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195,884 tools. Last updated 2026-06-12 07:22

"Transcribing Voice Conversations into Structured Meeting Notes" matching MCP tools:

  • Create or overwrite an OpenAkashic markdown note. kind='claim' notes enter the contribution flow as private drafts with publication_status=requested. Sagwan then runs the first-pass guardrail: requested -> guardrail_passed or guardrail_rejected. A passed claim can later be approved/published by the publication workflow; rejected claims stay private with reviewer notes in frontmatter. Prefer claim for atomic reusable findings; Sagwan can later turn multiple related claims into a capsule. kind='capsule' notes stay private until you request publication review. Other kinds (playbook, concept, etc.) remain Closed-only working memory. Writable roots: personal_vault/, doc/, assets/ only. Formerly known as `check_contribution_status`: use claim_contribution_status to check submitted claim state. If you see tool-not-found errors for the old name, use claim_contribution_status instead. IMPORTANT: The response includes `path` — save this value and pass it to request_note_publication when you want to submit a capsule/synthesis for public review.
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  • 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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  • Convert books (EPUB/PDF/TXT) to full audiobooks with automatic chapter detection, multi-voice narration, and optional translation to any language before narration. 3 voice tiers: OmniVoice Global (602+ langs, 100 chars/sat), Inworld Premium (#1 ranked TTS ELO 1217, 50 chars/sat), Minimax Studio (voice cloning from reference clip, 10 chars/sat). Min 500 sats. Async — returns jobId, poll until completed (5-60+ min). Single payment, full outcome — no multi-step orchestration required. Pay with Bitcoin Lightning — no API key or signup needed. Requires create_payment with toolName='epub_to_audiobook'.
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  • Returns a token-efficient batch of conversations for bulk analysis. Default output is summaries only (id, summary, trust_score, status, created_at) plus the perspective outline; opt in to full XML transcripts via include_transcripts=true. Default format is TOON (compact); JSON available. Behavior: - Read-only. - Errors when the perspective is not found or you do not have access. - Filters: period (7d/30d/90d/all, default 30d), status, trust_score range. Page size up to 50, default 10. Pass nextCursor back as cursor for the next batch. - Response includes total_matching, returned_count, has_more, nextCursor for sizing. - Citation format when transcripts are included: "conversation_id:message_index". When to use this tool: - Thematic analysis, sentiment distribution, or pattern detection across many conversations. - Building a research summary from many summaries cheaply, then drilling into specific transcripts. - Bulk export with filters. When NOT to use this tool: - Need one conversation in full detail (voice snippets, trust dimensions) — use perspective_get_conversation. - Just need a browsable list with metadata — use perspective_list_conversations. - Aggregate counts only — use perspective_get_stats (call first to size the dataset before batching).
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  • Place an outbound AUDIO/VOICE phone call via Twilio (PSTN) or Telegram (MTProto 1:1 call). Use this any time the user asks to 'call', 'ring', 'phone', 'dial', or have a spoken conversation. Do NOT use messages.send when the user asks to call someone — a call is real-time voice, not a text message. You conduct the conversation as the voice agent using the provided greeting and instructions.
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  • List tone profiles for a strategy. Today returns at most one entry — the tone_of_voice synthesized by the Tone of Voice Synthesis agent (POWER-mode bundles only). The shape is list-stable so future multi-tone bundles plug in without changing the contract. Use this to align generation with the brand-tied voice DNA before writing copy, hooks, or scripts.
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Matching MCP Servers

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    MCP server providing managed persistent memory for AI agents. Read and write structured state across sessions, tools, and restarts at 1000+ requests per second, with no infrastructure to self-host or operate.
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    Apache 2.0

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  • Podcast directory search + best podcasts + recommendations via Listen Notes. Free key required.

  • take-the-meeting MCP — wraps StupidAPIs (requires X-API-Key)

