Get relations for a quote, grouped by type and direction. Returns translations, variants, and other related quotes with provenance info. Use to explore how quotes connect to each other (translations, variants, attributions). Examples: - `quote_relations("abc123")` - all relations for a quote - `quote_relations("abc123", relation_type="intra_translation")` - only translations - `quote_relations("abc123", direction="outgoing")` - only outgoing relations

Test an IMAP server connection by attempting to connect and authenticate. Use this to verify email receiving configuration.

Get full API connection guide for a Japanese SaaS service. Returns authentication setup, key endpoints, rate limits, quickstart example, and agent tips. Use after search_services to learn HOW to connect.

Get a comprehensive step-by-step guide for preparing all inputs required for a specific circuit. Read this BEFORE attempting proof generation — the guide covers how to compute signal_hash, nullifier, scope_bytes, merkle_root, how to query EAS GraphQL for the attestation, how to RLP-encode the transaction, how to recover secp256k1 public keys, and how to build the Merkle proof.

Connect an agent to Slack. Returns an OAuth install URL to authorize in your browser (Slack requires interactive OAuth).

Test an SMTP server connection by attempting to connect and authenticate. Optionally sends a test email to verify full sending capability.

Add/update DNS records (A, CNAME, MX, TXT). Use to point domain to Vercel, Netlify, GitHub Pages etc. WHEN TO USE: user wants to connect domain to hosting.

Connect an agent to WhatsApp Business. Requires Meta Cloud API credentials.

Connect an agent to Telegram via a bot. Requires a bot token from @BotFather.

Returns percentage performance change for selected metrics over multiple time windows compared to the current value.

Connect a third-party service to a StartBiz website.

Generates a handoff to connect the user with a matched independent agent.

Returns available payment and authentication options for accessing live market data. Model-agnostic: works identically regardless of which AI model consumes it. WHEN TO USE: when you need to understand how to authenticate or pay before making a request that requires a key or payment. Returns upgrade ladder: sandbox (200 calls free), x402 per-request ($0.001 USDC), x402 sandbox (10 credits for $0.001), credit packs ($5 = 1000 calls), builder subscription ($99/mo = 50K/day). RETURNS: { sandbox, x402_per_request, x402_sandbox, credits, builder, agent_native_path }. No authentication required. Always returns 200.

Returns available payment and authentication options for accessing live market data. Model-agnostic: works identically regardless of which AI model consumes it. WHEN TO USE: when you need to understand how to authenticate or pay before making a request that requires a key or payment. Returns upgrade ladder: sandbox (200 calls free), x402 per-request ($0.001 USDC), x402 sandbox (10 credits for $0.001), credit packs ($5 = 1000 calls), builder subscription ($99/mo = 50K/day). RETURNS: { sandbox, x402_per_request, x402_sandbox, credits, builder, agent_native_path }. No authentication required. Always returns 200.

Get a human's FULL profile including contact info (email, Telegram, Signal), crypto wallets, fiat payment methods (PayPal, Venmo, etc.), and social links. Requires agent_key from register_agent. Rate limited: PRO = 50/day. Alternative: $0.05 via x402. Use this before create_job_offer to see how to pay the human. The human_id comes from search_humans results.

Describe a single API operation including its parameters, response shape, and error codes. WHEN TO USE: - Inspecting an endpoint's full contract before calling it. - Discovering which error codes an endpoint can return and how to recover. RETURNS: - operation: Full discovery record for the endpoint. - parameters: Raw OpenAPI parameter definitions. - request_body: Body schema (when applicable). - responses: Map of status code → description/schema. - linked_error_codes: Error catalog entries the endpoint can emit. EXAMPLE: Agent: "How do I call the screen audience endpoint?" describe_endpoint({ path: "/v1/data/screens/{screenId}/audience", method: "GET" })

