Embeddings, Rerank & Speech-to-Text (Tanod)

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

• 连接方式: 在云端运行
• 传输方式: Streamable HTTP
• 分类: 设计、图片与音频

安装

选择你使用的 AI 应用，然后复制设置。

你需要

• 在云端运行无需安装任何东西，通过互联网连接

你用的是哪个应用？

https://tanod.dev/mcp/ml

claude mcp add --transport http ml https://tanod.dev/mcp/ml

{
  "mcpServers": {
    "ml": {
      "url": "https://tanod.dev/mcp/ml"
    }
  }
}

{
  "servers": {
    "ml": {
      "type": "http",
      "url": "https://tanod.dev/mcp/ml"
    }
  }
}

https://tanod.dev/mcp/ml

你的选择通过 Cookie 保存在这台设备上，无需账户。

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同，请以 README 为准。

它能做什么

工具名称和描述与服务器在 2026年11月6日 报告的完全一致。这些内容由作者撰写，不是我们写的；我们以纯文本形式显示。

• embed_texts

  mlpeek: Create text embeddings. For images too use embed_multimodal; for pair scores use text_similarity. Returns 384-dimension embeddings of 1-64 texts with bge-small-en-v1.5 (English) or multilingual-e5-small (about 100 languages), normalised by default; `input_type` applies th

• embed_multimodal

  mlpeek: Create multimodal embeddings: images and text in one vector space. For text only use embed_texts. Returns 768-dim vectors of texts and images (URL or base64) in ONE space (EmbeddingGemma 2), comparable by dot product; `dimensions` 512/256/128, `input_type` text prefix, `i

• rerank_documents

  mlpeek: Rerank up to 100 documents against a query. For pair scores use text_similarity. Returns 1-100 documents scored against a query by the ms-marco-MiniLM-L6-v2 cross-encoder (Apache-2.0) and sorted best first: index, rank, score (sigmoid of the logit, a relevance in 0-1) and

• text_similarity

  mlpeek: Score the semantic similarity of one or up to 50 text pairs. To rank documents against a query use rerank_documents. Returns the cosine similarity of the embeddings of each text pair (bge-small-en or multilingual-e5-small): one pair as `a` + `b` (also a top-level `similar

• extract_entities

  mlpeek: Extract named entities: people, organisations, places, dates, money, ... For fields of your own schema use extract_json_with_schema. Returns named entities in English text with spaCy en_core_web_sm 3.8.0 (MIT): text, label (the 18 OntoNotes types: PERSON, ORG, GPE, LOC, D

• classify_zero_shot

  mlpeek: Classify a text into 1-10 of your labels. For named entities use extract_entities. Returns scores of 1-10 caller-supplied labels for an English text with the nli-deberta-v3-xsmall NLI cross-encoder (Apache-2.0): single-label scores sum to 1 (softmax over the labels), `mul

• text_sentiment

  utilpeek: Analyze the sentiment of an English text. For your own labels use classify_zero_shot; for counts use text_stats. Returns VADER sentiment of an English text: compound (-1 to 1), positive / neutral / negative shares and a label (positive at 0.05 or more, negative at -0.05

• text_keywords

  utilpeek: Extract the keywords and keyphrases of a text. For raw frequencies use text_frequency; for names and places use extract_entities. Returns the top keyphrases of a text by RAKE (phrases between stop words and punctuation, scored by word degree / frequency), with score and

• text_summarize

  utilpeek: Summarize a text by picking its key sentences. For paper abstracts use paper_digest; for counts use text_stats. Returns an extractive summary: the most central `sentences` (or `ratio` of them) by LexRank, picked verbatim and kept in their original order, with per-senten

• detect_language

  utilpeek: Detect the language of a text. To translate use translate_text. Returns the most likely language (ISO 639-1 and 639-3 code, name, confidence) and 3 alternatives, from an offline ensemble of lingua (Apache-2.0) and langid.py (BSD-2-Clause) over 75 languages; `reliable` i

