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543,184 tools. Updated 2026-09-10 09:31

"Information about voice or speech-related topics" matching MCP tools:

  • Transcribe English audio AND score how it was said in one call: transcript, tagged transcript, selected scores from 15 emotion and 16 speaking-style labels, and time-local segments. Use this when the user cares about both the words and the delivery — meetings, support calls, interviews, voice notes. Accepts wav/flac/mp3/m4a/ogg/webm. Up to 30 MB via audio_url or 8 MiB decoded via audio_base64; up to 60 minutes of English speech. Returns compact summaries by default. For words only use oruk_transcribe_audio; for tone only use oruk_analyze_tone.
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  • PROJECT-SCOPED: this call acts only on the explicit project_id and returns the project identity with its result. Rebalance the ORIGINAL footage's music vs its speech — the answer to 'remove the background music but keep the talking', 'the song is too loud under his voice', 'keep only the music'. The soundtrack is separated into speech/vocals + everything-else (once per video, then cached) and each side plays at its own gain: music_gain_db=-60 mutes the music, voice_gain_db=-60 mutes the speech, -12 ducks, 0 leaves untouched (range -60..+6). This changes ONLY the original footage's own audio — added music tracks are add_music/remove_music, NOT this. Not surgical on dense mixes — check the preview before promising total silence.
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  • Get Lenny Zeltser's expert CTI writing guidelines. Topics include tone, words, structure, executive_summary, voice, articles, summary, brief (one-page brief section guidance), handoffs (cross-server routing), methodology (the three subsections), fields (per-field guidance), and CTI-specific topics: attribution (full Six Signals prose), confidence (ICD-203 ladder), pyramid_of_pain, six_signals (signals table only), and anti_patterns. The general writing topics (tone/words/structure/executive_summary) now defer to `get_security_writing_guidelines` for the canonical Five Elements rules; CTI-specific content lives in the other topics. Pair the 'fields' topic with field_id for single-field guidance. This server never requests your campaign or threat-intel notes and instructs your AI to keep them local—templates and guidelines flow to your AI for local analysis.
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  • Text-to-speech with 3 tiers: OmniVoice Global (602+ languages including Yoruba, Bengali, Cebuano, Twi, zero-shot voice cloning, ~108 chars/sat — use 'language' parameter with ISO code), Inworld Premium (#1 ranked TTS ELO 1217, emotion control, 40+ languages, ~16 chars/sat), Minimax Studio (voice cloning from reference clip, 40+ languages, ~5 chars/sat). Adjustable speed (0.5-2.0x). Returns audio URL. Pay with Bitcoin Lightning — no API key or signup needed. When NOT to use: not for phone calls (use place_call for one-shot broadcasts, ai_call for AI voice agents, or open_voice_bridge to drive the call with your own LLM). For rare/underserved languages (Yoruba, Twi, Marathi, Cebuano, etc.), pick OmniVoice Global via language= — Inworld/Minimax don't cover these. Requires create_payment with toolName='text_to_speech'.
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  • 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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  • List the shows most related to a podcast, best first — "shows like this show". Each result carries the related show's slug, a calibrated score in (0,1], and a coarse band (strong: same beat and audience; moderate: overlapping subject or audience; weak: a loose connection) to branch on. Add `include: ["basis"]` to see WHY each pair is related: content similarity of recent episodes, shared topics, shared guests (named), same publisher, shared sponsors — use it to explain a recommendation or to keep only pairs related for the reason you care about (shared guests for booking, content for media planning). Related sets are precomputed per show from its transcripts, topic profile, guest roster, network and advertisers, restricted to the show's language. Only shows above a relatedness floor are listed, machine-generated and farmed feeds are never listed, and a publisher's duplicate feeds of one show appear once. An empty FIRST page is not an error: its `coverage` says whether the set is not computed yet, nothing cleared the floor, or the request's filters and the default policy removed everything; an empty page reached through a cursor is simply the end of the list. Not a topic browser: for shows that COVER a topic use `particle_podcast_resolve` with `topic_slug`. Not a guest lookup: for where a person has appeared use `particle_podcast_get_guest`. Not advertiser co-occurrence: use `particle_podcast_get_sponsors`. Every related show's slug feeds `particle_podcast_resolve`, `particle_podcast_list_episodes` and the other podcast tools; person slugs in the basis feed `particle_podcast_get_guest`, topic slugs feed `particle_podcast_resolve`'s `topic_slug`. For the five most related shows inline on a resolve, pass `include: ["related"]` to `particle_podcast_resolve` instead of calling this tool.
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Matching MCP Servers

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    An MCP server that provides information about Utkarsh, including bio, skills, work experience, and portfolio projects, accessible via local stdio or remote HTTP with OAuth.
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    A Goose MCP extension providing voice interaction with modern audio visualization, allowing users to communicate with Goose through speech rather than text.
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    MIT

Matching MCP Connectors

  • Hosted speech-to-text + speech emotion/tone analysis for agents. No install; trial keys built in.