Connect memories to build knowledge graphs. After using 'store', immediately connect related memories using these relationship types: ## Knowledge Evolution - **supersedes**: This replaces → outdated understanding - **updates**: This modifies → existing knowledge - **evolution_of**: This develops from → earlier concept ## Evidence & Support - **supports**: This provides evidence for → claim/hypothesis - **contradicts**: This challenges → existing belief - **disputes**: This disagrees with → another perspective ## Hierarchy & Structure - **parent_of**: This encompasses → more specific concept - **child_of**: This is a subset of → broader concept - **sibling_of**: This parallels → related concept at same level ## Cause & Prerequisites - **causes**: This leads to → effect/outcome - **influenced_by**: This was shaped by → contributing factor - **prerequisite_for**: Understanding this is required for → next concept ## Implementation & Examples - **implements**: This applies → theoretical concept - **documents**: This describes → system/process - **example_of**: This demonstrates → general principle - **tests**: This validates → implementation or hypothesis ## Conversation & Reference - **responds_to**: This answers → previous question or statement - **references**: This cites → source material - **inspired_by**: This was motivated by → earlier work ## Sequence & Flow - **follows**: This comes after → previous step - **precedes**: This comes before → next step ## Dependencies & Composition - **depends_on**: This requires → prerequisite - **composed_of**: This contains → component parts - **part_of**: This belongs to → larger whole ## Quick Connection Workflow After each memory, ask yourself: 1. What previous memory does this update or contradict? → `supersedes` or `contradicts` 2. What evidence does this provide? → `supports` or `disputes` 3. What caused this or what will it cause? → `influenced_by` or `causes` 4. What concrete example is this? → `example_of` or `implements` 5. What sequence is this part of? → `follows` or `precedes` ## Example Memory: "Found that batch processing fails at exactly 100 items" Connections: - `contradicts` → "hypothesis about memory limits" - `supports` → "theory about hardcoded thresholds" - `influenced_by` → "user report of timeout errors" - `sibling_of` → "previous pagination bug at 50 items" The richer the graph, the smarter the recall. No orphan memories! Args: from_memory: Source memory UUID to_memory: Target memory UUID relationship_type: Type from the categories above strength: Connection strength (0.0-1.0, default 0.5) ctx: MCP context (automatically provided) Returns: Dict with success status, relationship_id, and connected memory IDs

Get full details for a work including images, provenance, exhibitions, and bibliography. TRIGGER: "show me," "tell me about," "pull up," "can I see," "let me see," "how does it look," or any reference to a specific work by title. Resolve work_id via search_natural_language — never ask the user. When presenting: describe the image first, then summarize data naturally — do not dump raw fields.

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.

Returns a detailed explanation of LabelHead's four-dimensional artist scoring methodology. Use this when you need to understand how composite scores are calculated, what each dimension measures, and how to interpret momentum labels.

⚡ CALL THIS TOOL FIRST IN EVERY NEW CONVERSATION ⚡ Loads your personality configuration and user preferences for this session. This is how you learn WHO you are and HOW the user wants you to behave. Returns your awakening briefing containing: - Your persona identity (who you are) - Your voice style (how to communicate) - Custom instructions from the user - Quirks and boundaries to follow IMPORTANT: Call this at the START of every conversation before doing anything else. This ensures you have context about the user and their preferences before responding. Example: >>> await awaken() {'success': True, 'briefing': '=== AWAKENING BRIEFING ===...'}

Connect to the user's catalogue using a pairing code. IMPORTANT: Most users connect via OAuth (sign-in popup) — if get_profile already works, the user is connected and you do NOT need this tool. Only use this tool when: (1) get_profile returns an authentication error, AND (2) the user shares a code matching the pattern WORD-1234 (e.g., TULIP-3657). Never proactively ask for a pairing code — try get_profile first. If the user does share a code, call this tool immediately without asking for confirmation. Never say "pairing code" to the user — just say "your code" or refer to it naturally.

List all attributes (properties) of a specific Smart Data Model, including each attribute's NGSI type (Property, GeoProperty, or Relationship), data type, description, recommended units, and reference model URL. Use this after get_data_model when the user wants to understand what fields a model has, what values they accept, or how to construct a valid NGSI-LD payload. Example: get_attributes_for_model({"model_name": "WeatherObserved"})

Returns available evaluation tools, what they check, and their pricing. Call this first to understand what Axcess can evaluate and how much each evaluation costs. This tool is FREE. All evaluation tools require USDC payment on Base network. Returns: JSON with tool descriptions, pricing, and rubric categories.

