"A server for interacting with a large language model chat" matching MCP tools:

• open_sutta_viewer

  Tripitaka MCP (hosted)

  Open an interactive sutta viewer inside the chat — Pāli + English, plus an optional third row in the user's own language translated BY YOU. Renders each segment as: Pāli on top (canonical), the Bhikkhu Sujato English below it (verification anchor), and — when you supply `translations` — your translation in the user's language, clearly badged as AI-generated. Prefer this over dumping raw segments when the user wants to *read* a sutta. - `sutta_id` — standard SuttaCentral id, e.g. `sn56.11`, `mn10`, `dn22`. - `around` — a segment_id (e.g. `dn22:18.1`, from a search hit) to centre on; that segment is highlighted and scrolled into view. Use this after a search so the reader lands on the exact cited line. - `offset` — 0-based segment index for paging long suttas (use `next_offset` from the previous result). Do NOT combine with `around`. - `window` — segments before/after `around` to include (default 12). 🌐 **Translating for the user (important):** when the conversation language is neither English nor Pāli, you SHOULD translate the displayed segments and pass them via `translations` so the user reads in their own language while still seeing the originals: 1. Fetch the segments first (`get_sutta` with the same selector) so you have the exact Pāli + English text. (Already called this tool without translations? The result contains the segments — translate them and call this tool AGAIN with the same selector plus `translations` to upgrade the view.) Your translation must travel through the `translations` parameter to appear in the viewer — writing it as a normal chat message leaves the viewer bilingual and looks broken; the tool always accepts `translations`, so never report it as missing. 2. Translate **from the Pāli as the source, using the English as a semantic guide** — never relay-translate from English alone. Preserve untranslatable doctrinal terms (dukkha, jhāna, taṇhā…) as loanwords with a brief gloss instead of forcing equivalents. 3. Call this tool with `translations=[{segment_id, text}, ...]` covering ONLY the segments being displayed (never a whole long sutta), `translation_language` (BCP-47, e.g. "th", "es"), and `translation_disclaimer` — one short line IN THE USER'S LANGUAGE saying the translation is AI-generated in this conversation and should be checked against the Pāli/English above. Translations are conversation-ephemeral: nothing is stored server-side; the canon stays Pāli + English only. Translations whose segment_id is not in the displayed window are dropped (reported in `translations_dropped`). Without `around`, shows the sutta from the top (capped for long suttas).

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• director_note

  framesail

  Edit ONE scene with a natural-language note (the same director chat the editor UI uses): move/restyle/add/remove layers and overlays, retime, etc. Synchronous — returns the applied mutations + updated scene. Address the scene by project_id + segment_number (preferred — always resolves to the current scene) or by scene_id from a fresh list_scenes call. Scene durations are locked to the narration track: don't ask to extend/shorten a scene except the final one — for a held-shot feel mid-video, ask for calmer/static motion on the scene instead. For notes spanning the whole video use project_director_note instead.

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• ai

  Fast.io

  Ripley — the MCP delegation surface over Fastio's RAG agent. Ripley is read-only for storage CONTENT: it answers natural-language questions about workspace/share files & folders (with citations) and never creates/edits/deletes your files — for content writes, call the primitive MCP tools directly. It DOES create/manage chat threads (chat-create/chat-update/chat-delete/message-send) and can generate shares (share-generate). Prefer Ripley over issuing many primitive reads: ask one NL question and let the server-side agent search + synthesize. Quick start: action='ask' (question + profile) → returns {answer_text, citations, chat_id, message_id, web_url}; action='status' for an engineered workspace-status summary. Lower-level chat/message actions remain for multi-turn control. Call action='describe' for the full action/param reference. Destructive: chat-delete. Side effects: ask/status/chat-create/message-send consume credits; chat-cancel terminates an in-progress message (partial tokens billed; idempotent). Verbosity (detail param): chat-list/message-list default to terse (compact rows). chat-details/message-details default to full (drill-down). Pass an explicit detail='standard'|'full' to override (best-effort: chat/message/activity endpoints may not yet honor detail server-side).

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• get_latest_render

  kernelcad

  Render a project's current model server-side and return it as an inline image so you can SEE what you built. Use this after open_in_studio (or any /p/<slug> link): call with that `slug` to inspect whether the build looks right. CRITICAL — the image is rendered from the MODEL on the server; it does NOT reflect the user's Studio camera, zoom, or screen. NEVER ask the user to rotate, zoom, pan, move the camera, close a slider, or change their view to help you see — you cannot affect their screen and it cannot affect this render. To see a different angle, call this tool again with a different `view`. By DEFAULT (omit `view`, or `view:"all"`) it returns a CONTACT SHEET of all six canonical views in one labeled image — a 3×2 grid, top row [iso, front, right], bottom row [back, left, top] — so you can judge the model from every side regardless of its orientation (e.g. to find which side has the doors). Pass a single `view` (iso/front/back/left/right/top) for one large render of that angle. DETERMINISTIC: the same model + view always returns the same bytes — identical bytes are NOT a stale/lagging snapshot. If you changed the model, push it with open_in_studio FIRST, then re-render to see the change. The image is always current and never a blank capture. Colors and shading match Studio (same palette / base-material color). The slug is the capability: no OAuth for public/unlisted; private projects require the owner signed in. The PNG is base64-inlined as a real image block by default; pass `paths_only: true` for metadata only. No renderable geometry or a mesh failure → { ok: false, error, hint }, never a blank image.

