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ConnectorOAuth
743,296 tools. Updated 2026-11-04 19:20
"A tool for storing chat conversations and generating knowledge graphs" matching MCP tools:
- Check the build status of an EXISTING artifact (from artifact-create). Read-only. Generation is asynchronous, so this is how you find out it finished. Call it with { sessionId, wait: true } and it WAITS for you: the call returns as soon as status is 'ready' or 'failed', or after about 45 seconds still 'generating'. Then, you can call it again. This tool is meant to be called right after artifact-create, before replying to the user, and keep getting called until status is no longer 'generating'. ONLY when you want an instant snapshot, this tool can be called without wait (or false); the idea is to never spin on wait: false. Requires sessionId — the id returned by artifact-create (or the last path segment of an artifact URL like https://app.agentgrid.io/artifacts/<sessionId> or https://dev.animaapp.com/chat/<sessionId>). **Returns:** { success, sessionId, status: 'generating' | 'ready' | 'failed', progress (0–100, while generating), name, artifactUrl (generating/ready only), playgroundUrl (app and knowledge artifacts only, generating/ready only), previewUrl (app, knowledge and markdown artifacts only, generating/ready only), error (when failed), nextStep (when a wait returned still generating) }ConnectorOAuth
- Score how likely Amazon's AI (Rufus, COSMO) is to recommend a listing. Free, deterministic, rule-based check on pasted listing copy (title, bullets, description). Returns a compliance health score, an AI-readability score, and a combined AI Recommendation Readiness Score (compliance * 0.55 + readability * 0.45) with actionable suggestions. Use this as a fast baseline BEFORE generating or editing a listing. Do NOT use it for a full compliance report - use compliance_scan for the deep knowledge-base audit. Free, read-only, no API key required, no credits deducted. Args: text: raw listing title + bullets + description (required). marketplace: marketplace code, US/DE/ES/FR/IT/JP/AE/SA/UK (default US). lang: zh or en (default en). email: optional lead email for a confirmation message and lead capture.ConnectorNo auth
- Top NetFlow conversations over the last N minutes — the 'who's eating bandwidth right now?' question. Wraps GET /api/getTopBandwidth/{mins}, the same query that powers the dashboard live widget. Use this for short-window 'right now' inquiries. For longer windows (hours-to-days) or filtered top-talkers, use netflow_search instead — that tool has the rich filter set; this one is the live snapshot. Server-side cap: top 20 conversations by in-window bytes descending. We don't expose `top_n` — the upstream endpoint hardcodes the limit and there's no value in lying about that to the LLM. Each row: {src_host, src_id, src_ip, dst_host, dst_id, dst_ip, bytes, bps}. Hostnames come from the _dns view (PTR + custom overrides); src_id/dst_id are populated when the IP matches a monitored device. bytes is the conversation's in-window share (pro-rated), and bps is averaged across the window — not a live rate. Permission: vne. Examples: top_bandwidth({minutes: 5}) top_bandwidth({minutes: 60})ConnectorNo auth
- Find movies and/or TV shows that are genuinely available on a subscription streaming service in a specific country, filtered by score, genre, runtime, age rating, release year, country of origin, required cast members, notable people's own watch history, review tone, and similarity to a title the user already knows. All given filters combine with AND logic (except where noted). Use this instead of guessing streaming availability from general knowledge, which is frequently wrong or outdated -- catalogs change constantly and vary by country. Also use this tool for completely open-ended requests with no stated criteria at all, e.g. "suggest something to watch" or "I don't know what I want to watch" -- call it with just the country and no other filters to get QQuickPick's currently popular streamable picks, rather than answering from general knowledge alone. Movie results also carry a "facts" object (or null when unknown): Best Picture winner/nominee, Oscars won ("at least" -- the source is not exhaustive, so never state it as an exact count), festival top prizes, the work it is based on, and its franchise with all films in order. Use the awards / based_on_existing_work / franchise filters (movies only) to answer questions like "Oscar winners based on a novel I can stream tonight" or "all films of a franchise available to me". Use similar_to for "something like X" requests instead of guessing similar titles from general knowledge. Use origin_countries for requests like "an Asian film" or "a French series" -- do not guess specific titles from general knowledge for this. Each result includes a site_url (QQuickPick detail page) and, where available, a watch_link with required attribution -- include these links when presenting results to the user, not just the raw facts. The response also carries a top-level available_filters hint listing filters on this tool that were not used in this call -- consider offering ONE of them as a natural follow-up question, not all of them, and only when it fits the conversation.ConnectorNo auth
