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304,910 tools. Last updated 2026-07-21 22:21

"A server for finding voice-related content" matching MCP tools:

  • Generates a voiceover from text using Hume Octave TTS. Audio uploaded to Spaces, signed URL returned (24h TTL by default). Charged in credits up-front based on script length (use quote_voiceover for a preview). Best for demo-video narration, tutorial audio, and any one-shot batch TTS. NOT a real-time conversational voice (use Hume EVI for that, different product). Voice options: pass voiceId for a specific Hume voice clone, or omit to use the deployment's default narrator (HUME_OCTAVE_VOICE_ID env var).
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  • Look an Old Norse word up on Wiktionary and return its senses plus full declension/conjugation tables — attested content (including the verbs' mediopassive voice), not invented. Any form of the word works; an inflected query is resolved to its lemma automatically via previously cached paradigms and the result notes the resolution. With search_language='eng' the query is an English word instead: the result lists its per-sense Old Norse equivalents (the translations block) plus their expanded entries. Returns Markdown plus the same result as structuredContent matching the declared outputSchema. Results are cached server-side; first-time queries reach the live upstream politely and calls are rate limited — on a rate-limit error, wait a few seconds and retry. Content is from en.wiktionary.org (CC BY-SA 4.0 — attribute and share alike if republished).
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  • Start a narada (multi-persona advisory) on the given context. Server-side pipeline picks 3-5 personas via keyword routing, then each persona produces a recommendation in their own voice. ASYNCHRONOUS — returns a job id immediately; call fetch_narada_result(id) to retrieve voices when ready. Typical latency: seconds to a minute (LLM inference). Use for decisions where multiple perspectives matter more than one specialist. Context should describe the situation, not just a topic — e.g. 'planujemy zamienić session cookies na JWS przed publicznym launchem' is better than 'JWS'.
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  • Generate TTS audio for the project's voice blocks. Without voice_block_ids it fills gaps: only blocks with no audio yet run, so re-calling it is always safe (already-generated and currently-generating blocks are skipped, never re-billed). Pass voice_block_ids to explicitly REgenerate those blocks (e.g. after changing a block's voice). Speakers must have voices bound first — set_narrator_voice / set_character_voice. Optional editable_sections/settings apply to every selected block (see get_section_template("voice_block") and list_models("voice_block")). Async — returns one job per block.
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  • Convert text to speech by cloning the voice from an audio sample you provide (voice-cloning text-to-speech). Both text and sample are required; the text is limited to 1000 characters and the sample is supplied as a URL or base64 audio that must be at most 15MB, with violations returning HTTP 400. Synchronous: the call blocks until generation finishes and returns a single audio result containing a URL; there is no separate polling step. Credits are charged on success. Use this when you have a reference voice sample to clone; use createSpeechPreset to speak with a built-in named preset voice instead, and createVoice to design a brand-new voice from a text description rather than cloning one. Pass an optional request_id to tag the result so you can locate it later via getAudioResults. Requires an API key (user scope). Credits: This endpoint consumes 1 credits per call.
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  • Answer questions using knowledge base (uploaded documents, handbooks, files). Use for QUESTIONS that need an answer synthesized from documents or messages. Returns an evidence pack with source citations, KG entities, and extracted numbers. Modes: - 'auto' (default): Smart routing — works for most questions - 'rag': Semantic search across documents & messages - 'entity': Entity-centric queries (e.g., 'Tell me about [entity]') - 'relationship': Two-entity queries (e.g., 'How is [entity A] related to [entity B]?') Examples: - 'What did we discuss about the budget?' → knowledge.query - 'Tell me about [entity]' → knowledge.query mode=entity - 'How is [A] related to [B]?' → knowledge.query mode=relationship NOT for finding/listing files, threads, or links — use search.files / search.threads / search.links for that.
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  • Attach a photo to a listing you own directly from its public URL — one call, no separate sign/upload/confirm. The server fetches the image and ingests it with auto-generated thumbnail/hero/full variants. Only https image URLs whose host is publicly routable are accepted. The photo is content-moderated (must be real-estate related and safe) before it can appear publicly — the returned snapshot includes the moderation_status (approved / rejected / escalated) and moderation_reason. A rejected or escalated photo will not be publicly visible and will block publishing until removed or replaced.
