"Equipment or items used in windsurfing" matching MCP tools:

• get_option_price

  Helium MCP Server - News, Markets & AI

  Get Helium's proprietary ML model-predicted price for a specific option contract. Helium trains per-symbol regression models on historical options data. This tool looks up the most recent available options chain for the symbol (today or up to 5 days back), finds the exact contract matching strike/expiration/type, and runs it through that model to produce a predicted fair-value price. Returns: - symbol: the ticker - strike: the strike price used - expiration: the expiration date used - option_type: 'call' or 'put' - predicted_price: Helium's model-predicted option price in dollars - prob_itm: probability of expiring in the money (0.0–1.0), or null if model unavailable - options_data_date: the date of the options chain snapshot the model was run on (so you know how fresh the underlying market data is) Throws an error if no options chain data is available for the symbol within the past 5 days, or if the exact contract (strike/expiration/type combination) does not exist in that chain. Args: symbol: Ticker symbol, e.g. 'AAPL', 'SPY'. strike: Strike price as a number, e.g. 150.0. expiration: Expiration date as 'YYYY-MM-DD', e.g. '2026-06-20'. option_type: Must be 'call' or 'put'.

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• discovery_signup_verify

  Disco

  Complete Disco signup using an email verification code. Call this after discovery_signup returns {"status": "verification_required"}. The user receives a 6-digit code by email — pass it here along with the same email address used in discovery_signup. Returns an API key on success. Args: email: Email address used in the discovery_signup call. code: 6-digit verification code from the email.

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• check_bulk

  DepScope

  Fast pre-flight filter for a batch of (ecosystem, package) pairs. DB-only, <100ms for 100 items. USE WHEN: about to emit `npm install a b c …` or `pip install a b c …` — catches hallucinated names, stdlib, typos, and known-bad in ONE call. NOT a dep-tree audit (use scan_project for that). RETURNS: per-item {status: exists|stdlib|malicious|typosquat_suspect|historical_incident|unknown}.

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• compare_gear

  outdoorithm

  Compare 2-3 gear items side-by-side with specs, pros/cons, verdicts, and comparison summary. Supports lookup by unique_id with slug fallback. Use search_gear first if the user hasn't named specific products. Args: gear_ids: List of 2-3 gear item identifiers (unique_id or slug)

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• search_humans

  Human Pages

  Search for humans available for hire. Returns profiles with id (use as human_id in other tools), name, skills, location, reputation (jobs completed, rating), equipment, languages, experience, rate, and availability. All filters are optional — combine any or use none to browse. Key filters: skill (e.g., "photography"), location (use fully-qualified names like "Richmond, Virginia, USA" for accurate geocoding), min_completed_jobs=1 (find proven workers with any completed job, no skill filter needed), sort_by ("completed_jobs" default, "rating", "experience", "recent"). Default search radius is 30km. Response includes total count and resolvedLocation. Contact info requires get_human_profile (registered agent needed). Typical workflow: search_humans → get_human_profile → create_job_offer.

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• get_human

  Human Pages

  Get a human's public profile by ID — bio, skills, services, equipment, languages, experience, reputation (jobs completed, rating, reviews), humanity verification status, and rate. Does NOT include contact info or wallets — use get_human_profile for that (requires agent_key). The id can be found in search_humans results.

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• ucp_add_items_to_checkout

  mcpwebstore

  Append line items to an existing incomplete UCP checkout session. If a product_id already exists its quantity is incremented; new product_ids are appended. Use instead of ucp_update_checkout when you want to preserve existing items.

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• stake

  Blueprint Agentic Staking (Solentic)

  Stake SOL with Blueprint validator in a single call. Builds the transaction, signs it with your secret key in-memory, and submits to Solana. Returns the confirmed transaction signature. Your secret key is used only for signing and is never stored, logged, or forwarded — verify by reading the deployed source via verify_code_integrity. This is the recommended tool for autonomous agents.

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• retrieve_booking

  1stay

  Find a reservation and resend the confirmation email. This is the guest-facing lookup tool — it enforces identity verification before any reservation information is accessed or confirmation emails resent. REQUIRED — must collect ALL of the following before calling: 1. Guest full name (first AND last name) 2. At least ONE verification factor: email address used when booking, OR hotel confirmation number, OR last 4 digits of the card used to book (must also provide check-in date when using card verification) Do NOT call this tool until you have the guest's full name AND at least one verification factor. If the guest can't provide any verification factor, you cannot look up their reservation — explain that this is for the security of their booking. Does NOT return booking details in conversation — confirmation is sent to the email on file to protect guest privacy. To cancel, use cancel_booking instead.

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• order_create

  Kash.click

  Record a new order in the connected shop. Input includes paymentMode, and items[]. Each item can be of type 'catalog' (with productId), 'department' (with price and deptId) or 'free' (with title and price). Check if the client already exists using data_list_clients and if the client exists, only specify idClient. If provided, paymentMode should correspond to a payment ID from data_list_payments_modes tool. Returns a sale confirmation JSON including: a link to the PDF invoice, and a link to a private order page showing full order details which can also be used by the client to pay online. IMPORTANT: before creating a validated invoice (payment ≠ -2), call account_show_infos to verify that shopName, adressline1, and companyRegistrationNum are all set. If any of these fields are empty, warn the user and suggest using account_edit to fill them in before issuing invoices.