  • USPS state code OR.

  • Your saved voices — one tool for the whole voice library. Users speak plain language and never know ids: resolve every voice by NAME yourself (call action "list" first if unsure) and never ask the user for an id. action="list" returns every saved voice with voice_id, name, kind and ready — kind "reference" is an instant voice match saved from a clip and kind "clone" is a trained voice (both speak through generate_audio: pass the NAME as its voice param); kind "avatar" voices drive talking_avatar_video. action="create" saves a NEW reference voice from a clip: voice_name plus audio_url (e.g. the url upload_media returned) or audio_base64 (+ format) — free, ready instantly. action="rename" renames a saved voice (voice_id takes the id OR the current name, new_name is the new name). action="clone" registers a voice for talking_avatar_video from audio_sample_url + voice_name (charged 2 credits). action="delete" removes a voice by voice_id or name.
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  • Set or update the persisted brand profile for a brand. The profile is a structured JSON document applied across every pipeline stage: voice rules, banned terms, canonical vocabulary, framing allowlist, channel config, compliance disclosures, and verifier overrides. Use it to persist a profile derived from a repo or docs so future runs inherit the rules, or to update voice rules and banned terms before the next run. Required sections: `identity` and `voice` (a profile with no voice falls back to generic drafts). A re-set that omits voice is accepted with a default voice stub rather than rejected. Validation: tiered lints (error / warn / info). 'error' lints reject the set; 'warn' lints accept with a note. The response includes `lints[]`. `conflicts[]` lists fields locked by the brand kit, which takes precedence; those profile values are not stored. brand_id is unique per user; re-setting the same brand_id replaces the prior profile.
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  • Design a new voice from a character description (such as "deep-voiced warrior" or "cheerful young girl") and have it speak a short line of text, returning a sample of that newly created voice. Both voice_description and text are required, the spoken text is limited to 200 characters or the call returns HTTP 400, and type selects "human" or "non-human" voices. The job result is a single audio result containing a URL; there is no separate polling step. Credits are charged on success. Use this to invent and audition a voice from a description; use createSpeech for text-to-speech that clones a specific voice from an audio sample, and createSpeechPreset for text-to-speech using a named preset voice. Pass an optional request_id to tag the result so you can locate it later via `GET /assets/audio/results`. Requires an API key (user scope). Returns 202 with a job id immediately; poll `getApiJob` (pass `wait: 30`) until status is succeeded, then read its `result` field, which is exactly the response documented for this operation. Each account may have up to 50 generations queued or running at once; beyond that submissions return 429 (PENDING_JOBS_LIMIT) - wait for jobs to finish. Credits: This endpoint consumes 1 credits per call.
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  • Draft one or more posts into a ProductClank content space. FREE. Call get_content_workspace FIRST and write in that brand's voice, post types and topics — the drafts are scored against exactly that voice by the reviewer a moment after they land, and the scores show in get_content_queue. `platform` must be one of the space's platforms (omit it to use the first). Candidates land in the user's 'All Content' queue for approval; nothing is auto-published. Up to 25 per call. This drafts into the user's OWN content pipeline — it is NOT a community content campaign (use create_content_campaign for that).
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  • Deterministically resolve a (possibly misheard) name — optionally with a company hint — to ONE person in the network. Returns 'matched' (with confidence), 'ambiguous' (ranked alternatives to ask the user about), or 'none'. PREFER THIS over hand-rolled search_people ladders whenever the task is "who is X?" from a voice note, capture, or mention — same input always gives the same answer.
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  • Generate natural speech audio from English text. Produces high-quality speech with 12 English voices. Returns base64-encoded WAV audio (16-bit PCM, 24kHz mono) along with metadata. Available voices: - af_heart (default), af_bella, af_nicole, af_sarah, af_sky (American female) - am_adam, am_michael (American male) - bf_emma, bf_isabella (British female) - bm_george, bm_lewis, bm_daniel (British male) Args: text: English text to synthesize (1-5000 characters). voice: Voice ID. See list above. Defaults to 'af_heart'. speed: Speed multiplier from 0.5 to 2.0 (default: 1.0). Returns: dict with keys: - audio_base64 (str): Base64-encoded WAV audio (16-bit PCM, 24kHz) - duration_ms (str): Audio duration in milliseconds - voice (str): Voice ID used - text_length (str): Input text character count - processing_ms (str): Synthesis time in milliseconds
