Text Analysis & Cleanup Tools (Tanod)

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

• 连接方式: 在云端运行
• 传输方式: Streamable HTTP
• 分类: 其他

安装

选择你使用的 AI 应用，然后复制设置。

你需要

• 在云端运行无需安装任何东西，通过互联网连接

你用的是哪个应用？

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

你的选择通过 Cookie 保存在这台设备上，无需账户。

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同，请以 README 为准。

它能做什么

工具名称和描述与服务器在 2026年11月6日 报告的完全一致。这些内容由作者撰写，不是我们写的；我们以纯文本形式显示。

• 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

基础检查i

我们检查的是任何人都能看到的信息；我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月6日

• ! 注意

  作者与仓库一致

  注册中心没有列出源代码仓库，因此无法将发布者（tanod.dev）与代码对应起来

• … 尚未检查

  仍在维护

  我们还没有从 GitHub 读取这个仓库

• … 尚未检查

  有许可证

  我们还没有从 GitHub 读取这个仓库

• ✓ 通过

  端点有响应

  在 693 毫秒内响应（HTTP 200）

• – 不适用

  使用 OAuth 登录

  无需登录

• ✓ 通过

  工具描述中没有隐藏指令

  已读取 27 个工具描述，未发现针对 AI 的隐藏指令

与“其他”中的其他服务器比较i

• 星标数多于此分类中其他 32505 个服务器的 0%
• 发布到注册中心的时间比此分类中其他 32505 个服务器的 90% 更新
• 基础检查结果等于或优于此分类中其他 32505 个服务器的 37%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时，星标数才计入。

替代选择

同一分类中其他作者的服务器，按星标数排序。

如何看卡片：★ GitHub 星标i三大支柱：评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

作者的 README

仓库中没有找到 README