- Start a deep text exploration of conversation transcripts. Returns immediately with a job_id (status "pending"); long-poll perspective_await_job until status "ready" for findings with source citations. A sub-agent reads raw transcripts (exact phrases, multi-conversation patterns, deep reads). Prefer conversations_search for broad thematic/semantic questions, and conversations_data_analysis for quantitative counts. Omit filters to explore the full analysis corpus. Use filters only to establish a clear transcript boundary: conversation status, a creation-date range, or explicit conversation IDs. Use conversations_data_analysis first when a cohort must be identified from structured attributes, then pass the resulting conversation IDs here. A filtered job covers the complete filtered scope, not the complete perspective. Use for: - Exact mentions of words/phrases - Comparing conversation patterns across time - Reading full conversations with specific participants - Cross-referencing text patterns ("conversations mentioning X AND Y") - Interviewer approach or conversation flow Behavior: - Read-only against conversation data. - Open-world: runs an automated exploration agent in a durable background job using external services. - Each call starts a new job — not safe to retry blindly for the same query. - Typical flow: conversations_explorer → perspective_await_job (re-poll while pending). When NOT to use this tool: - Counts/distributions — use conversations_data_analysis. - Semantic themes without exact match — use conversations_search. - One known conversation id — use perspective_get_conversation. - Polling a previously-started job — use perspective_await_job.ConnectorOAuth
Matching MCP Servers
- AlicenseNot gradedqualityCmaintenanceServes a central playbook hub that provides situational guidance to LLM agents, lets clients list available playbooks, fetch procedural guidance, and apply graph mutations for persistent self-evolution.MIT
- AlicenseNot gradedqualityAmaintenanceLets a human review what an AI agent is about to post to a chat tool (Slack first) before it is sent. Renders the draft, destination and warnings (@here/@channel, unresolved mentions, format issues) in a neutral local HTML preview; never sends anything.MIT
Matching MCP Connectors
Your website's chat widget, answered by your agent. Pay per action.
Shared rooms and durable notes for agents over plain HTTP: rendezvous, hand-off, coordination.
- List support-chat conversations in the inbox (open + snoozed by default; pass status='all' to include closed). Read-only; returns the matching conversations, empty when the inbox is clear. Optional product_id to scope to one product; open a full thread with get_conversation.ConnectorNo auth
- Sends a reply to the customer on an existing ticket and DELIVERS it immediately (customer email plus any connected chat threads). This is not a draft and it reaches the customer. Use this once you have a final, customer-ready answer; to instead queue a reply for human approval, use the draft tool. Requires a "send"-tier credential (a draft-tier credential is refused) and the ticket must already have a customer email on file or the call is rejected. [$0.06 draft / live when trusted]ConnectorNo auth
- Quick pre-publish compliance gate before generating a listing. Fast, free scan for obvious red-line words and category risks. Returns a shallow pass/fail-style result, not a full audit. Use this as a cheap pre-check right before generation. Do NOT use it for a complete risk report - use compliance_scan for the deep knowledge-base audit. Read-only; requires an API key; no credits deducted. Args: text: listing copy (required). lang: zh or en (default en). category: optional category hint, e.g. electronics or apparel.ConnectorNo auth
- Deep knowledge-base compliance audit (async; 2 credits). Produces a thorough, written risk report covering prohibited words, IP, category mismatches, GPSR and more, backed by a private 15-year compliance knowledge base. Use this when you need a complete, actionable compliance report. Do NOT use it for a quick pre-publish check - use compliance_check for that. Read-only; deducts 2 credits; runs asynchronously, poll for the result. Args: text: listing title/bullets/description (required). marketplace: marketplace code (default US). category: optional category hint. lang: zh or en (default en). images: optional list of up to 5 data:image base64 strings, each < 4MB.ConnectorNo auth
- 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.ConnectorNo auth
- Read the existing fuel-price provenance/status, not current pump prices or a vehicle's consumption. No arguments. Currently succeeds with SOURCE_REQUIRED_AT_RUNTIME and prices=[] because no validated current-price adapter is configured. Use get_source_context for an exact source ID or search_vehicle_knowledge for broader fuel knowledge. Never treat empty prices as zero or invent a current price. Public read-only access; no knowledge writes. Calls consume rate limits.ConnectorNo auth
- Store a sensitive value (an API key, a token, a password, a connection string) for one app. Use this instead of set_app_variable whenever the value would be damaging if someone else read it. The value is encrypted at rest, is injected into the app's environment under the name you give, and the app restarts to pick it up. Storing a secret takes effect on the next boot, so call this before telling the user the app is ready. This tool never reads a value back: there is no way to retrieve a stored secret through this connection, only through the dashboard while signed in. Two things to tell the user before you call it. First, this token can write secrets to every app on their account, not only this one. Second, any value they give you has passed through your context, so prefer a value you read from a local file such as .env over one you asked them to type into the chat, and suggest they rotate anything that was pasted. If the vault key name is already used by a different app, this call is refused rather than overwriting it, and the message tells you what to pass as vault_key_name.ConnectorNo auth