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  • Generate spoken audio from text: narration, a voiceover, a read-aloud script, or a multi-voice dialogue. Pass text (up to 2048 chars) — the words to be spoken. To speak in one of YOUR saved voices, pass voice with the voice NAME (or id): users speak plain language and never know ids, so resolve the name yourself (the voice tool, action "list", shows every saved voice) and never ask the user for an id. Reference voices, trained clones and preset voices are all routed correctly by kind. To match a voice instantly from a clip instead, pass reference_audio_url (a short clip) or up to 3 reference_audio_urls and address them as @Audio1, @Audio2, @Audio3 in the text for dialogue. Alternatively pass image_url to voice a scene from a picture (cannot combine with reference audio). Optional speech_rate (-50..100), pitch (-12..12), loudness (-50..100). Returns a playable audio_url, duration_seconds, and generation_id (also saved to your library).
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  • Returns Fluentive's security, privacy, and compliance information. Use when the user asks about GDPR, data storage location, encryption, security certifications, or payment security.
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  • Score how speech sounds without transcribing it: selected emotion (happy, frustrated, worried, …) and speaking-style (sarcastic, confident, hesitant, warm, …) scores per acoustic segment. Runs the Resonance encoder and affect head only — the transcription decoder is never invoked, so nothing is transcribed and it consumes the same subscription audio minutes as unified analysis. Use this when the user asks about mood, delivery, sentiment, sarcasm, or emotional dynamics in audio. Up to 30 MB via audio_url or 8 MiB decoded via audio_base64; up to 60 minutes of English speech. Labels use model-specific thresholds; the highest-scoring emotion is returned if none passes, and styles can be empty. Outputs describe delivery, not probabilities of inner state. Need the words too? Use oruk_analyze_speech.
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  • Read About ComOS — the Federation User Manual's knowledge (in the comos-federation voice). Read-only — returns composed knowledge, performs no transaction. Returns: The composed about-us knowledge as markdown. Zero-arg; identity-free. Example: call about_us_about with arguments {}.
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  • Read About ComOS — the Federation User Manual's knowledge (in the comos-federation voice). Read-only — returns composed knowledge, performs no transaction. Returns: The composed about-us knowledge as markdown. Zero-arg; identity-free. Example: call about_us_about with arguments {}.
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  • Published Truss information by topic. Localized topics (overview, about, services, engagement, fit, faq) use locale, default en; pass he for Hebrew. Language-independent topics (identity, certifications, testimonials, clients, contact) ignore locale for content selection. Prefer get_truss_overview or topic overview for broad business understanding; prefer list_truss_services for the complete service catalog.
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  • Speech and sound: text-to-speech (default; optional voice_id, see list_resources), sound effects (model 'sfx', optional duration), multi-voice dialogue (model 'dialogue': pass the turns in the dialogue parameter, not prompt), transcription (model 'stt': pass audio_url, get text back; optional diarize/language_code), voice change (model 'voice-changer': audio_url + target voice_id) and audio cleanup (model 'voice-isolation': audio_url). Returns the audio URL (or the transcript for 'stt'). For music, use generate_music.
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  • Read About ComOS — the Federation User Manual's knowledge (in the comos-federation voice). Read-only — returns composed knowledge, performs no transaction. Returns: The composed about-us knowledge as markdown. Zero-arg; identity-free. Example: call about_us_about with arguments {}.
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  • Checks the HATVP public lobbying register by French company identifier and extracts declared topics. Use when: Find the public lobbying profile declared for a French organization. You need the public HATVP lobbying-register profile associated with a French organization. You want declared lobbying topics, domains or clients when exposed by that public source. Avoid when: You need ordinary company identity, directors or ownership information; use company/fr/profile or company/fr/directors. You need lobbying information not covered by the French HATVP public register. Limitations: Coverage is limited to declarations exposed by the HATVP public lobbying dataset. A missing record does not prove that an organization has never undertaken any influence-related activity. Price: 0.010 USD per call via x402. Paid-call behavior: source records are not modified, but a successful invocation can consume the advertised x402 payment; the payment-required challenge is free, and repeating the tool after a completed paid invocation may require a new payment. Accepted x402 networks: eip155:8453, xrpl:0.
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