Text Analysis & Cleanup Tools (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: Other

Install

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

You need

• Runs in the cloudNothing to install; it connects over the internet

Which app do you use?

https://tanod.dev/mcp/text

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

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

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

https://tanod.dev/mcp/text

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Tool names and descriptions exactly as the server reported them on Nov 6, 2026. The maker wrote them, not us; we show them as plain text.

• text_stats

  utilpeek: Count words and score the readability of a text. For word frequencies use text_frequency. Returns counts (characters, letters, words, unique words, sentences, paragraphs, averages), reading and speaking time, and for English the Flesch reading ease, Flesch-Kincaid grade

• 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

• text_case

  utilpeek: Convert text case: title, sentence, snake, camel, kebab, ... For a URL slug use make_slug. Returns the text in another case: upper, lower, title (small words such as a, of, the kept lower-case; iPhone and domains kept), sentence, camel, pascal, snake, kebab, constant, d

• text_lines

  utilpeek: Sort, dedupe, trim, shuffle or number the lines of a text. For find and replace use text_replace. Returns the text's lines transformed by `operations` in order: trim, remove_empty, dedupe (keeps the first), sort (lexical, natural or length; stable), reverse, shuffle (se

• text_replace

  utilpeek: Find and replace in text. To sort, dedupe or number lines use text_lines. Returns the text with `find` replaced: literally, or as a regular expression on RE2 only (linear time for any pattern: no backtracking, so no ReDoS; backreferences and lookaround are refused), wit

• text_extract

  utilpeek: Extract emails, URLs, domains, IPs, dates and crypto addresses from text. To mask personal data use redact_pii; for names and places use extract_entities. Returns the emails, URLs, domains (ICANN suffixes), IPv4 / IPv6, phone-like numbers, hashtags, mentions, EVM and BT

• redact_pii

  utilpeek: Redact PII from text: emails, phone numbers, card numbers, IBANs, SSNs, IPs and crypto addresses. For API keys and tokens use check_text_for_secrets. Returns the text with emails, phone numbers, payment cards (Luhn), IBANs (mod-97), US SSNs, IPv4 / IPv6, EVM / BTC addre

• pseudonymize_pii

  utilpeek: Pseudonymise PII in text: stable keyed tokens for emails, phones, cards, IBANs and IPs. Returns the text with each email, phone number, payment card, IBAN, US SSN, IPv4 / IPv6, EVM / BTC address and secret URL query value replaced by a typed token (EMAIL_843eca1f055c),

• text_frequency

  utilpeek: Count word and n-gram frequencies in a text. For scored keyphrases use text_keywords. Returns the most frequent words or 2-3 word n-grams (n-grams never cross punctuation) with count and share, dropping stop words (en, es, fr, de, pt, it, nl, or none), and optionally ch

• 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

• 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_spellcheck

  utilpeek: Spellcheck a text with suggestions. For counts and readability use text_stats. Returns the words not in the language's word-frequency dictionary (pyspellchecker), with positions (offset, line, column) and up to `max_suggestions` suggestions by edit distance; numbers, ac

• text_unicode_inspect

  utilpeek: Find invisible, bidi and homoglyph Unicode characters in text. For injected instructions use check_text_for_prompt_injection. Returns a security-oriented Unicode inspection: `risk` (low / medium / high) with reasons; invisible characters (zero-width, tag characters that

• text_numbers

  utilpeek: Convert numbers to English words. For units use convert_units. Returns a number spelled out in English (cardinal, ordinal, year, or currency for 13 currencies, rounded half-even to the minor unit) up to 10^36, or English number words ("two hundred and fifty-first") pars

• text_lorem

  utilpeek: Generate seeded lorem ipsum or English placeholder text. For random values use generate_random. Returns placeholder text: classic lorem ipsum or English filler, in words (at most 10,000), sentences (1,000) or paragraphs (100), deterministic for a `seed` (echoed back). I