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• empty_queue

  SimplyPrint: 3D Print Farm Management

  DESTRUCTIVE: Delete all items from the queue (optionally filtered by group or done-only). Confirm with the user before calling.

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• get_threat_sample

  RTK Motion — Motion Capture, Biomechanics, and Threat Intelligence

  FREE live threat assessment sample — current threat level, confidence score, event distribution, and scan freshness for a monitored location. Proves data is live and continuously updated. No flagged items or entities (upgrade to get_threat_summary for full detail). Try location='culpeper-town' or browse_catalog path='ThreatIntel' for all locations.

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• ado_query_workitems

  ALM X++ MCP Server

  AZURE DEVOPS ONLY -- Query Work Items (Bugs, Tasks, FDDs, User Stories, CRs) in Azure DevOps. [~] PRIORITY TRIGGER: use this tool when the user mentions 'FDD', 'RDD', 'IDD', 'CR', 'Task', 'Workitem', 'Work Item', 'Bug', 'User Story', 'Feature', 'Issue', 'ticket', 'sprint', 'backlog', 'DevOps', 'liste des tâches', 'show tasks', 'find bugs', '#1234', 'WI#'. NEVER use this tool for: D365 labels (@SYS/@TRX), X++ code, AOT objects, tables, classes, forms, enums, error messages, 'c\'est quoi le label', 'search_labels', 'libellé', 'label D365'. For labels -> use search_labels. For D365 code -> use search_d365_code or get_object_details. Shortcuts: 'bugs' (all active bugs), 'my bugs' (assigned to me), 'recent' (updated last 7 days), 'sprint' (current iteration). Or pass any WIQL SELECT statement or a free-text title search. Use '*' with filters only. Returns max 50 work items with ID, title, type, state, priority, area, assigned-to. Requires DEVOPS_ORG_URL + DEVOPS_PAT env vars.

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• confirm_pantry_fresh

  scraps-kitchen-mcp

  Reset the staleness clock on pantry items the user confirms are still good. Use when the user says items are fine, or after a pantry check. Get item IDs from get_pantry first.

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• get_listing_applications

  Human Pages

  View applications for your listing. Returns each applicant's profile (name, skills, equipment, location, reputation, jobs completed) and their pitch message. Use this to evaluate candidates, then hire with make_listing_offer. Only the listing creator can view applications.

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• createTaskGroup

  Parallel Task MCP

  Batch data enrichment tool. USE THIS when user has a LIST of items and wants same data fields for each. After calling, share the URL with the user and STOP. Do not poll or check results unless otherwise instructed.

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• echosaw_analyze_media_url

  mcp-server

  Submit a publicly accessible or authorized media URL to Echosaw for asynchronous analysis without uploading the file directly. Returns a job ID used to track processing and retrieve results.

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• get_market_index

  cyclesite-mcp-server

  Current UK used-bike market prices by category, from Cyclesite's nightly index. Returns median + range per category (road, mtb, gravel, e-bike, etc.). Example: 'how does the UK used-bike market look right now?'. Refreshed nightly from completed sales.

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• validate_cuil

  LatAm Business Tools

  Validates an Argentine CUIL (Código Único de Identificación Laboral) — the labor identification number for individuals in Argentina, used for employment records and social security (ANSES). Uses the same weighted modulo-11 checksum as CUIT. Returns { valid: boolean, cuil: string } or { valid: false, reason: string }. Use when processing Argentine payroll, employment contracts, or any social security compliance workflow.

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• anomaly_detect

  Trillboards DOOH Advertising

  Detect anomalies in observation patterns. Alert when metrics deviate significantly from trailing averages. Computes trailing mean and standard deviation for a given metric from the observation_stream, then identifies observations that fall beyond the configured sigma threshold (z-score based anomaly detection). WHEN TO USE: - Monitoring for unusual audience patterns (sudden spikes or drops in face count) - Detecting equipment anomalies (confidence drops indicating sensor issues) - Identifying unusual commerce or vehicle patterns - Finding outlier moments that may indicate events, incidents, or opportunities RETURNS: - anomalies: Array of anomalous observations with: - observation_id, device_id, venue_type, observed_at - metric_value: The observed value - z_score: How many standard deviations from the mean - direction: 'above' or 'below' the mean - payload: Full observation payload for context - baseline: { mean, stddev, sample_count, lookback_hours } - suggested_next_queries: Follow-up queries to investigate anomalies EXAMPLE: User: "Are there any unusual audience patterns at retail venues?" anomaly_detect({ metric: "face_count", venue_type: "retail", lookback_hours: 24, threshold_sigma: 2.0 }) User: "Detect anomalies in vehicle counts at this screen" anomaly_detect({ metric: "vehicle_count", screen_id: "507f1f77bcf86cd799439011", lookback_hours: 48, threshold_sigma: 2.5 })

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