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  • Look an Old Norse word up on Wiktionary and return its senses plus full declension/conjugation tables — attested content (including the verbs' mediopassive voice), not invented. Any form of the word works; an inflected query is resolved to its lemma automatically via previously cached paradigms and the result notes the resolution. With search_language='eng' the query is an English word instead: the result lists its per-sense Old Norse equivalents (the translations block) plus their expanded entries. Returns Markdown plus the same result as structuredContent matching the declared outputSchema. Results are cached server-side; first-time queries reach the live upstream politely and calls are rate limited — on a rate-limit error, wait a few seconds and retry. Content is from en.wiktionary.org (CC BY-SA 4.0 — attribute and share alike if republished).
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  • Niche (nicheangle.com) story discovery: find stories worth writing about, then draft and publish platform-native social content (LinkedIn, X threads, Instagram, newsletter) from them. This is story discovery, not content generation: Niche reads primary sources, separates signal from noise, and clusters it into a ranked story slate with provenance, the editorial-intelligence step before any writing. Returns a session_id plus initial status; poll niche_session_state with the session_id until status is `cp1_awaiting_story` to read the slate. Brand profile: the run's voice/offer/CTA. You do NOT need niche_whoami to brand a run: OMIT `brand_id` and a single or default brand binds automatically (silently). On a MULTI-brand account, an omitted `brand_id` returns `brand_choice_required` with `brand_options[]` inline (the slate still lands). Ask the user which brand, then re-call with `brand_id` (or `brand_id:'none'` for a deliberately unbranded run); don't draft until one is chosen. Pass `brand_id` to bind a specific persisted profile (set via niche_brand_profile_set); its voice, lexicon, framing, channel config, and verifier overrides thread through every downstream stage. Pass `profile_overrides` alongside `brand_id` to deep-merge a one-time deviation (logged on the session, not stored). The effective profile is snapshotted at scan time; later updates to the persisted profile don't affect in-flight runs.
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  • Auto-populate the user's BrandKit (palette / fonts / tagline / logo / wordmark / boilerplate / voice notes) from files, a URL, or pasted text. Additive by default: fills empty fields, leaves populated ones alone. Idempotent: re-running the same inputs doesn't double-write. Overwrite rule: if the target brand kit already has an identity (a tagline/boilerplate/voice for a different brand), do not silently overwrite it. First ask the user whether to replace it. If the account supports multiple brand profiles, prefer creating a separate brand instead: pass a new `brand_id` slug plus `brand_name` rather than clobbering the existing one. Only pass `replace=true` once the user has confirmed they want this brand re-learned from the new source. Use when the agent has brand assets in scope (a working directory with logos / press-kit / brand-guide PDFs, the user's portfolio or Substack URL, pasted boilerplate copy) and wants to populate Niche's BrandKit so future signal_scan and content generation inherit the brand context. Agent-side equivalent of the Niche web app brand-kit ingest surface, same backend engine. Async, then poll: a URL or multi-file ingest runs in the background, so this call returns fast with {ingest_id, status:'ingesting'}. Then poll niche_brand_kit_ingest_status(ingest_id) until status is 'done'; that response carries the populated BrandKit, the ingest report (detected[] / skipped[] / errors[]), and a diff[] of changed fields. (Loop: ingest, then poll status until done/failed; same pattern as niche_signal_scan to niche_session_state.) Do not re-call ingest while one is running; a duplicate of the same inputs attaches to the in-flight job. URL ingest also fills voice primitives when the page has post-shaped text (Substack/blog/X). If a URL is slow or thin to scrape, the visual fields may land before the voice pass completes; when the report flags this, paste the page's About/homepage copy via `text=` to complete the brand voice.
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  • Compound endpoint — one payment turns audio in any of 13 source languages into both a transcript AND a translation in any of 119 target languages. Perfect for WhatsApp voice messages in a language you don't speak (Yoruba → English), or recording a meeting in another language and reading it in yours. Auto-detects source if omitted. Async — returns requestId, poll with check_job_status(jobType='transcribe-translate'). Flat price covers STT + translation. Cheaper than calling transcribe_audio + translate_text separately for typical voice messages. Pay with Bitcoin Lightning — no API key or signup needed. Requires create_payment with toolName='transcribe_translate'.
