"Automated Chat Service Based on Personalized Text Messaging Style" matching MCP tools:

• send_message

  Fast MCP Telegram

  Send text and optional file attachments to a Telegram chat. Supports reply-to (including forum topics and channel discussion groups), auto-detected or explicit parse_mode (markdown/html), and file attachments as http(s) URLs, local paths, or data: URIs. When files are provided, the message text becomes a caption. For channel posts with reply_to_id, automatically posts in the linked discussion group. Success: dict with message_id, date, chat, text, status='sent', and sender info. Error: dict with ok=false and error string. Use send_message to create new messages; use edit_message to modify existing ones. Use send_message_to_phone when targeting a phone number instead of a chat_id. Full documentation: https://github.com/leshchenko1979/fast-mcp-telegram/blob/main/docs/Tools-Reference.md

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• list_style_presets

  framesail

  The curated preset catalog for the no-AI style creation path, grouped by axis (art_style / narrative_style / director_style). Show the user the labels + descriptions and let THEM pick one per axis — don't choose silently. Art presets include preview image URLs (view_image works on them). Create with create_style(presets={axis: id, ...}) — instant, no analysis job. Full field text lands on the style row (get_style shows it after creation).

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• send_message

  Dock

  Send a direct message to another agent or human in the messaging substrate. Wires through cue.dock.svc, the same path the /live UI uses, so the recipient sees this message in their drawer (and, once they have a Dock-connected agent worker running, their agent harness's inbox). Address format is `<agent_slug>@<user_slug>`: `flint@socrates` targets the `flint` agent owned by user `socrates`; `self@<user_slug>` targets a human's synthetic self-agent (use this to message a human directly when you don't know which of their agents to ping). Use this when an agent legitimately needs to ask a teammate (human or agent) for help, hand off work, or follow up async; don't use it as a chat-ops side-channel for things that belong in workspace events. Sender identity follows the caller: agent callers send AS themselves, user callers send AS their self-agent (`self@<their_slug>`). Body cap is 32,000 chars. Returns `{ messageId, threadId, to }` on success. The recipient is resolved against the substrate's identity space, NOT against your accessible workspace set, this is messaging, not workspace write access. Pre-cue.dock.svc-deploy environments return `cue_not_configured` (caller treats as 'messaging not deployed yet').

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• edit_message

  Fast MCP Telegram

  Replace the text of an existing message in a Telegram chat. Only works on messages sent by the authenticated account. Cannot edit media or other message attributes — text only. Success: dict with message_id, date, chat, text, status='edited', and edit_date. Error: dict with ok=false and error string (e.g. message not found or not editable). Use edit_message to update a previously sent message; use send_message to create new ones. Full documentation: https://github.com/leshchenko1979/fast-mcp-telegram/blob/main/docs/Tools-Reference.md

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• project_memory

  Zephex

  Persists short notes about this project across chat sessions — facts you learned that are not in source code. Not a repo scanner (use get_project_context for stack). remember: saves title (max 80 chars), content (max 500 chars), type, optional area/tags — rejects API keys, tokens, and instruction-poisoning text. recall: keyword search over title/content/area/tags; returns up to 5 matches with content, type, area, age_days (~500 token cap). list: recent titles. forget: delete by uuid. Max 200 memories per project. Types: decision (why we chose X), gotcha (surprise bug), goal (current objective), preference (user style), area_fact (subsystem fact), convention (naming/rules). scope: project (default), personal (cross-project notes), all (search every project with warnings). Call when: user says remember/recall/last time; before auth/billing/deploy where past choices matter; after a non-obvious fix worth saving; new session on same repo. Do not call when: stack/scripts (get_project_context), finding code (find_code), fact already in this chat. After recall: apply matches directly — do not re-scan the repo. Use the same path on remember, recall, and list (stdio: optional, uses cwd). Stdio stores in ~/.zephex SQLite; hosted stores in cloud per user.

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• check_text

  Writing Style Checker

  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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• search_products

  Apostle — AI Men's Skincare Commerce

  Browse and search the product catalog. Use when the user wants to see what's available, look up specific products, browse by category, compare options, or asks 'show me' / 'what do you have.' Do not use when the user needs personalized recommendations based on skin concerns — use skincare_recommend instead. Returns all matching products with prices, images, and checkout. Unlike skincare_recommend, this does not score or filter — it shows everything that matches so the user can decide.

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• fluxink-mcp-chatgpt-show-style-canvas

  fci-mcp

  Open the FluxInk natural texture ink style capture canvas. The user draws a few sample characters in a personal handwriting style. The widget then renders the requested text as a PNG spritesheet IN THAT EXACT PERSONAL HANDWRITING STYLE. Output is a downloadable spritesheet of individual handwritten glyphs that can be used as an asset (for example with image generation). Use this when the user asks to render or generate text in a personal handwriting style. Use this when the user wants a personalized handwritten note, card, letter, invitation, journal entry, or signature line that should look hand written by the user. Use this when the user wants to capture, clone, or sample a personal handwriting style and reuse it. Do NOT use this when the user just wants handwriting recognized (call show_handwriting_canvas instead). Do NOT use this for generic decorative handwritten fonts, calligraphy art, or AI generated script unrelated to the personal writing of the user. Do NOT use this to read text from an existing photo of handwriting (call recognize_image instead). Do NOT use this when the user wants a formatted document or layout (call create_layout instead). Do NOT use this for plain informational requests. Supports English and Chinese. Always pass the COMPLETE target text in the text parameter. Never truncate or abbreviate. Every character must appear in the spritesheet. Do NOT re-open if a FluxInk natural texture ink canvas is already visible from any earlier turn. Instead instruct the user to update the target text inside the existing widget. Only set force_new=true on an explicit user request for a brand new style canvas. After calling, write a single short acknowledgement and do NOT describe the UI. Once the user saves the spritesheet they can re-upload it for further design work.

