MCP server

Tanod ML Official

dev.tanod/ml

Checked 25 Oct 2026, 20:29 UTC · Connected to it and listed its tools

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

Website

At a glance

Type

MCP server

Publisher

tanod.dev (verified by the MCP Registry)

Status

Working at last check

Tools

18

Token cost

About 10,384 tokens per request

Sets up in

Claude Code, Claude Desktop, Codex, Gemini CLI, Cursor, VS Code, Antigravity, ChatGPT, OpenAI API, Grok

Last checked

25 Oct 2026, 20:29 UTC

Connect it to your agent

Add to CursorAdd to VS CodeVS Code Insiders

Opens the app, which shows the server and asks you to confirm. For other apps, copy the setup below.

Claude Code

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

Claude Desktop

Settings → Connectors → Add custom connector, and paste the URL.

Codex (config.toml)

[mcp_servers.ml]
url = "https://tanod.dev/mcp/ml"

Gemini CLI

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

Cursor

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

VS Code

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

Antigravity

agy mcp add ml https://tanod.dev/mcp/ml

ChatGPT

Settings → Apps & Connectors → Advanced settings: turn on Developer mode, then Create a connector and paste the URL. Which plans can do this varies.

OpenAI API (Responses tools)

{"type":"mcp","server_label":"ml","server_url":"https://tanod.dev/mcp/ml","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"ml","server_url":"https://tanod.dev/mcp/ml"}

Tools

• check_text_for_prompt_injection ~365 tokens
  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), automated and heuristi
• classify_zero_shot ~468 tokens
  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), `multi_label` scores eac
• detect_language ~269 tokens
  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` is true only with at
• embed_multimodal ~863 tokens
  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, `image_detail` low/sta
• embed_texts ~598 tokens
  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 the model's query or p
• extract_entities ~435 tokens
  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, DATE, MONEY, ...) and
• extract_json_with_schema ~478 tokens
  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`, `usage` (tokens) and `ms
• rerank_documents ~430 tokens
  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 logit; `top_k` keep
• text_keywords ~347 tokens
  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 count. Typically un
• text_sentiment ~280 tokens
  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 or less), optionall
• text_similarity ~593 tokens
  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 `similarity`) or 1-50 `pairs
• text_summarize ~379 tokens
  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-sentence scores. It does n
• text_to_speech ~778 tokens
  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 languages (the voice sets t
• transcribe_audio ~884 tokens
  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; `speakers` labels
• transcribe_podcast_episode ~820 tokens
  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`, optional episode selec
• translate_document ~861 tokens
  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 `<!-- page N -->` marker per page, source language and per-p
• translate_speech ~1057 tokens
  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, es, fr, de, pt, it, n
• translate_text ~479 tokens
  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 (other pairs pivot throug

Browse

• PDF tools
• OCR (text from images)
• Converting to Markdown
• Text to speech

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