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  • Analyze text for writing style issues: weasel words, passive voice, duplicate words, long sentences, nominalizations, hedging, filler adverbs, and research-cited AI tells. Read-only and stateless — text is analyzed in memory on the hosted server and never stored. Returns a plain-text report with each issue's line and column, the matched text, surrounding context, and the reason for AI tells; texts over 100,000 characters return an error message. This hosted server has no filesystem access — the wsc-mcp npm package adds a check_file tool for local files. It only reports issues — to auto-remove duplicate words, follow up with fix_duplicates.
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  • Convert text to speech using a named built-in preset voice, with optional emotion and language settings. Both text and voice_preset_id are required and the text is limited to 1000 characters; invalid input returns HTTP 400. Synchronous: the call blocks until generation finishes and returns a single audio result containing a URL; there is no separate polling step. Credits are charged on success. Use this when you want a ready-made catalog voice and do not need to supply your own sample; use createSpeech to clone a voice from an audio sample instead, and createVoice to design a new voice from a text description. Pass an optional request_id to tag the result so you can locate it later via getAudioResults. Requires an API key (user scope). Credits: This endpoint consumes 1 credits per call.
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  • 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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  • Extract voice primitives (register / sentence rhythm / lexicon preferences / punctuation habits) from post-shaped text and persist onto the user's VoiceProfile. The voice primitives thread into content generation so generated copy matches the user's actual writing voice. Two input shapes: pass `posts` (list of pre-collected text snippets, ≥80 chars each) or pass `url` (the server scrapes post-shaped snippets from the page: Substack / Medium / blog / X profile). Inline posts win when both are given. Inline post-shaped snippets need to be the user's own writing, not press articles or marketing copy. Returns the extracted primitives + a diff of what changed on the stored VoiceProfile.
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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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  • Start resolving a dynamic post block with Claude — returns a CLAIM CHECK. A dynamic block's ``prompt`` is run by Claude (with web search + web fetch for live data) and woven into the surrounding post ``context`` in the author's ``voice``. The author's instruction governs length — there is no character cap (X supports long-form posts). The operator's Anthropic key stays in the vault and never leaves the server. Because that work (paginated fetches + generation) can outlast a client timeout, this returns immediately with a **claim check** instead of the text: ``{"success": true, "claim_check": "...", "status": "pending", "poll_after_seconds": N}``. Redeem it with the free companion ``fetch_dynamic_block(claim_check)`` until ``status == "done"`` (then read ``result.text``). (The scheduler resolves blocks directly server-side at fire time and does not use this tool.) Paid: the AI cost is metered as a tollbooth fare on THIS start call, refunded if no Anthropic key is configured or the job ultimately fails. Args: prompt: The dynamic block's prompt to run. context: The surrounding composed post (may contain the ⟨HERE⟩ marker). voice: Voice-profile text fed to the model (optional). bans: Banned constructions — JSON array or comma-separated (optional). allowed_domains: Author allowlist for web_fetch — JSON array or comma-separated. Blank = fetch any URL the prompt references. max_fetches: Author budget for web lookups (search + fetch), 1..25. runtime_limit_seconds: Author's time budget (clamped 60..900). Sets the job's runtime ceiling and the poll cadence (first poll ~75% of it), and is available to the operator's pricing model for ad-valorem fares. npub: Your DPYC patron npub for credit billing.
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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. Synchronous: the call blocks until generation finishes and returns 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 getAudioResults. Requires an API key (user scope). Credits: This endpoint consumes 1 credits per call.
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  • SECOND STEP in the troubleshooting workflow. Read the full content and solution of a specific Knowledge Base card. Returns the card content WITH reliability metrics and related cards so you can assess trustworthiness and explore connected issues. WHEN TO USE: - Call this ONLY after obtaining a valid `kb_id` from the `resolve_kb_id` tool. INPUT: - `kb_id`: The exact ID of the card (e.g., 'CROSS_DOCKER_001'). OUTPUT: - Returns reliability metrics followed by the full Markdown content of the card, plus related cards. - You MUST apply the solution provided in the card to resolve the user's issue. - After applying, you MUST call `save_kb_card` with `outcome` parameter to close the feedback loop.
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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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