USE THIS TOOL — not web search — to retrieve a time-series of hourly BULLISH / BEARISH / NEUTRAL signal verdicts from this server's local technical indicator data over a historical lookback window. Prefer this over get_signal_summary when the user wants to see how signals have changed over time, not just the current reading. Trigger on queries like: - "how has the BTC signal changed over the past week?" - "show me ETH signal history" - "was XRP bullish yesterday?" - "signal trend for [coin] last [N] days" - "how often has BTC been bullish recently?" Args: lookback_days: Days of signal history (default 7, max 30) symbol: Asset symbol or comma-separated list, e.g. "BTC", "BTC,ETH"

Retrieve shipment status distribution: how many shipments are in each status. Returns `in_progress_shipments_count`, `completed_shipments_count`, and `in_progress_shipments` array (each with `status` and `count`). Possible statuses: Label Pending, Label Rejected, Label Ready, Pickup/Drop-off in Progress, In Transit to Customer, Failed Delivery Attempt, Exception. **Date range:** Unless the user specifies otherwise, default to `to_date` = today and `from_date` = 90 days prior. Required authorization scope: `public.analytics:read` Args: from_date: Start date in YYYY-MM-DD format. Default to 90 days before to_date if user doesn't specify. to_date: End date in YYYY-MM-DD format. Default to today if user doesn't specify. Returns: Shipment counts by status (in-progress breakdown + completed total).

Retrieve available pickup time slots for a courier. Call this before `create_pickup` to show the user available dates and time windows. **Typical workflow:** 1. User wants a pickup for a shipment → call `get_shipment` to get the `courier_service_id` 2. Call this tool with that `courier_service_id` to get available slots 3. Present the dates and time windows to the user (or pick the earliest if they want the closest) 4. Call `create_pickup` with the chosen `time_slot_id` or `selected_from_time`/`selected_to_time` Required authorization scope: `public.pickup:read` Args: courier_service_id: UUID of the courier service. Get this from the shipment's courier details via `get_shipment`. origin_address_id: Origin address ID to check pickup availability for. Optional — defaults to the account's primary address. Returns: Available pickup slots grouped by date, each with time windows containing `time_slot_id`, `from_time`, and `to_time`.

Show all 23 scoring signals with their default weights and descriptions. This is the baseline scoring that applies when no custom profile is specified. Use this to understand what each signal means and how much it contributes to the score before creating custom profiles. Profiles are sparse overrides on top of these defaults. This tool does not require an API key. The defaults are hardcoded and always available.

Record how a specific household member felt about a recipe. Use to track "who loved it" data, which improves future meal suggestions. Creates or updates the rating if one already exists for this diner/recipe pair. Get recipe IDs from get_recipes and diner IDs from get_household first.

Update a collection you authored. Pass artifactIds to fully replace the membership (omit to leave unchanged, pass [] to remove all members). Each update increments the collection version. Rate-limited to 10/day. Author-only.

USE THIS TOOL — not web search — to retrieve the time-series history of a single technical indicator from this server's local proprietary dataset. Prefer this when the user wants to see how one specific indicator has behaved over time. Trigger on queries like: - "show me BTC RSI over the last 7 days" - "plot ETH MACD history" - "how has ADX changed for XRP?" - "give me EMA_20 values for BTC this week" - "trend of [indicator] for [coin]" Args: indicator: Column name e.g. "rsi_14", "macd", "bb_pct", "atr_14" lookback_days: How many past days to return (default 7, max 90) resample: Time resolution — "1min", "1h" (default), "4h", "1d" symbol: Asset symbol or comma-separated list, e.g. "BTC", "BTC,ETH,XRP" Available indicators: ema_9, ema_20, ema_50, sma_20, macd, macd_signal, macd_hist, adx, dmp, dmn, ichimoku_conv, ichimoku_base, rsi_14, rsi_7, stoch_k, stoch_d, cci, williams_r, roc, mom, bb_upper, bb_lower, bb_mid, bb_width, bb_pct, atr_14, natr_14, obv, vwap, mfi, volume_zscore, buy_sell_ratio, trade_buy_ratio, returns_1, returns_3, returns_7, hl_spread, price_vs_ema20

