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

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

• Connection: Runs in the cloud
• Transport: Streamable HTTP
• Category: Design, images & audio

Install

Pick the AI app you use, then copy the settings.

Passed 5/5 checks · What you ask goes through the maker’s server (tanod.dev)

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"
    }
  }
}

Which apps can use it

Based on how this server connects and on each app’s own docs, checked Nov 10, 2026.

Connect over the internet in

ClaudeChatGPTGooseLM StudioMsty StudioCherry StudioCursorVS Code + GitHub CopilotVisual StudioWindsurf (Devin Desktop)ZedKiroClineContinueJetBrains AI AssistantClaude CodeCodex CLIGemini CLIOpenCodenodecodeWarpLibreChatOpen WebUIRaycast

All apps, with pros and cons →

What it can do

Tool names and descriptions exactly as the server reported them on Nov 9, 2026. The maker wrote them, not us; we show them as plain text.

• 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

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 11, 2026

• ✓ Pass

  Maker matches the repo

  The registry name was verified against the domain tanod.dev (the registry checks domain ownership)

• ✓ Pass

  Still maintained

  Code last changed yesterday

• ✓ Pass

  Has a licence

  Uses MIT

• ✓ Pass

  Endpoint responds

  Responded in 306 ms (HTTP 200)

• – Not applicable

  Sign-in with OAuth

  No sign-in needed

• ✓ Pass

  No hidden instructions in tool descriptions

  Read 19 tool descriptions and found no hidden instructions to the AI

User reviews

No reviews yet

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Compared with others in Design, images & audioi

• More stars than 77% of the other 2908 servers in this category
• Published to the registry more recently than 94% of the other 2908 servers in this category
• Basic checks equal to or better than 100% of the other 2908 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

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README from the maker

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

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

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

npx -y tanod-mcp

claude mcp add tanod -- npx -y tanod-mcp

claude mcp add tanod -e TANOD_PRIVATE_KEY=0x... -e TANOD_MAX_USD_PER_DAY=0.50 -- npx -y tanod-mcp

{

{

{