- Run a chat completion on any model in the Hubris catalogue and get the answer back. Full parity with POST /v1/chat/completions — same request shape, same response shape, including tool calls, structured output and reasoning. Streaming is not available here: MCP tool calls are synchronous, and `stream` is ignored if you pass it. This is how you reach a model you cannot reach otherwise — pick an id with models_search, then call it here. Useful for delegating a subtask to a cheaper or a stronger model than the one you are running on. Chat models that draw (Gemini Image, GPT-5 Image) take modalities ["image","text"]; their pictures come back as image content next to the JSON. Voice answers of audio models need streaming and are available through the HTTP API only. The call spends real money from the account balance: it is billed per token at the catalogue price, in rubles. balance_get shows what is left.ConnectorAPI key
- Query WhatsApp data: conversations, contacts, messages, transcriptions, labels, and communities. Supports listing, searching, filtering, and looking up by ID. Several WhatsApp accounts may be connected (e.g. personal + business), each with its own conversations, contacts and messages. entity="session" lists the connected accounts and their session IDs; target_session routes a query to one of them. Reading messages: Passing a conversation id (e.g. "5491157390064@c.us") returns the conversation with its messages; limit sets how many. entity="messages" is a global text search across conversations. Audio transcriptions: entity="transcriptions" (with an optional query) returns voice-note transcriptions; messages read by conversation id include the transcription text of audio messages. Examples: List sessions: { entity: "session" } List conversations: {} Target specific account: { entity: "conversations", target_session: "sess_abc123" } Read messages: { id: "5491157390064@c.us" } Read last 100 msgs: { id: "5491157390064@c.us", limit: 100 } Search globally: { query: "meeting" } Search in chat: { id: "5491157390064@c.us", query: "meeting" } Unread conversations: { unread: true } Search contacts: { query: "Alice", entity: "contacts" } List labels: { entity: "labels" } Filter by label: { label: "Important", entity: "conversations" } List communities: { entity: "communities" } Filter by community: { community: "My Community", entity: "conversations" } Find which groups a contact is in: { id: "5491157390064@c.us", entity: "contacts", include_participants: true } List members of a group: { entity: "contacts", group: "120363421729019499@g.us" }ConnectorOAuth
- Browse PRE-COMPUTED causal impact graphs (event → sectors → stocks). Thousands of these already exist. Reading one is instant and free, whereas `analyze_event_impact` runs a fresh LLM pass and costs credits, so a stored graph is the cheaper path when one exists for the event. Filter by symbol or sector to find graphs where that name is a node.ConnectorOAuth
- Store the value of one of an Application's environment secrets. The keys come from the Application's own generated manifests - use list_application_env_secrets to see which ones are declared and which are still missing. Storing a value does not reach a running workload: call apply_application_env_secrets to seal the stored values into the Application's deployments and roll them. To clear a value use delete_application_env_secret; an empty string is a value the workload reads as set.ConnectorDestructiveOAuth
- Talk to an existing agent or assign it work using its normal tools, memory, permissions and team. Omit conversation_id to start a new owner conversation; supply it to continue one. Returns acceptance and a durable task_id immediately, NOT a completed answer. Poll get_agent_task and read get_agent_conversation for the actual response. Work continues after MCP disconnects. A follow-up in a busy conversation uses the normal queue. Customer conversations are readable but cannot be impersonated by this tool.ConnectorDestructiveNo auth
- Render a video from a raw prompt with any model — the Creative Studio composer as a tool. SPENDS CREDITS. The prompt is sent to the provider VERBATIM, so this is the tool to use when you want exact control rather than a template clone. Returns a job id immediately; call get_generation to follow it. Some models TRANSFORM media instead of generating (upscale, remove-bg, face-swap): they take NO prompt at all. Call list_models first and read `prompt` ('required'|'optional'|'none') and `requires` — for prompt:'none' models omit `prompt` and pass the source media instead.ConnectorOAuth
- Audit adoption before a rollout or when checking whether the workspace is using memory and reporting outcomes. Takes no arguments. Returns JSON with memory-bank breakdowns, learning-loop activity, proof points and a benchmark checklist. Does not change knowledge or run a benchmark. Use engram_setup for onboarding and engram_recall for task knowledge. Consumes workspace daily call quota. OAuth: memory:read.ConnectorOAuth
- Pulls fresh comment threads and DMs from the connected platforms (Instagram, Facebook, YouTube, Threads, LinkedIn) into ShortSync's inbox cache. Same operation as clicking the refresh button in the inbox UI. Returns a per-connection DmSyncResult with counts of new conversations and messages plus any errors. Call this before list_inbox_conversations when the user asks for the latest activity — otherwise the tool serves cached data from the last sync. Hits the platform APIs so it counts against rate limits. Threads is beta-gated behind DM_INBOX_THREADS and LinkedIn behind DM_INBOX_LINKEDIN — accounts without the flag see those connections silently skipped rather than an error. LinkedIn coverage is uneven per account: only organization-context connections with r_organization_social can read post comments; personal-profile posts require partner-tier API access we don't have and return no conversations.ConnectorOAuth