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467,366 tools. Updated 2026-08-20 20:23

"Speech recognition technology and systems" matching MCP tools:

  • RAW text-to-speech from the voice-model catalog: speak a script in a chosen voice and return the served MP3 URL. For a standalone voiceover / narration clip — NOT for adding audio to a video (render_ad and generate_video voice their own spots; change_voice re-voices a finished clip). engine picks the voice model (default 'seed-audio'; also 'eleven-v3', 'minimax-speech', 'kokoro'); voice is a preset name from that engine (see hermoso_capabilities → voice engines) — a name that engine does not have is REFUSED for free with its real list, and a few engines generate their own voice and take no preset at all (the reply says which voice actually spoke). Paid (a couple of credits by length; ≤900 characters).
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  • Audit a technology stack for exploitable vulnerabilities. Accepts a comma-separated list of technologies (max 5) and searches for critical/ high severity CVEs with public exploits for each one, sorted by EPSS exploitation probability. Use this when a user describes their infrastructure and wants to know what to patch first. Example: technologies='nginx, postgresql, node.js' returns a risk-sorted list of exploitable CVEs grouped by technology. Rate-limit cost: each technology requires up to 2 API calls; 5 technologies counts as up to 10 calls toward your rate limit.
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  • Multi-language UGC content moderation for marketplaces, social platforms and comment systems. Detects policy violations in text content across 9 policies and 12 languages without external API calls. Policies checked: • hate — hate speech, slurs, dehumanization (50+ terms × 12 languages) • sexual — explicit sexual content, pornography references, nudity solicitation • violence — threats, weapon references, graphic violence • self_harm — suicidal ideation, self-injury, eating disorder promotion • harassment — doxxing, stalking, cyberbullying, blackmail • scam — phishing, investment fraud, romance scam, lottery fraud • spam — bots, keyword stuffing, excessive caps, emoji storms, suspicious URLs • copyright — piracy, leaked content, serial keys, streaming fraud • minor_safety — grooming signals, CSAM references, minor + adult content combos Languages: en / fr / de / es / it / pt / nl / zh / ja / ko / ar / ru (auto-detected) Output includes severity (low/medium/high/severe), confidence (0-100), matched patterns, excerpt, recommended action, age appropriateness (adult/teen/child), and signals. No API key required. Stateless — no content is stored or logged.
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  • Open the FluxInk handwriting recognition canvas. The user draws freehand strokes with a stylus, finger, or mouse. The strokes are converted by one of two model families: general recognition for handwriting, math, and chemical formulas, or structure recognition for molecular structures. Use this when the user asks to handwrite, draw, sketch, ink, scribble, or scrawl something. Use this when the user wants to draw a math equation, chemical formula, or molecular structure rather than type it. Use this when the user asks for a canvas, drawing pad, handwriting input box, or whiteboard. Use this when the user wants to convert stylus or finger drawings into recognized text or markup. Do NOT use this when the user types a question, equation, or formula in chat and just wants an answer. Do NOT use this when the user uploads or references an existing image of handwriting (call recognize_image instead). Do NOT use this when the user wants a formatted document, study sheet, or layout PDF (call create_layout instead). Do NOT use this when the user wants text rendered in a personal handwriting style (call show_style_canvas instead). Do NOT use this for conversational or informational requests that need no ink input. Do NOT re-open if a FluxInk handwriting canvas is already visible from any earlier turn. Instead instruct the user to keep drawing on the existing canvas. Only set force_new=true when the user explicitly asks for a brand new, fresh, or blank canvas. Always pass the original chat message in the prompt parameter so context is preserved after recognition. After calling, write a single short acknowledgement and do NOT describe the canvas UI.
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  • Transcribe the actual audio of a YouTube, TikTok or Instagram video with Whisper large-v3. Not caption scraping: the audio is downloaded and run through speech recognition, so it works on videos with no subtitles, in any language, and on TikTok and Instagram where no caption track exists at all. Extraction runs from a real residential IP, reaching sources that refuse datacenter ranges. Returns full text plus sentence-level timestamps. — $0.02/call, paid per request via x402 (USDC).
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  • Return the canonical list of 26 ancient divination systems Mythsensus implements (slug, English + Thai name, region, required inputs). Use first when asked "what systems do you support?".
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Matching MCP Servers

