Text Analysis & Cleanup Tools (Tanod)

할 수 있는 일

이 서버는 아직 쉬운 말로 된 요약을 쓰지 못했어요. 아래는 레지스트리에서 읽어 온 사실이에요.

• 연결 방식: 클라우드에서 실행
• 전송 방식: Streamable HTTP
• 카테고리: 기타

설치

쓰고 있는 AI 앱을 고른 다음 설정을 복사하세요.

필요한 것

• 클라우드에서 실행설치할 것 없음. 인터넷으로 연결돼요

어떤 앱을 쓰세요?

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

선택한 앱은 이 기기의 쿠키에만 저장돼요. 계정은 필요 없어요.

이 설정은 제작자의 레지스트리 정보로 만들었어요. README와 다르면 README를 따르세요.

할 수 있는 일

도구 이름과 설명은 서버가 2026년 11월 6일에 알려 준 그대로예요. 우리가 아니라 제작자가 쓴 글이고, 일반 텍스트로만 보여 드려요.

• 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

기본 점검i

누구나 볼 수 있는 정보를 점검했고, 코드는 읽지 않았어요. 해당하지 않는 항목은 개수에서 뺐어요. · 2026년 11월 8일 점검

• ! 주의

  제작자와 저장소 일치

  레지스트리에 소스 저장소가 없어서, 게시자(tanod.dev)와 코드를 맞춰 볼 수 없어요

• … 아직 점검 안 함

  계속 관리되고 있음

  아직 GitHub에서 이 저장소를 읽어 오지 않았어요

• … 아직 점검 안 함

  라이선스 있음

  아직 GitHub에서 이 저장소를 읽어 오지 않았어요

• ✓ 통과

  엔드포인트 응답

  693ms 만에 응답(HTTP 200)

• – 해당 없음

  OAuth로 로그인

  로그인 필요 없음

• ✓ 통과

  도구 설명에 숨겨진 지시 없음

  도구 설명 27개를 읽었고, AI에게 보내는 숨겨진 지시는 없었어요

기타 카테고리의 다른 서버와 비교i

• 이 카테고리의 다른 서버 18428개 중 0%보다 스타가 많아요
• 이 카테고리의 다른 서버 18428개 중 90%보다 최근에 레지스트리에 공개됐어요
• 기본 점검 결과가 이 카테고리의 다른 서버 18428개 중 36%와 같거나 더 좋아요

레지스트리에서 이 카테고리의 활성 서버 전체와 비교했어요. 스타는 게시자가 저장소를 소유한 경우에만 세요.

대안

같은 카테고리에서 다른 제작자가 만든 서버예요. 스타 순으로 정렬했어요.

카드 보는 법:★ GitHub 스타i세 기둥: 리뷰 · 점검 · 사용 정도i업데이트 = 최신 버전을 레지스트리에 공개한 날i

제작자의 README

저장소에서 README를 찾지 못했어요