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• get_bind_link

  Quotor — Home & Auto Insurance Quotes

  Generate a single-use secure link, valid for 7 days, that the customer can follow to a Libertas bind-request page. The chat widget will usually render an inline 'Request Bind' card directly — prefer request_bind_inline for the in-chat flow. Use get_bind_link when the customer wants to leave the chat, finish on a different device, or have their final numbers emailed to them (pair it with the email offer during a long finishing wait). The bind link reveals the real carrier name (the only place it's revealed).

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• scalix_db_text_to_sql

  Scalix Cloud

  Translate a plain-language question into a candidate SQL query using pattern-matching against the live schema (no AI model — simple questions only: counts, averages, filtered selects on a named table). Returns the SQL without executing it, with a confidence score; low confidence means the table was guessed. Review the statement and tables_used, then run it with scalix_db_query. For complex questions, read scalix_db_schema and write the SQL directly.

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• remember

  XMemo

  Save durable information for future recall; skip transient chat. Existing Projects paths attach automatically. create_project is a deprecated ordinary-client compatibility input; model-routed project creation belongs to project(entity='project', action='create'). Use Ledger, not generic memory, for financial records.

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• search_destination_sentiment

  SeaWeb

  Travel Product A — destination sentiment/trend AGGREGATES (use for "how do travelers feel about X over time", never for real-time alerts — that is the standing-query/event side). Returns the full (aspect x time-bucket) grid for one geo_id: per-cell cluster_count, quality-weighted mean AND variance, a 5-bin polarity histogram, language/source-tier breakdowns, and top-k canonical source URLs as receipts. Counts count deduplicated story clusters, never raw documents; cells nobody wrote about are explicit zero rows; aspects with no votes are NAMED in empty_aspects. aspects subset of: crowding, price, safety, weather, service, authenticity, accessibility. window_start/window_end ISO-8601 (default last 8 weeks); bucket day|week|month. Find geo_ids with resolve_geo. First call loads the embedding model server-side (slow once, then warm).

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• create_agent

  WebRun Browser Automation

  Create a scheduled agent — a recurring or one-time automation that runs on a timer in a real Chrome browser. Two modes. WORKFLOW MODE: pass workflowId plus a schedule — the platform copies everything else (prompt, starting URL, output contract, model, proxy, policy, files) from the workflow and bakes your variables into the prompt; results and memory stay centralised on the workflow. STANDALONE MODE: omit workflowId and provide name, prompt (a Goal / Ground rules / Stages / Output browsing runbook — see the prompt field description), and a schedule; the connection's environment is used automatically. Ask the user only what it should do and when it should run, in plain language — pick sensible defaults for everything else and state them; never ask about environments or technical settings. Results are delivered to your connected chat and appear in session history.

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• what_can_you_do

  netcafe-tasks

  Describe a task in plain language (any language) and get back exactly which tools on this server do it, with ready-to-run example calls — instead of reading the whole catalogue and guessing. Also returns multi-step recipes when a task needs several tools chained (invoices to a ledger, a bank statement reconciled, a messy CSV turned into a deliverable). Deterministic and free: it calls no model, costs nothing, and never runs out of quota. Call this FIRST when you are not sure what this server offers.

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• what_can_you_do

  netcafe-memory

  Describe a task in plain language (any language) and get back exactly which tools on this server do it, with ready-to-run example calls — instead of reading the whole catalogue and guessing. Also returns multi-step recipes when a task needs several tools chained (invoices to a ledger, a bank statement reconciled, a messy CSV turned into a deliverable). Deterministic and free: it calls no model, costs nothing, and never runs out of quota. Call this FIRST when you are not sure what this server offers.

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• what_can_you_do

  netcafe-almanac

  Describe a task in plain language (any language) and get back exactly which tools on this server do it, with ready-to-run example calls — instead of reading the whole catalogue and guessing. Also returns multi-step recipes when a task needs several tools chained (invoices to a ledger, a bank statement reconciled, a messy CSV turned into a deliverable). Deterministic and free: it calls no model, costs nothing, and never runs out of quota. Call this FIRST when you are not sure what this server offers.

