly state when to use this tool over alternatives or provide exclusion criteria. There is no mention of when not to use it or reference to 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?
Annotations provide readOnlyHint=true, and the description adds important behavioral details: returns a 300-character preview plus a link, and large transcripts are not inlined. This goes beyond what annotations provide and gives clear expectations about the response structure.
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, each serving a clear purpose: first states the core action and format capability, second explains output behavior. No 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?
For a simple tool with 2 parameters, no output schema, and clear annotations, the description covers the essential aspects: purpose, format flexibility, and return characteristics (preview + link, large transcript behavior). It is comprehensive enough 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 coverage is 100% with descriptions for both parameters. The description does not add significant meaning beyond what the schema already provides, e.g., the format parameter is fully enumerated in the schema. The 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?
The description clearly states the tool fetches finished transcripts in a requested format, specifying the verb and resource. It distinguishes from sibling tools like transcribe_file and transcribe_url which create transcripts, and list_recent_jobs/get_job_status which are for listing or checking status.
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 only for finished transcripts and notes that large transcripts are not inlined. However, it does not explicitly state when not to use it (e.g., for ongoing jobs) or mention alternative tools for checking job status before calling this tool.
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?
Beyond the readOnlyHint annotation, the description adds valuable behavioral details: it uses a local SQLite cache, stores only specific fields, and explicitly lists what is never stored. This manages expectations about data completeness and scope.
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 very concise, consisting of two sentences that front-load the main purpose and immediately follow with important caveats. Every sentence adds value without 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?
For a read-only listing tool with one parameter and no output schema, the description adequately covers what data is returned and what is excluded. Minor gap: missing details on ordering or pagination beyond the limit parameter, but still 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% coverage with a description for the 'limit' parameter. The tool description does not add additional meaning to the parameter, but the schema already suffices. 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 tool lists jobs from the local SQLite cache, specifying exact fields stored (job_id, source kind, status, duration, created-at) and explicitly what is not stored (URLs, file paths, transcripts). This distinguishes it from sibling tools like get_transcript or transcribe_file.
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 that this only lists jobs from the local machine, which helps the agent decide when to use it. However, it does not explicitly exclude other scenarios or mention alternative tools for different needs.
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 polling behavior ('Polls until the job is done or the poll timeout elapses') which is not captured by annotations. Annotations indicate the tool is neither read-only nor destructive, so the description adds value by explaining the asynchronous nature.
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. The first sentence states the core function, and the second provides behavioral context and output. Every word is necessary, and the structure is front-loaded for quick understanding.
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 (4 parameters, no output schema), the description covers the essential aspects: purpose, polling behavior, and output (preview + link). However, it could mention the format of the preview or potential error conditions to be fully 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 100%, so the baseline is 3. The description does not add any extra meaning beyond what is already in the parameter descriptions (e.g., 'url', 'diarize', 'language', 'word_timestamps'). No additional semantics 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 the tool's purpose: 'Transcribe audio or video from a URL'. It specifies supported URL types (direct media link, podcast episode, Creative-Commons YouTube) and distinguishes from sibling tools like transcribe_file and transcribe_urls_batch by focusing on single URL 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 clear context on when to use the tool (for URL-based media) and implicitly excludes file upload or batch processing. However, it does not explicitly mention alternatives or when not to use it, missing the opportunity to guide the agent away from inappropriate uses.
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 concurrency and failure behavior beyond annotations (readOnlyHint=false, destructiveHint=false). Adds value explaining parallel execution and partial failures.
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 filler, front-loaded with purpose, then details. Every word 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?
Covers key behavioral aspects for a batch tool. Missing output schema description, but the tool likely returns results immediately; context is adequate for invocation 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?
All parameters have schema descriptions (100% coverage). The description adds the batch context but does not elaborate on diarize, language, or word_timestamps 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?
Description clearly states the verb 'transcribe' and the resource 'a list of URLs', distinguishing it from sibling tools like transcribe_url (single URL) and transcribe_file (file).
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 mentions concurrency, batch size limit, and per-URL failure handling, providing good usage context. Could be improved by explicitly stating when not to use (e.g., single URLs).
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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