Matching MCP Connectors

  • Propose a new/updated Idea note → Inbox. Title-match to update; send the COMPLETE revised text. Set resync:true ONLY when you rewrote the note FROM the current systems (get_stale lists notes the systems have moved past) — it stops the adopted note from immediately nagging to re-generate the systems it was just written from.
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  • Search SaaS Browser technologies by name or category. Returns matching technology IDs for use with the SearchSaasTool technology_ids filter.
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  • Fetch current top news headlines from GNews (requires BYO API key). Optionally filter by category (general, world, nation, business, technology, entertainment, sports, science, health), country code, and language. Returns up to 100 articles with title, description, source, and publication date.
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  • List works for which the full text (every scene, speech, and line) is loaded — beyond just the famous-quote excerpts. Use this to discover what is available for deep structural lookup via get_scene and get_act.
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  • Compare two developmental topics, conditions, or skills side by side (e.g. 'speech delay' vs 'autism'; 'speech therapy' vs 'occupational therapy'). Returns corpus-grounded points of similarity and difference. Non-diagnostic.
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  • Transcribe audio to text with timestamps. Uses Mistral Transcription — high-accuracy speech recognition that handles accents, background noise, and overlapping speakers. 13 languages: en, zh, hi, es, ar, fr, pt, ru, de, ja, ko, it, nl. Up to 500 MB / 60 minutes per file. Async — returns requestId, poll with check_job_status(jobType='transcription'), then get_job_result. 10 sats/min. Privacy: audio and transcripts are ephemeral — processed, returned, and discarded. Never persisted. Pay per request with Bitcoin Lightning — no API key or signup needed. Requires create_payment with toolName='transcribe_audio'.
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  • Convert text to natural-sounding speech. Returns WAV audio (16-bit PCM, 24 kHz mono) synthesized with neural voices. 54 voices across 9 languages -- including 3 native Brazilian Portuguese voices (pf_dora, pm_alex, pm_santa). Usage is metered per character. Args: text: Text to convert to speech (max 5000 characters per request). language: Language code; selects the default voice when no voice is given. voice: Explicit voice ID (see list_voices). Overrides language. speed: Speech speed multiplier, 0.5-2.0. output_format: 'audio' for playable MCP audio content, 'base64_json' for a JSON object with the base64-encoded WAV and metadata. Returns: MCP audio content (audio/wav), or when output_format='base64_json' a dict with keys: - audio_base64 (str): Base64-encoded WAV bytes - mime_type (str): 'audio/wav' - voice (str): Voice used - characters (int): Characters billed - estimated_cost_usd (float): Estimated cost of this request
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  • What a word means, in thousands of languages — 8.15M dictionary entries with senses, part of speech, etymology and pronunciation. Covers what a general model is weakest at: historical languages (Old English, Gothic, Ancient Greek, Middle French) and hundreds of regional and indigenous ones. A single spelling often has entries in many languages and you get all of them — `hund` returns Danish, Gothic, Icelandic, Middle English and more — or pass `language` to narrow, `pos` for one part of speech. Use this for words and lookup() for things: define("java") gives the word in eight languages, lookup("Java") gives the island.
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  • Retrieves a comprehensive list of available Text to Speech voices used for generating audio voiceovers. You must call this tool first to obtain the required voice object before you can generate any Text to Speech audio. You can optionally filter the voices by providing a Language_code.
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  • List oruk’s speech models with lifecycle and per-task pricing (USD per audio minute), the five API tasks, the 15 emotion and 16 speaking-style labels, and audio limits. No API key required. Use this to choose a model, estimate cost before analyzing long audio, or see which labels exist.
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  • Input: Text to convert to speech, language ID, and voice type. Output: Returns the URL of the generated audio file. Functionality: This tool converts text to speech in the specified language and voice. It follows these steps: 1. Gets user_id from context and validates tokens. 2. Generates speech from the text using the specified language and voice. Args: text: The text to convert to speech language_id: The language ID (defaults to "en" for English) voice_id: The voice type (defaults to "female") Returns: The URL of the generated audio file INSTRUCTION FOR CLIENT MODEL: - Extract the required input parameter 'text' (type: string) and optional parameters 'language_id' (type: string, default 'en') and 'voice_id' (type: string, default 'female') from the user's prompt. - If 'language_id' or 'voice_id' are not specified, use the default values. - Ignore any extraneous information in the user's input. - Pass the extracted values to this tool as 'text', 'language_id', and 'voice_id'. - RECOMMENDATION: Call get_languages() first to see available language IDs before choosing a language_id. - Example: For user input "Convert 'Hello world' to French with a male voice", extract 'text' as 'Hello world', 'language_id' as 'fr', and 'voice_id' as 'male'.
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  • Browse all funding categories with opportunity counts. Categories include: Grant, Construction, Goods & Services, Professional Services, Technology, Healthcare, Research, and more. Useful for understanding what types of opportunities are available. Does not count toward your monthly searches.
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  • Use when researching which vendors dominate AI recommendations in a healthcare technology category or validating a health IT vendor selection. Returns top recommended vendors, AI consensus narrative, and sample size from healthcare-specific citation analysis. Example: EHR category — Epic leads at 67% AI citation share, Oracle Health 18%, MEDITECH 9% — consensus near-universal for large health systems, fragmenting below 200 beds. Source: Stratalize AI citation composite.
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  • Multi-language UGC content moderation for marketplaces, social platforms and comment systems. Detects policy violations in text content across 9 policies and 12 languages without external API calls. Policies checked: • hate — hate speech, slurs, dehumanization (50+ terms × 12 languages) • sexual — explicit sexual content, pornography references, nudity solicitation • violence — threats, weapon references, graphic violence • self_harm — suicidal ideation, self-injury, eating disorder promotion • harassment — doxxing, stalking, cyberbullying, blackmail • scam — phishing, investment fraud, romance scam, lottery fraud • spam — bots, keyword stuffing, excessive caps, emoji storms, suspicious URLs • copyright — piracy, leaked content, serial keys, streaming fraud • minor_safety — grooming signals, CSAM references, minor + adult content combos Languages: en / fr / de / es / it / pt / nl / zh / ja / ko / ar / ru (auto-detected) Output includes severity (low/medium/high/severe), confidence (0-100), matched patterns, excerpt, recommended action, age appropriateness (adult/teen/child), and signals. No API key required. Stateless — no content is stored or logged.
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