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127,485 tools. Last updated 2026-05-05 19:34

"A platform for monitoring and analyzing data in real-time" matching MCP tools:

  • Live DKIM DNS lookup — queries <selector>._domainkey.<domain> TXT record in real time and returns the DKIM key record, errors and warnings. Does NOT require a project — works for any domain, even ones not monitored. Use this to verify a DKIM selector exists, check key length, or diagnose signing failures.
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  • Get the aggregate wash-report dataset: 30-day total active buyers, real-volume %, suspected_wash and self_test counts, full 8-label distribution, 14-day wash percentage time series, and five anonymized case studies (Service A through E) with pattern signals. For per-address real-time wash analysis with full signal breakdown, use the paid POST /api/v1/wash/check HTTP endpoint ($0.05 USDC) — that endpoint speaks x402, agents pay and receive data in a single HTTP round-trip.
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  • Check real-time inventory, price, and shipping for a product SKU. This tool queries the connected e-commerce platform (Shopify, WooCommerce, etc.) for live inventory data. Returns current stock level, price, and availability status. Args: sku: Product SKU (Stock Keeping Unit) - e.g., "RED-WIDGET-001" Returns: Dictionary with: - sku: The requested SKU - stock: Current inventory count - price: Current price in USD - can_ship_today: Boolean indicating same-day shipping availability - message: Human-readable status message Example: >>> await check_stock("WIDGET-001") { "sku": "WIDGET-001", "stock": 42, "price": 29.99, "can_ship_today": True, "message": "✅ WIDGET-001 (Awesome Widget) - 42 in stock at $29.99" }
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  • Fetch a live Solana DEX divergence trading signal from Soliris Arc — the agent-to-agent data market built on Arc (Circle's L1 blockchain). Each signal costs $0.001 USDC paid automatically on-chain via the x402 protocol. Signals identify real-time arbitrage spreads across Raydium, Orca, Jupiter, and Meteora. This is the agentic economy in action: your AI pays another AI for data, settled in under 1 second, no humans in the loop. Use demo=true to get a sample signal without payment. For live signals the API returns a 402 with payment details. Powered by Soliris (soliris.pro).
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  • 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" })
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  • Get the aggregate wash-report dataset: 30-day total active buyers, real-volume %, suspected_wash and self_test counts, full 8-label distribution, 14-day wash percentage time series, and five anonymized case studies (Service A through E) with pattern signals. For per-address real-time wash analysis with full signal breakdown, use the paid POST /api/v1/wash/check HTTP endpoint ($0.05 USDC) — that endpoint speaks x402, agents pay and receive data in a single HTTP round-trip.
    Connector

Matching MCP Servers

  • A
    license
    C
    quality
    C
    maintenance
    Enables interaction with Google Cloud services including billing cost analysis, log querying, and metrics monitoring through natural language commands. Provides comprehensive tools for managing GCP resources, analyzing costs, detecting anomalies, and retrieving operational insights.
    Last updated
    40
    1
    Apache 2.0

Matching MCP Connectors

  • Time MCP server via HTTP

  • Get the current time anywhere and access concise timezone information. Set your preferred timezone…