  • Search notes by keyword or list recent notes. Returns summaries (id + description) only. Use get_note to retrieve the full content of a specific note. With query: Case-insensitive keyword search on description and content. Without query: Returns most recently updated notes.
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  • Save a new note with learned knowledge or procedures. Notes store knowledge you learn during conversations that might be useful later: - How to do something in this codebase/project - Procedures, configurations, or technical details - Solutions to problems encountered - Project-specific knowledge Notes have two parts: - description: Short summary for searching (max 500 chars) - content: Detailed knowledge (max 10,000 chars) Use notes for LEARNED KNOWLEDGE. Use facts for TRUTHS ABOUT THE USER. Examples: - description: "How to deploy this Next.js project to Vercel" content: "1. Run 'vercel' command... 2. Configure environment variables..." - description: "Database migration process for this project" content: "Migrations are in supabase/migrations/. To apply: npx supabase db push..." SELF-LEARNING (scope="ai_client"): Your persistent memory across conversations. Save a note whenever you learn something worth remembering — don't wait, save as you go. Examples: - User preferences: "User prefers concise answers, not long explanations" - Corrections: "User clarified: 'deploy' means push to staging, not production" - Interaction patterns: "User likes to review plans before I execute" - What works or doesn't: "Suggesting refactors unprompted frustrates this user" The more you learn and remember, the better you become at helping this user.
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  • Find or list chat threads/conversations — by topic, participant, unread/unanswered status, or recency. Omit `query` to list threads by filter. For message content use search.messages; for files use search.files. `since` filters by recency and pairs with only_unread / only_unanswered.
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  • Return the full dossier projection for a meeting reading, in the requested cognitive lens. Same lens enum and default as describe_place / describe_corridor — eight total projections (seven stakeholder lenses — developer, investor, broker, attorney, business, resident, civic-leader — plus synthesis as the default). Returns the lens-projected body, full frontmatter (jurisdiction, board, meeting_date, document_type, key_signals, vote tallies), citation-stable claims[] (per the Phase 11 Citable Contract; populates as meeting claim scopes graduate), four-clock freshness, and the structured record_status block (record_type / meeting_status / outcome_status / minutes_available / vote_final) — the last prevents agents from summarizing agenda intent as completed action. Use to ground citations in a specific meeting's reading; pair with list_meetings or meeting_index for discovery.
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  • Get the full results of a completed Sieve analysis. Returns the Sieve Score (0-140), meeting decision (Take Meeting/Pass/ Need More Info), executive summary, key strengths, and key concerns. Args: deal_id: The deal ID returned by sieve_screen. sections: Comma-separated filter (e.g. 'summary,strengths,concerns'). Options: summary, profiles, findings, questions, strengths, concerns. Empty returns everything. Score and decision are always included.
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  • Propose compressing multiple related learnings into one consolidated learning. Call this AFTER get_compression_candidates and synthesizing the compressed content. Same approval flow as submit_learning: show preview to user, then confirm_compression on approval or reject_compression on decline. Write a synthesised structured learning: • problem — best single problem statement across the cluster • cause — common root cause if one exists (optional) • solution — consolidated fix • notes — model-specific nuances (e.g. grok adds X, claude adds Y)
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  • Clone any voice from a single audio sample. Returns a reusable voice_id for text_to_speech — speak in the cloned voice indefinitely. High-fidelity reproduction capturing tone, cadence, and accent. Turbo (faster) or HD (higher quality) modes. 7,500 sats per clone. Pay per request with Bitcoin Lightning — no API key or signup needed. Requires create_payment with toolName='clone_voice'.
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  • Submit feedback about Hjarni itself — confusing tool descriptions, missing capabilities, unexpected errors, friction, or praise. Use this when something about the MCP server, a tool, or the product behavior is worth flagging to the maintainers. Do NOT use this for the user's own notes or knowledge — those belong in notes-create. Required: category ('bug'|'confusing'|'missing_feature'|'friction'|'praise'|'other'), message (string, what's wrong and ideally what you'd expect instead). Optional: severity ('low'|'medium'|'high', default 'medium'), tool_name (the MCP tool the feedback is about, e.g. 'notes-update'), context (JSON-encoded string with any extra structured data — error excerpts, the arguments you tried, the workflow that broke).
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  • Search commercial real estate listings. Returns paginated hits with facet counts. For AI-driven search, call interpret_search first to convert a natural-language query into structured filters, then pass those filters here.
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  • Use this BEFORE any creation task ("help me write X", "I'm working on Y"). Runs two parallel searches and returns them separately: a SKILLS bucket (skill/voice/template, the craft layer) and a KNOWLEDGE bucket (knowledge/principle/brand/idea/resource, the material). Bring both into context before producing output. If the skills bucket is empty and `output_type` is set, this also increments a skill-gap counter; when count reaches 3 the response includes `skill_gap.skill_gap_threshold_reached: true` so you can prompt the user to codify a skill.
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  • Place an outbound AUDIO/VOICE phone call via Twilio (PSTN) or Telegram (MTProto 1:1 call). Use this any time the user asks to 'call', 'ring', 'phone', 'dial', or have a spoken conversation. Do NOT use messages.send when the user asks to call someone — a call is real-time voice, not a text message. You conduct the conversation as the voice agent using the provided greeting and instructions.
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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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  • Use this tool whenever the user shares an audio file and wants it transcribed to text. Triggers: 'transcribe this recording', 'convert this audio to text', 'what was said in this meeting', 'transcribe this voice note', 'turn this podcast into text'. Accepts base64-encoded audio (mp3, wav, m4a, ogg, flac, webm, mp4, etc.), max 25MB. Returns the full transcript, word count, and character count. Powered by OpenAI Whisper. Free 200 calls/day — no OpenAI API key required; Toolora absorbs the cost.
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  • Get the structured transcript and final state of a voice call by call_id. Returns per-turn rows in chronological order, call status (active/completed/failed/abandoned), duration, and an `outcome` field telling whether the recipient picked up (answered/no_answer/busy/declined/failed/unknown). `answered_at` is non-null once the recipient picked up. Returns active turns if the call is still in progress.
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