dev.tanod/text

up checked 2026-11-11T07:39:09.732Z

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Publisher-provided title

Text Analysis & Cleanup Tools (Tanod)

Publisher-provided description

Analyse and clean up text: stats, language, keywords, summary, sentiment, PII redaction, diff.

Summary by lookupd

Analyses and cleans text: stats, language detection, keywords, summaries, sentiment, PII redaction, diffs, secret scanning and PDF…

Install

Claude Code, remote (streamable-http)

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

Codex, remote (streamable-http)

codex mcp add text --url https://tanod.dev/mcp/text

Cursor, remote (streamable-http) → .cursor/mcp.json

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

Other clients: docs. Or tell your agent: Set up lookupd for me: read https://lookupd.com/agent.md and follow it.

Tools (27 tools)

Publisher-provided 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, se
• 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 al
• 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 (smal
• 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`
• 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
• 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_entit
• 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.
• 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,
• 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 nev
• 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 keyp
• 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 mos
• 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_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 (pysp
• 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-orien
• 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 cu
• text_lorem utilpeek: Generate seeded lorem ipsum or English placeholder text. For random values use generate_random. Returns placeholder text: classic lorem ipsum or Engli
• 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:
• 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 transliterate
• 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, stri
• 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
• 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 converte
• 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 profile
• 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
• 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
• 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_t
• 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 mos
• 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,

Details

• Version: 0.1.2
• Transports: streamable-http
• Uptime (7 days): 100%
• Median latency: 1867 ms
• First indexed: 2026-11-01T09:01:42.613Z
• Last seen in registry: 2026-11-09T14:17:23.853Z
• Repository: https://github.com/tanod-labs/tanod-mcp
• Website: https://tanod.dev

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