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133,431 tools. Last updated 2026-05-13 02:28

"A search engine for OECD statistics and data" matching MCP tools:

  • Permanently delete a sweepstakes and all associated data including participants, statistics, and automations. Use fetch_sweepstakes first to get the sweepstakes_token. CRITICAL: This is a DESTRUCTIVE operation that cannot be undone. ALWAYS ask for explicit user confirmation before deleting, showing the sweepstakes name. NEVER delete multiple sweepstakes in batch or loops. If user requests bulk deletion, refuse and ask them to delete one at a time after reviewing each. # delete_sweepstakes ## When to use Permanently delete a sweepstakes and all associated data including participants, statistics, and automations. Use fetch_sweepstakes first to get the sweepstakes_token. CRITICAL: This is a DESTRUCTIVE operation that cannot be undone. ALWAYS ask for explicit user confirmation before deleting, showing the sweepstakes name. NEVER delete multiple sweepstakes in batch or loops. If user requests bulk deletion, refuse and ask them to delete one at a time after reviewing each. ## Pre-calls required 1. fetch_sweepstakes if the user gave you a sweepstakes name instead of a token ## Parameters to validate before calling - sweepstakes_token (string, required) — The unique identifier (token) of the sweepstakes to delete ## Notes - DESTRUCTIVE — IRREVERSIBLE. Always confirm with the user before calling. Explain what will be lost. - IRREVERSIBLE — destroys ALL associated data (participants, rules, winners, statistics) - ALWAYS require explicit user confirmation. Explain exactly what will be lost.
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  • Discover the most relevant verified datasets for a given topic. Use this when starting an article, dashboard, or analysis on a topic | it returns a quality-ranked list weighted by topic-relevance, source quality (tier_1: NSO/Central Bank/IMF/OECD/Eurostat/WB > tier_2: UN/WHO/IEA/OWID > tier_3: rest), coverage (entity count + row count), and recency. Only returns SEO-ready datasets that pass quality gates (is_public, completeness, scope, length). Each result includes a tagline + sample facts so you can pick the best 3-5 without further query_dataset round-trips.
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  • List the AI engine channels tracked by Peec. A model channel is a stable identifier for an AI engine (e.g. "openai-0" = ChatGPT UI) that persists even as the underlying model is upgraded — use it to filter or break down reports by engine without worrying about model version changes. Use this tool to resolve channel descriptions (e.g. "ChatGPT UI", "Perplexity") to channel IDs before filtering reports (model_channel_id filter), and to label channel IDs from report output before presenting results. The current_model_id column gives the model ID currently active in the channel — pass this as model_id where reports require it. is_active indicates whether the channel is enabled for this project — inactive channels return empty data. unsupported_country_codes lists country codes that cannot be used with this channel (chats requested for those countries are not created). Returns columnar JSON: {columns, rows, rowCount}. Columns: id, description, current_model_id, is_active, unsupported_country_codes.
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  • Safely evaluate mathematical expressions with support for basic operations and math functions. Supported operations: +, -, *, /, **, () Supported functions: sin, cos, tan, log, sqrt, abs, pow Note: Use this tool to evaluate a single mathematical expression. To compute descriptive statistics over a list of numbers, use the statistics tool instead. Examples: - "2 + 3 * 4" → 14 - "sqrt(16)" → 4.0 - "sin(3.14159/2)" → 1.0
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  • Get total clinical trial study count from ClinicalTrials.gov matching a query, without fetching study data. Fast and lightweight. Use for quick statistics or to build breakdowns by calling multiple times with different filters (e.g., count by phase, count by status, count recruiting vs completed for a condition).
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  • Use when benchmarking workforce planning against sector labor market conditions, assessing industry growth trajectory for strategic planning, providing economic context for board reporting, or evaluating talent acquisition timing for a specific industry. Returns BLS payroll employment by major sector with month-over-month change, year-over-year change, and trend classification from the official establishment survey covering 650,000 US worksites — the same data the Federal Reserve uses to assess labor market conditions. Example: Healthcare sector — 8.41M employed, +47K MoM, +3.2% YoY, EXPANDING for 14 consecutive months — persistent hiring demand supports above-market compensation benchmarks. Source: Bureau of Labor Statistics Current Employment Statistics.
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Matching MCP Servers

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    A model Context Protocol (MCP) server that provides comprehensive OECD statistics through the SDMX API, supporting AI assistants and chatbots to query OECD datasets in areas such as economy, health, education, and environment.
    Last updated
    9
    2
    Apache 2.0
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    Enables natural language search and discovery of open-access scientific datasets through the EOSC Data Commons OpenSearch service. Provides tools to search datasets and retrieve file metadata using LLM-assisted queries.
    Last updated
    14
    MIT

Matching MCP Connectors

  • OECD MCP — Organisation for Economic Co-operation and Development data

  • Search PubMed and summarize biomedical literature — designed for AI health agents.

