Tanod Text
Text tools: stats, language, case, lines, replace, extract, keywords, summary, sentiment, diff.
| Endpoint | https://tanod.dev/mcp/text (streamable-http) |
|---|---|
| Registry version | 0.1.0 |
| Now | 25 tools · grade A (98) · pin ts_d3af5db2a4eca0e844f91cfe3c81e95f · protocol 2025-06-18 |
| First / last listed | 2026-10-27 01:50 UTC / 2026-10-27 01:50 UTC |
| Grade over time | 2026-10-27: A (98), 25 tools |
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Tools: text_stats detect_language text_case text_lines text_replace text_extract redact_pii text_frequency text_keywords text_summarize text_sentiment text_spellcheck text_unicode_inspect text_numbers text_lorem diff_text make_slug markdown_to_html html_to_text convert_subtitles check_subtitle_delivery pdf_redact pdf_redaction_check check_text_for_secrets check_text_sources
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, Gunning fog, SMOG, Coleman-Liau and ARI scores (estimates from heuristic syllable counts; Dale-Chall is not offered). params: text*, lang |
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` is true only with at least 20 letters and confidence 0.6 or more. Short text is often flagged unreliable; text with no le params: text* |
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, dot or alternating; identifier styles split camelCase, acronym and digit boundaries per line. Typically under 0.3 s. Pric params: text*, to* |
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 (seeded, repeatable) and number; optionally the most frequent lines with counts. At most 200,000 lines. Typically under 0.3 params: text*, operations*, sort_mode, descending, case_insensitive, seed, number_start, number_separator, count_unique, top_n |
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), with \1 / \g<name> in the replacement; at most `max_replacements` (`limit_reached` reported). A pattern RE2 cannot compile params: text*, find*, replace*, mode, case_sensitive, multiline, dot_all, max_replacements |
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 BTC addresses (Base58Check / Bech32 checksums verified) and dates (normalised to ISO) found in the text, deduplicated and params: text*, types, max_per_type |
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 addresses and secret URL query values replaced by a tag ([EMAIL]), same-length stars or a short hash, plus each entity's type params: text*, types, mask |
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 character counts. Typically under 0.3 s. Price: USD 0.001. Free: 10 utilpeek calls per IP per UTC day. The submitted text params: text*, language, remove_stopwords, top_n, ngram, case_sensitive, min_length, include_numbers, characters |
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 count. Typically under 0.3 s. Price: USD 0.002. Free: 10 utilpeek calls per IP per UTC day. The submitted text is never params: text*, language, top_n, max_words |
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-sentence scores. It does not paraphrase, shorten or check facts. Typically under 0.5 s. Price: USD 0.003. Free: 10 utilpeek ca params: text*, language, sentences, ratio |
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 or less), optionally per sentence. A lexicon heuristic: it misses sarcasm and domain jargon. Typically under 1 s. Price params: text*, per_sentence |
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, acronyms, CamelCase, URLs and emails are skipped. A dictionary lookup, not grammar or context aware. Typically under 1 s ( params: text*, language, max_suggestions |
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 can smuggle prompt-injection text, controls) with positions; bidi controls (Trojan Source, CVE-2021-42574); UTS #39 con params: text*, normalize, strip_invisible, list_chars, skeleton |
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") parsed back to a number. `value` is a decimal string or an integer, never a float. Invalid or out-of-range input is a 422 (n params: mode, value, text, style, currency |
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). Input: optional `kind`, `unit`, `count`, `seed` and `start_with_lorem`; an empty body gives 3 paragraphs. Typically under params: kind, unit, count, seed, start_with_lorem |
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 (equal, insert, delete, replace) with exact character offsets into a and b. At most 1,000 hunks and 5,000 change records, th params: a*, b*, mode, context |
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, Arabic, Hebrew, Indic, Thai and emoji are not romanised: they are dropped and counted in `dropped_characters`. Nothing left params: text*, separator, max_length, lowercase |
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, forms or javascript:/data: links; every link gets rel="nofollow noopener noreferrer". HTML over 4 MB is a 422 output_too_la params: markdown*, flavor |
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 description; scripts, styles, navigation, footers, forms and embeds are dropped and <main> or <article> is preferred. Over 250,00 params: html*, format, base_url |
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 an optional time shift in milliseconds. Strict parsing: the input format is detected, a malformed cue is named in a 422. params: to*, content, segments, from_format, shift_ms |
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, line count and characters per second (Unicode grapheme clusters, not bytes or display columns). At most 1,000,000 chara params: content*, profile*, from_format, intentional_overlaps |
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`, `patterns` and `boxes`. Matching needs a text layer: OCR scans first or give boxes. Automated: check the result before params: url, pdf_base64, terms, case_insensitive, patterns, boxes, dpi, output |
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 its length with `mask`), and a verdict leaks, clean or no_text_layer. Reads the text layer (pdfplumber) under dark filled params: url, file_base64, pages, mask |
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, Stripe, Slack, Google, OpenAI and other sk- keys, Hugging Face, npm, PyPI, SendGrid, Telegram, PEM and hex keys, JWTs, params: text* |
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), `share_found` and the sources ranked by sentences held. A sample in one web index (Mojeek): not found is no proof of o params: text*, max_sentences, exclude_domains |
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