Tanod ML

Name: Tanod ML
Author: dev.tanod

MCP serverMCP

Local ML: speech to text (SRT/VTT), text embeddings, reranking, similarity, entities, zero-shot.

by dev.tanodhomepage ↗v0.1.1

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Evidence

Everything here was measured by our prober or read from a registry. Nothing is self-reported.

30-day availability47/47 checks ok

when	check	result	http	latency	detail
4 h ago	mcp initialize	ok	200	727 ms	server tanod-ml 0.1.0 · protocol 2025-06-18 · 18 tools
10 h ago	mcp initialize	ok	200	265 ms	server tanod-ml 0.1.0 · protocol 2025-06-18 · 18 tools
17 h ago	mcp initialize	ok	200	721 ms	server tanod-ml 0.1.0 · protocol 2025-06-18 · 18 tools
28 h ago	mcp initialize	ok	200	275 ms	server tanod-ml 0.1.0 · protocol 2025-06-18 · 18 tools
35 h ago	mcp initialize	ok	200	716 ms	server tanod-ml 0.1.0 · protocol 2025-06-18 · 18 tools
2 d ago	mcp initialize	ok	200	268 ms	server tanod-ml 0.1.0 · protocol 2025-06-18 · 18 tools
2 d ago	mcp initialize	ok	200	758 ms	server tanod-ml 0.1.0 · protocol 2025-06-18 · 18 tools
2 d ago	mcp initialize	ok	200	815 ms	server tanod-ml 0.1.0 · protocol 2025-06-18 · 18 tools
3 d ago	mcp initialize	ok	200	294 ms	server tanod-ml 0.1.0 · protocol 2025-06-18 · 18 tools
3 d ago	mcp initialize	ok	200	711 ms	server tanod-ml 0.1.0 · protocol 2025-06-18 · 18 tools

Tools observed via MCP handshake

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 bg
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 ba
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-L
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 th
extract_entities — mlpeek: Extract named entities: people, organisations, places, dates, money, ... For fields of your own schema use extract_json_with_schema. Returns named entit
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 wit
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_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 keyp
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 mos
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 al
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 transla
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. Retu
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_
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
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`
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
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 tran
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

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Official MCP Registrydev.tanod/ml

first seen 13 d ago · fetched 4 h agoopen ↗

Field history (6 recent events)

13 d ago · declared · summary → "Local ML: speech to text (SRT/VTT), text embeddings, reranking, similarity, entities, zero-shot."
13 d ago · declared · description → "Local ML: speech to text (SRT/VTT), text embeddings, reranking, similarity, entities, zero-shot."
13 d ago · declared · publisherName → "dev.tanod"
13 d ago · declared · homepageUrl → "https://tanod.dev"
13 d ago · declared · version → "0.1.1"
13 d ago · declared · protocols → ["mcp"]

Tanod ML

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