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

• create_campaign

  xmagnet

  Create a DRAFT email campaign via a programmatic wizard. Call this tool and it will guide through the steps — no manual orchestration needed. WIZARD STEPS (handled automatically by the tool): 1. Call with contacts + total_contacts → tool returns engine picker (NextGen vs MyConvo) 2. Add campaign_type from user's click → tool returns campaign category chips (promotional, newsletter, event…) 3. Add campaign_category from user's click → tool returns engine-specific template gallery MyConvo: shows plain_email_templates (personal plain-text). NextGen: shows campaign_templates (HTML). 4. Add template_id from user's pick → tool creates the draft campaign. RULES: Reuse contacts from prior search — never re-search. Pass total_contacts from search result's total_in_crm so the user always sees the full count. Saves as DRAFT only — no emails sent.

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• list_curated_flows

  Oecd

  List OECD dataflow refs we have pre-vetted, grouped by topic (gdp, labour, prices, finance, households, health, demographics, projections, tax, education, environment, technology). Pass the flow_ref to fetch_dataset. For everything else use search_dataflows or browse https://data-explorer.oecd.org.

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• find_alpha

  Name Whisper — ENS Intelligence Layer

  Scan the ENS marketplace for alpha — names listed below their valuation. Returns ranked opportunities with a discount %, fair-value range, confidence rating, and comparable data. Candidates are selected by DESIRABILITY (real curated collections, short, accessibly priced above a floor that excludes 0.001-ETH floor-dumps), then each is precision-priced by the full Name Whisper valuation engine — the SAME engine behind get_valuation and the Value page — which is the sole judge of undervaluation. The returned fair-value range (estimatedValueEth), confidence and discountPct are the engine's own numbers, via the same cache-first path as get_valuation (with display-only signals disabled for speed), so they are authoritative and consistent with get_valuation. They are computed conservatively (the seller-wallet boost is off), so if anything they slightly UNDERSTATE fair value — report them as-is; do NOT inflate the fair value or upgrade the confidence. Use estimatedValueEth.mid as the fair-value anchor. Only opportunities the engine confirms are surfaced: a believable discount band (20%+, capped where valuations stop being reliable), MEDIUM+ confidence, and a REAL comparable-sale match (type/collection/word/entity/semantic — never a coarse same-length average). This means genuinely good, believable deals (typically 25–65% off) — not 99%-off junk. It will still surface a large discount when the engine confirms it with real comps; it just won't fabricate one. **Use this instead of search_ens_names + repeated get_valuation when the user asks for "best value", "best buy", "cheapest good name", "undervalued", "bargains", or any ranked-by-value query across multiple listings.** find_alpha does the search + engine valuation + ranking in a single call — you do NOT need to call get_valuation again on its results. If it returns fewer names than asked, the rest weren't genuine discounts vs the engine — say so rather than padding the list. Supports filters (minLength, maxLength, maxPriceEth, charType) so narrow queries like "4-letter names under 1 ETH, best value" are one call, not six. It has NO collection/category/club param. Do NOT use it for "floor price of the 999 club", "cheapest 10k-club names", or "floor of <collection>" — those name a specific collection, so use search_ens_names (which returns that collection's real listings sorted by price), or sweep if the user wants to buy the cheapest N. find_alpha is for value-ranked discovery across the market, not a named collection's floor.

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• discover_by_topic

  Autario Data Analytics Platform

  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_providers

  Dbnomics

  List all statistics providers available in DBnomics (80+), including provider codes (e.g. ECB, BLS, EUROSTAT, IMF, OECD) and their names. Use provider codes with list_datasets and get_series.

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• benchmark

  XFMS — Xpansion Framework Model Source

  Run a live A/B test against the engine's TOP 3 PICKS for a stated purpose — the engine chooses the candidates from the full catalog. Generates 5 representative test queries (auto-expands to 10 or 15 if results are too close to call), runs them through the picked models in parallel, and returns real cost, latency, and plain-English commentary on who won what. Use AFTER `pick` or `rank` when the user wants the engine's own picks stress-tested with live data. DO NOT use this when the user has already named specific candidate models — the engine will ignore the names and test its own picks. Use `compare` instead in that case. Costs more than `rank` (15+ live LLM calls).