  • Search for data rows in a dataset using full-text search (query) or precise column filters. Returns matching rows and a filtered view URL. Use to retrieve individual rows. Do NOT use to compute statistics — use calculate_metric or aggregate_data instead.
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  • AI-powered company analysis using semantic search over Nordic financial data. Orchestrates multiple searches internally and returns a synthesized narrative answer with source citations. Covers annual reports, quarterly reports, press releases and macroeconomic context for Nordic listed companies. Use this when you want a synthesized answer rather than raw search chunks. For raw data access, use search_filings or company_research instead. For a full due diligence report with AI-planned sections, use the Alfred MCP server: alfred.aidatanorge.no/mcp Args: company: Company name or ticker question: What you want to know about the company model: 'haiku' (default) or 'sonnet'
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  • Search for recalled products similar to your query. This tool searches DeepRecall's global product safety database using AI-powered multimodal matching. Provide a text description and/or product images to find similar recalled products. Use Cases: - Pre-purchase safety checks: Before buying, verify if similar products were recalled - Supplier vetting: Check if a supplier's products have safety issues - Marketplace compliance: Verify products against recall databases - Consumer protection: Identify potentially hazardous products Data Sources: - us_cpsc: US Consumer Product Safety Commission - us_fda: US Food and Drug Administration - safety_gate: EU Safety Gate (Europe) - uk_opss: UK Office for Product Safety & Standards - canada_recalls: Health Canada Recalls - oecd: OECD GlobalRecalls portal - rappel_conso: French Consumer Recalls - accc_recalls: Australian Competition and Consumer Commission Cost: 1 API credit per search Args: content_description: Text description of the product (e.g., "children's toy with small parts") image_urls: List of product image URLs for visual matching (1-10 images) filter_by_data_sources: Limit search to specific agencies (optional) top_k: Number of results (1-100, default: 10) model_name: Fusion model - fuse_max (recommended), fuse_flex, or fuse input_weights: Weights for [text, images], must sum to 1.0 api_key: Your DeepRecall API key (optional if provided via X-API-Key header) Returns: Search results with matched recalls, scores, and product details Example: search_recalls( content_description="baby crib with drop-side rails", top_k=5 )
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  • Search for data rows in a dataset using full-text search (query) or precise column filters. Returns matching rows and a filtered view URL. Use to retrieve individual rows. Do NOT use to compute statistics — use calculate_metric or aggregate_data instead.
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  • Lists Compute Engine virtual machine (VM) instances. Details for each instance include name, ID, status, machine type, creation timestamp, and attached guest accelerators. Use other tools to get more details about each instance. Requires project and zone as input.
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  • Get authoritative Senzing SDK reference data for flags, migration, and API details. Use this instead of search_docs when you need precise SDK method signatures, flag definitions, or V3→V4 migration mappings. Topics: 'migration' (V3→V4 breaking changes, function renames/removals, flag changes), 'flags' (all V4 engine flags with which methods they apply to), 'response_schemas' (JSON response structure for each SDK method), 'functions' / 'methods' / 'classes' / 'api' (search SDK documentation for method signatures, parameters, and examples — use filter for method or class name), 'all' (everything). Use 'filter' to narrow by method name, module name, or flag name
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  • Get current statistics for the ShippingRates shipping intelligence database. Use this as a starting point to understand what data is available before calling other tools. Returns record counts for D&D tariffs, local charges, transit schedules, freight rates, surcharges, ports, shipping lines, countries, and the last data refresh timestamp. FREE — no payment required. Returns: { tariff_records, ports, transit_schedules, freight_rates, local_charges, shipping_lines, countries, last_scrape (ISO datetime) } Related tools: Use shippingrates_lines for per-carrier breakdowns, shippingrates_search for keyword discovery.
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  • Fetch clinical trial results data from ClinicalTrials.gov for completed studies — outcome measures with statistics, adverse events, participant flow, and baseline characteristics. Only available for studies where hasResults is true. Use clinicaltrials_search_studies first to find studies with results.
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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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  • Report when a tool result was unhelpful, incomplete, or wrong. Call this whenever you override a recommendation, skip a cart result, or notice the engine output doesn't match what the user needs. Do not use proactively — only when you observe an actual issue. This helps improve the engine.
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  • Search for local businesses. Returns name, category, city, country, logo_url, available_details (what data exists), and scores. Check available_details to see what is available, then call get_business for full details.
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  • Search Vaadin documentation for relevant information about Vaadin development, components, and best practices. Uses hybrid semantic + keyword search. USE THIS TOOL for questions about: Vaadin components (Button, Grid, Dialog, etc.), TestBench, UI testing, unit testing, integration testing, @BrowserCallable, Binder, DataProvider, validation, styling, theming, security, Push, Collaboration Engine, PWA, production builds, Docker, deployment, performance, and any Vaadin-specific topics. When using this tool, try to deduce the correct development model from context: use "java" for Java-based views, "react" for React-based views, or "common" for both. Use get_full_document with file_paths containing the result's file_path when you need complete context.
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  • Get real-time ENTIA platform statistics: total registered entities, country coverage, active data sources, and published Entia Homes. Note: total_entities is the full registry; only ~79K pass the Quality Gate for full publication. Cached 1h server-side. No API key required.
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