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• get_briefed

  VARRD — Statistically Validated Trading Edges + AI Research Engine

  Get a personalized market news briefing based on your validated edge library. Profiles your strategies, searches today's news for the instruments and setups you actually trade, and writes a concise digest connecting each headline to your specific book. Each news item includes a ↳ line tying it to your actual positions and edges (e.g. 'your ES momentum setups', 'your GC mean-reversion edge'). Requires at least 5 strong edges in your library. Costs credits.

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• search_savvly_content

  savvly

  Search the Savvly Q&A Content Library — audience-tagged questions and answers compiled from Savvly's marketing collateral plus the factual FAQ, organized by stakeholder (employee, advisor, broker, employer, universal, general) and section (kebab-case slugs, e.g. 'tax-legacy', 'retention-talent-strategy', 'implementation'). Use this when the user asks about Savvly's positioning, value props, audience-specific talking points, or Q&A-style messaging. Each entry carries the verbatim answer plus any disclaimer footnotes attached to it in the source. These facts come from Savvly's own current records; the response includes primary sources (e.g. SEC filings) for reference.

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• recommend

  Seemor Restaurant Intelligence

  Get personalized restaurant recommendations based on a natural language query. Uses cuisine, occasion, ambiance, price, and dimensional analysis to find the best matches. Returns ranked results with relevance levels and match reasons in 3-8 seconds. Include a location in your query or provide the location parameter.

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• create_style

  framesail

  Create a style. Two mutually exclusive paths: References (best): inputs=[{"input_type": "youtube" | "text", "value": "<url or description>"}] — YouTube videos are watched and text directions read; async analysis writes the style's art/narrative/director fields: await_jobs(style_id=...) before using the style. (Image/video FILE references require the multipart REST endpoint POST /styles.) Presets (instant, no analysis): presets={"art_style": id, "narrative_style": id, "director_style": id} — all three axes, ids from list_style_presets.

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• set_managed_backups

  mcp

  Turns nightly AUTOMATED (scheduled) backups ON or OFF for a MANAGED data service — the toggle that create_backup (a one-off volume snapshot) is NOT. Once enabled, redu's nightly job backs the service up on its own and prunes to the retention window; see them with list_backups and recover with the service's restore. Works for managed Postgres/MySQL/MariaDB/Redis/Qdrant/ClickHouse and can be flipped ANY time after provisioning, not only at create. Requires a card (automated backups are a paid feature; no-card trials cannot enable them). Pass the service type + its numeric id, enabled, and optional retention (days).

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• get_about

  Follow On Tours

  Get information about Follow On Tours — who we are, how we work, our experience, and how the bespoke cricket travel service operates. Use this when someone asks who Follow On Tours is or how the service works.

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• edit_message

  fast-mcp-telegram

  Replace the text of an existing message in a Telegram chat. Only works on messages sent by the authenticated account. Cannot edit media or other message attributes — text only. Success: dict with message_id, date, chat, text, status='edited', and edit_date. Error: dict with ok=false and error string (e.g. message not found or not editable). Use edit_message to update a previously sent message; use send_message to create new ones. Full documentation: https://github.com/leshchenko1979/fast-mcp-telegram/blob/main/docs/Tools-Reference.md

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• zoom_list_recordings

  local-mcp

  Lists Zoom meeting recordings saved locally on this Mac (~/Documents/Zoom), newest first: meeting name, date, and which artifacts exist (transcript, captions, saved chat, audio, video). Local recordings only — no Zoom API, no admin approval. Use zoom_read_transcript to read the text of a meeting.

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• fancy_text

  mcp

  Use this when you need to style ASCII letters and digits as Unicode glyphs (bold-serif, italic-serif, bold-italic-serif, bold-sans, script, fraktur, double-struck, monospace, circled, squared, parenthesized, small-caps) for places that lack font control such as social bios or usernames. Pass a `style` and `text` to get the transformed string; characters outside A-Z, a-z, and 0-9 (spaces, punctuation, emoji) pass through unchanged. Omit `style` to receive the list of valid style keys instead of transforming. Deterministic: same input, same output. Example: {style: "bold-serif", text: "Hello 123"} -> result "𝐇𝐞𝐥𝐥𝐨 𝟏𝟐𝟑".

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• get_cost_estimate

  Sats4AI - Bitcoin-Powered AI Tools

  Get an exact sat cost quote for a service BEFORE creating a payment. Useful for budget-aware agents to price-check before committing. No payment required, no side effects. Pass service=text-to-speech&chars=1500, service=translate&chars=800, service=transcribe-audio&minutes=5, etc. Returns { amount_sats, breakdown, currency }. Omit params to see the full catalog of supported services.

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• create_permissionless_market

  TrenchFu

  Create a structured prediction market comparing two competitors on a specific metric (volume, trades, price, unique_traders). 0.5 SOL seed, creator fee tiers (NEW 25%, PROVEN 35%, ELITE 50%), automated data resolution. For freeform claim-based markets use create_tmb_battle instead (0.1 SOL seed, any statement, tribunal-resolved).

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• jobs_recommendations

  FoundRole — AI Job Search & Application Tracker MCP for Claude & ChatGPT

  Returns the authenticated user's personalized job recommendations built from their resume, skills, target roles, and preferred location. Results are ranked by fit, may include related roles, and carry the same salary, match, H-1B, and job-trust insight payload used by job search. A processing status means the personalized feed is still being prepared; a later call returns the completed feed. Page numbers fetch additional recommendations from the same feed.

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