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• what_can_you_do

  netcafe-build

  Describe a task in plain language (any language) and get back exactly which tools on this server do it, with ready-to-run example calls — instead of reading the whole catalogue and guessing. Also returns multi-step recipes when a task needs several tools chained (invoices to a ledger, a bank statement reconciled, a messy CSV turned into a deliverable). Deterministic and free: it calls no model, costs nothing, and never runs out of quota. Call this FIRST when you are not sure what this server offers.

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• what_can_you_do

  netcafe-translate

  Describe a task in plain language (any language) and get back exactly which tools on this server do it, with ready-to-run example calls — instead of reading the whole catalogue and guessing. Also returns multi-step recipes when a task needs several tools chained (invoices to a ledger, a bank statement reconciled, a messy CSV turned into a deliverable). Deterministic and free: it calls no model, costs nothing, and never runs out of quota. Call this FIRST when you are not sure what this server offers.

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• what_can_you_do

  netcafe-live-data

  Describe a task in plain language (any language) and get back exactly which tools on this server do it, with ready-to-run example calls — instead of reading the whole catalogue and guessing. Also returns multi-step recipes when a task needs several tools chained (invoices to a ledger, a bank statement reconciled, a messy CSV turned into a deliverable). Deterministic and free: it calls no model, costs nothing, and never runs out of quota. Call this FIRST when you are not sure what this server offers.

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• what_can_you_do

  netcafe-docflow

  Describe a task in plain language (any language) and get back exactly which tools on this server do it, with ready-to-run example calls — instead of reading the whole catalogue and guessing. Also returns multi-step recipes when a task needs several tools chained (invoices to a ledger, a bank statement reconciled, a messy CSV turned into a deliverable). Deterministic and free: it calls no model, costs nothing, and never runs out of quota. Call this FIRST when you are not sure what this server offers.

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• present_choices

  AgentPay (Russia)

  Create a comparison page (choice board, 2–4 options with pros/cons). MANDATORY when search_products returns 2+ similar hits or clarifyHint.action is present_choices — call immediately, do not wait for «сравни». For a basket/recipe: pass kind=bundles and wants[{q}] for EVERY ingredient in one call (server searches each want in category-matched stores only — PC parts → ТехноДвор, phones → ТехноСалон; no Auchan/Fix Price junk). Returns choiceSetId + pageUrl + catalogSearchScopeRu. Share BOTH pageUrl (VPN / abroad) and pageUrlRu (Russia without VPN) in chat ALWAYS, and speak sayToUserRu verbatim. Do NOT hand-pick SKUs from other stores when scope says ТехноДвор only. NEVER substitute a markdown table for this page (especially ChatGPT/Grok: pass canRenderImages=false, tell owner to open pageUrl). Do NOT create_purchase until get_choice_status shows chosen or the owner picks in chat (then pass clarification.confirmed). If AGENTPAY_API_KEY required and you already have sessionId from this chat: pass sessionId and retry. Never begin_agent_link again. Never ask the owner to edit connector settings or reconnect. Never web-search.

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• get_price_estimate

  BridgeNode MCP

  Estimate the USDC cost of a chat completion request before paying — free, no payment, no authentication required. Read-only: no state changes and no external calls; the estimate is computed locally from server pricing config, so repeated calls with identical inputs return identical results (idempotent). Use this tool to check the exact price for a given model/mode, messages, and max_tokens before calling the paid chat_completions tool. Provide either mode (auto/eco/premium routing) or model (explicit id, mutually exclusive with mode); one of the two is required — if both are sent, model wins. mode values: auto = cheapest model fitting the context, eco = cheapest available, premium = best model.

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• whenpeak_performance_now

  WhenPeak

  Report the current moment's performance state for the account this server is configured with: the score right now and whether now is a peak, a dip, or a neutral window. Returns the current score, the window type, a plain-language recommendation, and today's peak and dip times. Meant as a cheap check before an agent recommends, schedules or starts demanding work. Choose this tool for "right now". Use whenpeak_best_window to find a slot later today, and whenpeak_quick_predict when working from sleep the user describes rather than stored history. Requires WHENPEAK_API_KEY on the server and reads that one account's history, so it is only meaningful where the server runs with the user's own key. Without a key it returns a not_configured error rather than failing. Read-only and stores nothing, but each call counts against that account's monthly quota.

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• interpret_result

  QueueSim

  Given an M/M/c configuration (arrivalRate, serviceRate, servers) and optionally an observed average wait, returns a queueing-theory framed interpretation: where you sit on the utilization curve, what ρ means in plain language, what one more or fewer server would qualitatively do, and which complexity factors (priority, abandonment, skills routing) might be hiding in real data the M/M/c model can't see. Use this to TEACH while answering — when the user wants context around a number, not just the number itself. Pure text computation, no simulation, no RNG — deterministic output.

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