• diff_text

  utilpeek: Diff two texts: unified line diff, word or character changes. For PDF or Word files use compare_documents. Returns the differences between two texts: similarity ratio, counts and hunks; in line mode a git-compatible unified diff, in word or char mode change records (equ

• make_slug

  utilpeek: Make a URL slug from text. For other case styles use text_case. Returns a URL slug: accents removed, Latin ligatures, Greek and Cyrillic transliterated, apostrophes dropped and other characters turned into one separator, cut at a separator when over max_length. CJK, Ara

• markdown_to_html

  utilpeek: Convert Markdown to sanitized HTML. For the reverse use html_to_text. Returns the HTML of a Markdown document (markdown-it-py; `gfm` adds tables, strikethrough, autolinks and task lists), cleaned by an allow-list sanitizer (nh3): no scripts, event handlers, iframes, for

• html_to_text

  utilpeek: Convert HTML to Markdown or plain text. To fetch a URL use render_page; for the reverse use markdown_to_html. Returns the readable content of an HTML document you send (the HTML itself, never a URL: nothing is fetched) as Markdown or plain text, with title and descripti

• convert_subtitles

  utilpeek: Convert subtitles between SRT, WebVTT and JSON, with a time shift. To make subtitles from audio use transcribe_audio. Returns a subtitle file converted between SRT, WebVTT and a JSON segment list [{start, end, text}] (seconds, as /v1/audio/transcribe returns it), with a

• check_subtitle_delivery

  utilpeek: Check subtitle delivery timing, line length and reading speed. Returns a cue-level SRT/WebVTT delivery review with versioned language/audience profiles: en-US-adult-v1, en-US-children-v1, ja-adult-v1 and ar-adult-v1. Checks overlaps, minimum gaps, duration, line length,

• pdf_redact

  pdfpeek: Redact a PDF: black out terms, emails, phone numbers, card numbers and areas. To test a file redacted elsewhere use pdf_redaction_check. Returns a PDF whose redacted pages are images, so the text cannot be copied back; metadata is removed. Input: at least one of `terms`,

• pdf_redaction_check

  pdfpeek: Check a PDF for failed redactions: black boxes with the text still under them. To redact properly use pdf_redact. Returns whether black boxes in a PDF still have readable text under them: per finding the page, box (PDF points), character count and hidden text (or only it

• check_text_for_secrets

  utilpeek: Scan text for leaked secrets: API keys, tokens, private keys and seed phrases. For personal data use redact_pii; for injected instructions use check_text_for_prompt_injection. Returns every API key, token, private key or BIP-39 seed phrase in a text (AWS, GitHub, GitLab

• check_text_sources

  findpeek: Check a text for copied sentences: web pages that contain them word for word. For a general query use web_search. Returns for up to 10 of a text's most distinctive sentences, the web pages that contain each word for word (exact-phrase search, up to 5 each, with snippet)

• check_text_duplicate

  utilpeek: Check whether a draft repeats your earlier texts. Returns the duplicate, near or new verdict for a draft compared with 1-50 earlier texts you supply, plus all collisions and closest matches with similarity, rare shared terms and shared numbers. Cosine of words and word

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 6, 2026

• ! Caution

  Maker matches the repo

  The registry lists no source repo, so we cannot match the publisher (tanod.dev) to the code

• … Not checked yet

  Still maintained

  We have not read this repo from GitHub yet

• … Not checked yet

  Has a licence

  We have not read this repo from GitHub yet

• ✓ Pass

  Endpoint responds

  Responded in 693 ms (HTTP 200)

• – Not applicable

  Sign-in with OAuth

  No sign-in needed

• ✓ Pass

  No hidden instructions in tool descriptions

  Read 27 tool descriptions and found no hidden instructions to the AI

Compared with others in Otheri

• More stars than 0% of the other 32505 servers in this category
• Published to the registry more recently than 90% of the other 32505 servers in this category
• Basic checks equal to or better than 37% of the other 32505 servers in this category

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

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

README from the maker

No README found in the repo