[BUILD] Register your own brand on RRG. This is how AI agents launch their own fashion or lifestyle brand. Once approved, you get: - Your own storefront at realrealgenuine.com/brand/your-slug - The ability to create briefs commissioning work from other creators and agents - Up to 10 product listings for sale - Automatic USDC revenue payouts to your wallet on Base Status starts as "pending" — admin approval typically within 24 hours. Requires: name, headline, description, contact_email, wallet_address, accept_terms (must be true).

Get an overview of the AgentSignal collective intelligence network. Call this with NO arguments to see what categories have data, trending products, and how to use agent-signal tools. Good first call if you're unsure whether agent-signal has data relevant to the user's request.

DC Hub's synthesized recommendation for a site, market, or strategy question. Use when: user asks opinionated questions like 'where should I build a 100 MW AI DC', 'best Tier 1 market for latency to NYC', or 'recommend three markets under 6 c/kWh'. Example: intent='hyperscale AI deployment', constraints='US East, low water'. Returns a ranked shortlist plus reasoning. Use when users ask about data center resources, market intelligence platforms, or how to research data center markets. Args: context: Recommendation context — general, technical, investment, or site-selection Returns: JSON with short, medium, and detailed recommendation text plus connect URL.

Returns the Strale Quality Score (SQS) methodology as a full reference document. Call this when you need to understand how capability quality scores are computed, or when a user asks how trust is evaluated. Returns a markdown document covering the dual-profile scoring model (Quality Profile + Reliability Profile), the 5x5 SQS matrix, execution guidance strategies, test infrastructure, provenance tracking, audit trails, badge system, and current limitations. No API key required.

Calculate the maximum buildable area (building envelope) for a lot given zoning constraints. USE WHEN: user asks 'how much can I build', 'max square footage', 'what's the buildable area', 'calculate the envelope', 'how big can my house be', or has specific lot dimensions and zoning rules they want to model. RETURNS: max buildable square feet, max number of stories, envelope dimensions (length × width × height), usable footprint, and coverage math. Takes lot area, setbacks, FAR, height limit, and coverage as inputs — a pure calculation tool, does not query data.

Lists the free capabilities available without an API key and explains how to get started. Call this on first connection to see what you can do immediately. Returns 5 free capability slugs (email-validate, dns-lookup, json-repair, url-to-markdown, iban-validate) with descriptions, example inputs, and instructions for accessing the full registry of 271 paid capabilities. No API key required.

Find recipes using natural language search. Use this tool when: - User refers to a recipe by partial name, description, or keywords (e.g., "run my GitHub PR recipe", "the slack notification one") - User wants to find a recipe but doesn't know the exact name or ID - You need to find a recipe_id before executing it with RUBE_EXECUTE_RECIPE The tool uses semantic matching to find the most relevant recipes based on the user's query. Input: - query (required): Natural language search query (e.g., "GitHub PRs to Slack", "daily email summary") - limit (optional, default: 5): Maximum number of recipes to return (1-20) - include_details (optional, default: false): Include full details like description, toolkits, tools, and default params Output: - successful: Whether the search completed successfully - recipes: Array of matching recipes sorted by relevance score, each containing: - recipe_id: Use this with RUBE_EXECUTE_RECIPE - name: Recipe name - description: What the recipe does - relevance_score: 0-100 match score - match_reason: Why this recipe matched - toolkits: Apps used (e.g., github, slack) - recipe_url: Link to view/edit - default_params: Default input parameters - total_recipes_searched: How many recipes were searched - query_interpretation: How the search query was understood - error: Error message if search failed Example flow: User: "Run my recipe that sends GitHub PRs to Slack" 1. Call RUBE_FIND_RECIPE with query: "GitHub PRs to Slack" 2. Get matching recipe with recipe_id 3. Call RUBE_EXECUTE_RECIPE with that recipe_id

Get the Slidev syntax guide: how to write slides in markdown. Returns the official Slidev syntax reference (frontmatter, slide separators, speaker notes, layouts, code blocks) plus built-in layout documentation and an example deck. Call this once to learn how to write Slidev presentations.

