transcribe-mcp
transcribe-mcp
Расшифровывайте аудио и видео, и находите тишину, которую стоит вырезать — из любого MCP-клиента. Никакой учетной записи, никакого API-ключа, никакой подписки. Ваш агент платит долю цента за вызов, в USDC, прямо из кошелька, которым вы управляете.
{
"mcpServers": {
"transcribe": {
"command": "npx",
"args": ["-y", "transcribe-mcp"],
"env": { "TRANSCRIBE_MCP_WALLET_KEY": "0x..." }
}
}
}Это вся настройка. Нигде не нужно регистрироваться.
Tools
tool | what it does | price |
| публичный URL аудио/видео → текст, тайминги, определенный язык | $0.05 |
| субтитры или тайминги → отрезки, которые стоит вырезать, и сколько вы сэкономите | $0.002 |
| цены, лимиты и как работает оплата | free |
find_dead_air принимает именно то, что возвращает transcribe_url, так что они работают в цепочке: расшифруйте выступление, затем вырежьте из него тишину.
"cuts": [{ "from": 2.55, "to": 10.85, "seconds": 8.3, "reason": "silence" }],
"stats": { "sourceSeconds": 20, "speechSeconds": 5.05, "wouldSavePercent": 74.75 }Related MCP server: dtelecom-stt
Почему оплата за вызов вместо API-ключа
Потому что агент не может заполнить форму регистрации. x402 — это HTTP-нативная оплата: сервер отвечает 402 Payment Required с ценой, клиент платит, запрос проходит. Никакой панели управления, никакой ротации ключей, никакого минимума, никакого ежемесячного счета. Если вы никогда не вызываете его, вы ничего не платите.
Расчеты производятся в USDC на Base (eip155:8453).
Запуск без кошелька
Он запускается нормально. shop_info отвечает, tools/list работает, а платные инструменты сообщают, что нужно настроить. Посмотрите, прежде чем пополнять что-либо.
Чего он не будет делать
Он не будет тратить сверх лимита. Каждый вызов фильтруется через потолок в $0.10; предложение выше него отклоняется, и клиент отказывается платить, а не угадывает.
Он не возьмет с вас плату за отказ. Неверные аргументы перехватываются локально и никогда не покидают вашу машину.
4xxот магазина никогда не проводится.Он не солжет о продаже. Если заголовок расчета отсутствует, он сообщает об этом, а не сообщает о платеже, который не может подтвердить.
Файлы ограничены 800 КБ. Слишком большие отклоняются бесплатно, до оплаты.
Конфигурация
variable | meaning |
| приватный ключ кошелька с небольшим количеством USDC на Base. Требуется только для платных инструментов. |
Используйте выделенный кошелек с несколькими долларами, а не ваш основной. Это верно для любого инструмента, которому вы передаете приватный ключ, и здесь это тоже верно.
Tests
npm testMIT.
Available Tools
3 toolsfind_dead_airFind the silence worth cuttingA
Given subtitles or timings, return the spans of dead air and how much of the runtime cutting them would save. Costs $0.002 per call — a tenth of transcription, because the expensive half is not redone. Feed it the segments transcribe_url returns.