• denoise_audio

  mlpeek: Reduce background noise in speech audio. Returns mono 12 kHz WAV/MP3/Ogg speech audio with steady noise reduced by FFmpeg afftdn (no model weights), duration and filter. Public URL or base64, up to 25 MB and 10 minutes; base64 or output=url. Not voice isolation; music and

• transcribe_audio

  mlpeek: Speech to text and subtitles: transcribe audio to text, timed segments, SRT and WebVTT. For a podcast episode use transcribe_podcast_episode; to translate use translate_speech. Returns the transcript of audio of up to 10 min and 25 MB; `translate_to_english` gives English

• transcribe_podcast_episode

  mlpeek: Transcribe a podcast episode from its RSS feed: a window of up to 10 minutes as text, timed segments, SRT. For your own audio use transcribe_audio. Returns a transcript window (up to 10 min from start_s) of a podcast episode picked from its RSS feed. Input: `feed_url`, op

• translate_text

  mlpeek: Translate text between English and 14 languages. For a PDF use translate_document; for audio use translate_speech; to only name the language use detect_language. Returns the text translated between English and es, fr, de, pt, it, nl, ru, zh, ja, ko, ar, hi, id or tl (othe

• check_text_for_prompt_injection

  mlpeek: Check text for prompt injection before an agent acts on it. For hidden Unicode use text_unicode_inspect. Returns a `verdict`, a `score` (0-1; `injection` at 0.9 and above) and `chunks`, the flagged sentences with score and offsets. A small open classifier (DeBERTa-v3), au

• extract_json_with_schema

  mlpeek: Extract JSON from text that always matches your JSON Schema. For fixed entity types use extract_entities. Returns `data`, JSON extracted from the `text` that always parses and validates against your `json_schema` (decoded under a grammar built from it), plus `model`, `usa

• text_to_speech

  mlpeek: Speak text aloud in 41 voices: English, Spanish, French, Hindi, Italian, Portuguese. For audio to text use transcribe_audio. Returns the text spoken as audio. Offline neural TTS (Kokoro-82M); the audio is a synthetic voice. At most 2,000 characters; 41 voices in 6 languag

• translate_speech

  mlpeek: Translate speech: audio to English or 14 languages, optionally spoken. For a transcript in the spoken language use transcribe_audio. Returns speech translation of an audio file of up to 2 minutes: the language is detected and the speech returned as text in `target` (en, e

• translate_document

  pdfpeek: Translate a PDF document, digital or scanned, to English or 14 languages as Markdown. For plain text use translate_text. Returns each page's text (text layer or OCR) translated offline into `target` (default en) as Markdown with a ` ` marker per page, source language and

基础检查i

我们检查的是任何人都能看到的信息；我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月6日

• ! 注意

  作者与仓库一致

  注册中心没有列出源代码仓库，因此无法将发布者（tanod.dev）与代码对应起来

• … 尚未检查

  仍在维护

  我们还没有从 GitHub 读取这个仓库

• … 尚未检查

  有许可证

  我们还没有从 GitHub 读取这个仓库

• ✓ 通过

  端点有响应

  在 683 毫秒内响应（HTTP 200）

• – 不适用

  使用 OAuth 登录

  无需登录

• ✓ 通过

  工具描述中没有隐藏指令

  已读取 19 个工具描述，未发现针对 AI 的隐藏指令

与“设计、图片与音频”中的其他服务器比较i

• 星标数多于此分类中其他 2172 个服务器的 0%
• 发布到注册中心的时间比此分类中其他 2172 个服务器的 91% 更新
• 基础检查结果等于或优于此分类中其他 2172 个服务器的 43%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时，星标数才计入。

替代选择

同一分类中其他作者的服务器，按星标数排序。

如何看卡片：★ GitHub 星标i三大支柱：评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

作者的 README

仓库中没有找到 README