  • [Step 3 of find_a_clinician] Real-time availability for ONE specific provider. Returns the next 10 open slots with start timestamps. Use when: The user has picked a provider from find_provider / start_here and you need fresh slot data before book_appointment. Don't use when: You don't have a provider_id yet — use find_provider first. Example: check_availability({ state: 'Florida', appointment_type: '354092', provider_id: 'abc123' })
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  • Use this as the primary tool to retrieve a single specific custom monitoring dashboard from a Google Cloud project using the resource name of the requested dashboard. Custom monitoring dashboards let users view and analyze data from different sources in the same context. This is often used as a follow on to list_dashboards to get full details on a specific dashboard.
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  • Is AgentMarketSignal working? Check the real-time status of all 5 AI data pipelines (whale tracking, technical analysis, derivatives, narrative sentiment, market data) and the signal fusion engine. Returns last run times, durations, and any errors.
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  • Get detailed info for a single lending pool including APY history over time. Useful for analyzing rate trends and comparing pools. Use read_pool_list to discover pool addresses.
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  • Look up locations for up to 100 IP addresses at once. Returns geolocation and ISP data in the same order as input. Use for analyzing multiple IPs efficiently.
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  • Get the weekly 'Signal of the Week' content package — a pre-written, data-verified marketing bundle generated every Monday from live SupplyMaven data. Returns a Substack article (~500 words), LinkedIn post (~200 words), and Twitter/X thread (4-5 tweets), all built from verified supply chain data. Every number in the content traces back to a live data source. Designed for automated content distribution via Claude Desktop + platform MCP servers. The content package includes the signal headline, full data context (GDI, SMI, commodities, ports, signals), and platform-specific formatted content ready for publishing.
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  • Generate and plot synthetic financial price data (requires matplotlib). Creates realistic price movement patterns for educational purposes. Does not use real market data. Note: Use for time-series price data with optional moving average overlay. For general XY data, use plot_line_chart instead. Examples: plot_financial_line(days=60, trend='bullish') plot_financial_line(days=90, trend='volatile', start_price=150.0, color='orange')
    Connector
  • Fetch a live Solana DEX divergence trading signal from Soliris Arc — the agent-to-agent data market built on Arc (Circle's L1 blockchain). Each signal costs $0.001 USDC paid automatically on-chain via the x402 protocol. Signals identify real-time arbitrage spreads across Raydium, Orca, Jupiter, and Meteora. This is the agentic economy in action: your AI pays another AI for data, settled in under 1 second, no humans in the loop. Use demo=true to get a sample signal without payment. For live signals the API returns a 402 with payment details. Powered by Soliris (soliris.pro).
    Connector
  • Get statistics about available causal training data: total tuples, unique creatives, venue diversity, date range. Queries observation_stream for rows that have both a creative ID and a VAS outcome recorded, giving a picture of how much training data is available for the causal prediction engine. WHEN TO USE: - Checking if enough data exists for reliable causal predictions - Understanding the diversity of training data (creatives, venues, time range) - Monitoring causal dataset health and growth - Planning data collection strategies RETURNS: - data: Dataset statistics - total_tuples: number of context-action-outcome records - unique_creatives: number of distinct creatives with VAS data - unique_venue_types: number of distinct venue types represented - date_range: { start, end } of available data - observations_per_creative: { min, max, mean, median } distribution - metadata: { query_window_days } - suggested_next_queries: Follow-up queries EXAMPLE: User: "How much causal training data do we have?" get_dataset_stats({})
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  • Detect anomalies in time-series data — use after pulling numeric metrics from monitoring APIs, financial data sources, IoT sensors, or spreadsheet columns. Send a single numeric array and specify a window size. Early windows define 'normal', recent windows are tested for anomalies. Typical workflow: (1) Pull a column of numbers from Sheets, a Supabase time-series table, or a metrics API. (2) Pass the array here. (3) Get back which time windows are anomalous. Examples: - Revenue monitoring: Pull monthly revenue from Sheets → detect anomalous months - Stock screening: Pull 90 days of closing prices → find unusual price windows - Server health: Pull response-time metrics → identify degradation windows - Sensor QA: Pull temperature readings from IoT API → flag sensor drift
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  • Generate AI-powered platform-optimized content without publishing. Uses AI to create platform-specific text, hashtags, and titles from a prompt or media URL. Respects brand voice profiles if configured. Returns generated content variants for each target platform. Use publish_content to publish the generated content, or publish_ai to generate and publish in one step.
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  • Query real data from a dataset. Check instructions for featured dataset_ids and NOTES section for common filter patterns (municipal budgets, contracts, weather, energy, fuel prices).
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  • Monitor real-time vessel traffic and congestion at critical maritime chokepoints — Suez Canal, Panama Canal, Strait of Malacca, Strait of Hormuz, Bab el-Mandeb, and other strategic waterways. Returns total vessel count, average speed, count of slow or stationary vessels, and a congestion score with severity level. When chokepoints congest or close, global shipping routes reroute within days — this data detects that signal in real time.
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  • Check if a product is currently available. Uses Shopify Storefront API to verify real-time stock status. Use when a customer asks 'is MIRA in stock?' or before recommending a product.
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