| Name | Required | Description | Default |
|---|---|---|---|
| vtt | No | WebVTT subtitles. Use this or segments, not both. | |
| segments | No | Timings as [{start, end, text}] in seconds — exactly what transcribe_url returns. | |
| durationSeconds | No | Total runtime in seconds. Improves the saving estimate; optional. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the per-call cost, that 'the expensive half is not redone' (implying caching or reuse), and that it returns spans and savings estimates. This is transparent for a read‑only analysis tool, though limits like max input size or error handling are not mentioned.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description delivers core purpose in the first sentence, then adds essential usage and cost info in a second sentence. No redundant words; every sentence earns its place. It is front‑loaded and efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has 3 optional parameters and no output schema. The description explains the input options (VTT or segments), the pipeline from transcribe_url, and the nature of the output (spans and saving estimate). It lacks a precise specification of the return format, but the description is sufficient for an agent to invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. The description adds context about using segments from transcribe_url and the cost, but does not significantly expand on what the parameters mean beyond the schema. The value comes from pipeline guidance rather than parameter detail.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns 'the spans of dead air and how much of the runtime cutting them would save,' which is a specific verb+resource. It distinguishes itself from the sibling 'transcribe_url' by focusing on silence detection rather than transcription.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly tells the agent to 'feed it the segments transcribe_url returns,' establishing a clear pipeline. It also highlights the cost advantage ($0.002 vs. transcription cost). However, it does not explicitly state when not to use this tool (e.g., if no subtitles or timings are available).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
shop_infoWhat this costs before you spend anythingA
Free. Returns the prices, the size limit, the chain and how payment works, so an agent can decide whether to call the paid tools at all.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description states the tool is free, which is a key behavioral trait. However, with no annotations, it doesn't disclose auth requirements, side effects, or whether results are cached or dynamic. This partial transparency is adequate for a simple cost-checking tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence front-loaded with 'Free.' It wastes no words while conveying purpose, output, and usage context. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given zero parameters and no output schema, the description sufficiently covers what the tool returns and when to use it. It lacks details about response format or authentication, but the low complexity makes it complete enough for a simple info tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With zero parameters, the input schema requires no explanation. The description adds value by specifying what the tool returns, which is not parameter semantics but does inform the agent's decision to call it.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns specific information (prices, size limit, chain, payment) and positions it as a free decision support tool before using paid tools. This distinguishes it from siblings transcribe_url and find_dead_air, which have different purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly tells the agent to use this tool before calling paid tools to decide if they are needed. While it doesn't list exclusions or alternative tools, the context of preventing unnecessary paid calls is clear and actionable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
transcribe_urlTranscribe audio or video from a URLA
Turn a publicly reachable audio or video URL into text with per-segment timings and a detected language. Costs $0.05 per call, paid in USDC from your own wallet. The file must be under 800 KB; oversized files are refused for free before any payment.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Public http(s) URL of the audio or video file. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses cost, payment method, file size limit, and refusal policy for oversized files. It also describes output behavior (per-segment timings and language detection). Missing details on error handling for unreachable URLs but overall provides key behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, front-loaded with purpose, each sentence adding non-redundant information (purpose, cost, size limit). Concise without missing critical details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter tool with no output schema and no annotations, the description covers purpose, cost, size limit, and output shape (timings, language). It is sufficient for an agent to decide to invoke, though more detail on output format would increase completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with a basic param description. The tool description adds value beyond the schema: constraints (public reachable, <800KB), cost, and output details. This helps an agent understand the parameter's context and limits.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool transcribes audio/video from a URL into text with per-segment timings and detected language. The verb 'transcribe' and resource 'audio or video from a URL' are specific and distinct from unrelated sibling tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description tells when to use: for public URLs needing transcription with timings and language detection. It provides important constraints (file <800KB, cost $0.05, USDC payment). However, it does not explicitly state when to avoid the tool or suggest alternatives, though siblings are unrelated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
3 tool updates
v0.1.2- First observed
find_dead_air - First observed
shop_info - First observed
transcribe_url
TDQS
Each tool serves a completely distinct role: transcribe_url handles audio-to-text, find_dead_air post-processes timings to find silence, and shop_info provides pricing metadata. There is zero overlap in purpose.
Tool names follow a clear verb_noun pattern (transcribe_url, find_dead_air, shop_info) with snake_case. shop_info deviates slightly from the domain-action style of the others, but it is still predictable and readable.
Three tools is perfectly scoped for this server: a core action (transcribe), a derived analysis (find_dead_air), and informational helper (shop_info). No tool feels extraneous, and the count matches the narrow purpose well.
The tool surface covers the key transcription workflow and provides a helpful cost info tool. An obvious gap is the lack of an update or delete operation for transcripts, but for a consumption-oriented service this is reasonable. The surface feels complete for its intended use.
Maintenance
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Related MCP Connectors
AI transcription from URLs or files. 119 languages, diarization, SRT/VTT/text export.
Verbatim transcription of public video/audio URLs to clean text, SRT, and timestamped records.
Pay-per-use AI and data tools via x402: image, video, music, voice, search, crypto. USDC.
Transcribe audio & video to text for AI agents: 100+ languages, speaker labels, webhooks.
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