Tanod ML

Last 30 days

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Uptime

100%

Response time

366 ms typical, 847 ms slowest 5%

Last check

5 h ago

Next check

in 1 h

How to call it

Add it to any MCP client that supports remote servers.

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

15 tools

• embed_texts

  mlpeek: Create text embeddings. 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 the model's query or passage prefix. Input: `texts`, optional `model`, `norm

• rerank_documents

  mlpeek: Rerank up to 100 documents against a query. 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 logit; `top_k` keeps the best k. Eng

• text_similarity

  mlpeek: Score the semantic similarity of one or up to 50 text pairs. 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 `similarity`) or 1-50 `pairs`. Input: `a` + `b`, or `pairs`; eac

• extract_entities

  mlpeek: Extract named entities: people, organisations, places, dates, money, ... 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, DATE, MONEY, ...) and code-point offsets (end exclusive), plu

• classify_zero_shot

  mlpeek: Classify a text into 1-10 of your labels. 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), `multi_label` scores each label on its own; s

• text_sentiment

  utilpeek: Analyze the sentiment of an English text. 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 or less), optionally per sentence. A lexicon heuristic: it misses sarc

• text_keywords

  utilpeek: Extract the keywords and keyphrases of a text. Returns the top keyphrases of a text by RAKE (phrases between stop words and punctuation, scored by word degree / frequency), with score and count. Input: `text`, optional `language`, `top_n` and `max_words`. Typically unde

• text_summarize

  utilpeek: Summarize a text by picking its key sentences. Returns an extractive summary: the most central `sentences` (or `ratio` of them) by LexRank, picked verbatim and kept in their original order, with per-sentence scores. It does not paraphrase, shorten or check facts. Input:

• detect_language

  utilpeek: Detect the language of a 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` is true only with at least 20 lett

• transcribe_audio

  mlpeek: Speech to text and subtitles: transcribe audio to text, timed segments, SRT and WebVTT. Returns the transcript of an audio file (MP3, WAV, FLAC, OGG, Opus, M4A/AAC, WebM, MP4 audio) of up to 10 minutes and 25 MB: `text`, `language`, `duration`, timed `segments` and `srt`

• transcribe_podcast_episode

  mlpeek: Transcribe a podcast episode from its RSS feed: a window of up to 10 minutes as text, timed segments, SRT. Returns a transcript window (up to 10 min from start_s) of a podcast episode picked from its RSS feed: text, language, timed segments, srt and vtt with episode times

• translate_text

  mlpeek: Translate text between English and 14 languages. Returns the text translated between English and es, fr, de, pt, it, nl, ru, zh, ja, ko, ar, hi, id or tl (other pairs pivot through English): `text`, `source_lang` (detected when you omit `source`, then `detected` is true),

• check_text_for_prompt_injection

  mlpeek: Check text for prompt injection before an agent acts on it. 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), automated and heuristic: it can miss new attack

• text_to_speech

  mlpeek: Speak text aloud in 41 voices: English, Spanish, French, Hindi, Italian, Portuguese. Returns the text spoken as audio: `data_base64` (mp3, wav or ogg), `content_type`, `bytes`, `duration_s`, `sample_rate`, `voice`, `speed`. Offline neural TTS (Kokoro-82M); the audio is a

• translate_speech

  mlpeek: Translate speech: audio to English or 14 languages, optionally spoken. 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, es, fr, de, pt, it, nl, ru, zh, ja, ko, ar, hi, id, tl) with ti

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