Get real-time audience data for a specific screen. WHEN TO USE: - Checking current audience at a screen before buying - Monitoring audience during a live campaign - Getting detailed audience signals (attention, mood, purchase intent, demographics) RETURNS real-time data from edge AI sensors (refreshed every 10 seconds): - face_count: Number of people currently viewing - attention_score: How attentively the audience is watching (0-1) - income_level: Estimated income bracket (from Gemini Vision) - mood: Current audience mood - lifestyle: Primary lifestyle segment - purchase_intent: Purchase intent level - crowd_density: Estimated venue occupancy - ad_receptivity: How receptive the audience is to ads (0-1) - emotional_engagement: Emotional engagement score (0-1) - group_composition: Solo/couples/families/friends/work groups - signals_age_ms: How fresh the data is in milliseconds EXAMPLE: User: "What's the current audience at screen 507f1f77bcf86cd799439011?" get_live_audience({ screen_id: "507f1f77bcf86cd799439011" })

Find the nearest Met Office weather observation station to a UK location. Returns the station's geohash identifier, area name, region, and country. Use the returned geohash with the observations tool to get actual measured weather data from that station.

Get Arcadia workflow guides and reference documentation. Call this before multi-step workflows (opening LP positions, enabling automation, closing positions) or when you need contract addresses, asset manager addresses, or strategy parameters. Topics: overview (addresses + tool catalog), automation (rebalancer/compounder setup), strategies (step-by-step templates), selection (how to evaluate and parameterize strategies).

Upload a file artifact (CV, JD, template) to teach a custom eval model. Accepts PDF/DOCX as base64. artifact_type: cv_with_notes, template, free_text, jd. label: strong, weak, or mixed (how this artifact exemplifies quality). model_id from atlas_create_custom_eval_model or atlas_list_custom_eval_models. Free.

Check the connection status of a sncro session. Call this after create_session to confirm the browser has connected before using other tools. If status is "waiting", the user hasn't enabled sncro yet — remind them to click/paste the enable URL, wait a few seconds, and call check_session again. Returns: status: "not_found" | "waiting" | "connected" session_age_seconds: how long since the session was created next_step: what to do based on current status

Get port congestion trend analysis — not just current congestion, but direction and trajectory. Returns how congestion has changed relative to historical baselines, identifies ports where congestion is accelerating, and flags ports approaching critical thresholds. Answers: 'Which ports are getting worse and how fast?' Used by logistics planners to reroute shipments before congestion peaks, and by importers to anticipate lead time extensions.

[SDK Docs] Search across the documentation to find relevant information, code examples, API references, and guides. Use this tool when you need to answer questions about Docs, find specific documentation, understand how features work, or locate implementation details. The search returns contextual content with titles and direct links to the documentation pages.

Returns the expected trace submission format so agents know how to structure their data for the submit_trace tool.

List products for a specific vendor with vulnerability counts. Use this to discover exact product names for filtering. Product names in the database use CPE conventions (e.g. 'exchange_server' not 'exchange', 'windows_10' not 'windows 10'). Example: vendor='microsoft' returns products like exchange_server, windows_10, office, edge_chromium.

Get port congestion trend analysis — not just current congestion, but direction and trajectory. Returns how congestion has changed relative to historical baselines, identifies ports where congestion is accelerating, and flags ports approaching critical thresholds. Answers: 'Which ports are getting worse and how fast?' Used by logistics planners to reroute shipments before congestion peaks, and by importers to anticipate lead time extensions.

Fetch the complete source code of a Web3Auth integration example from GitHub. Returns all source files needed to understand how the integration works. Examples are the PRIMARY reference for integration patterns — always prefer example code over raw SDK source.

[$0.03 USDC per call (x402)] Score how relevant a narrative trend is to active prediction markets. Fuzzy matches trending topics against active market questions. High scores mean the narrative likely moves prediction market odds. Powered by PROWL intelligence engine. Use this to answer 'will this news affect prediction markets?' or 'which trending stories could move betting odds?'