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• web_search_multilang

  gapup-mcp

  Multi-language, multi-source web search that goes beyond Anglo-centric results. Supports 15 languages (fr/de/es/it/pt/nl/ja/zh/ko/ar/ru/sv/pl/tr/en) with automatic detection. Aggregates results from Mojeek (independent search engine, multilang) and Wikipedia (native multilang API), with DDG and HN as English-language complements. Returns deduplicated results ranked by cross-engine consensus. Use when you need non-English search results, when DDG fails, or for geographically-biased queries. Phase 2 #7 of the geo/lang expansion plan. Note: Brave/Bing/Searx are blocked from DO IPs — configure AICI_RESEARCH_PROXY_URL for residential proxy.

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• interslavic_get_inflections

  hub

  Fetch the full declension (noun/adjective) or conjugation (verb) table for a word identified by a search result's inflection handle (entry_id + word_class). Use this only when a search ran without inline forms or left a match un-expanded — a default search already returns each match's table inline. Returns Markdown plus the table as structuredContent with the shape {"result": <paradigm>} — switch on result.category ('noun' | 'adjective' | 'verb' | 'not_found') before reading the body. Tables are generated by the official Interslavic morphology engine, not attested.

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• benchmark

  XFMS — Model Source

  Run a live A/B test against the engine's TOP 3 PICKS for a stated purpose — the engine chooses the candidates from the full catalog. Generates 5 representative test queries (auto-expands to 10 or 15 if results are too close to call), runs them through the picked models in parallel, and returns real cost, latency, and plain-English commentary on who won what. Use AFTER `pick` or `rank` when the user wants the engine's own picks stress-tested with live data. DO NOT use this when the user has already named specific candidate models — the engine will ignore the names and test its own picks. Use `compare` instead in that case. Costs more than `rank` (15+ live LLM calls).

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• get_catalog

  reefapi-mcp

  The FULL ReefAPI catalog — EVERY engine with its one-line title, grouped by category. This is the whole menu (≈ a few thousand tokens); SCAN IT AND PICK THE BEST ENGINE YOURSELF. You are an LLM, so you match the user's intent semantically — across ANY language, typo, or phrasing — far better than a keyword search can. Use this whenever search_engines didn't surface the right engine (or to be sure you didn't miss a better one). After you pick: get_engine_schema(engine) -> get_action_schema -> call_engine.

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• web_search_multilang

  gapup-mcp

  Multi-language, multi-source web search that goes beyond Anglo-centric results. Supports 15 languages (fr/de/es/it/pt/nl/ja/zh/ko/ar/ru/sv/pl/tr/en) with automatic detection. Aggregates results from Mojeek (independent search engine, multilang) and Wikipedia (native multilang API), with DDG and HN as English-language complements. Returns deduplicated results ranked by cross-engine consensus. Use when you need non-English search results, when DDG fails, or for geographically-biased queries. Phase 2 #7 of the geo/lang expansion plan. Note: Brave/Bing/Searx are blocked from DO IPs — configure AICI_RESEARCH_PROXY_URL for residential proxy.

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• faa_get_aircraft_type

  faa-aircraft-registry-mcp-server

  Decode a 7-character FAA manufacturer/model/series code to aircraft specifications from the reference table — manufacturer, model, aircraft category, aircraft type, engine type, number of engines, number of seats, weight class, cruise speed, and type-certificate data sheet/holder. Use faa_search_aircraft_types first to discover a code by manufacturer or model name.

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• search_indexed_documents

  RChilli MCP Hub

  Keyword-search the user's ALREADY-INDEXED corpus of resumes or JDs and return matching documents (RChilli Search Engine). Requires documents to have been indexed beforehand. Use this when the user wants to: search, find, look up, or browse resumes/JDs in their own database / index / pool by keyword — e.g. "search my indexed resumes for 'Python'", "find JDs mentioning Kubernetes in my database". Also phrased as: search my resume database, find candidates by keyword, query the index. Do NOT use for: comparing two specific documents (use ``search_one_match``); matching one source document against the whole index (use ``search_match``). Args: keyword: Search keyword. indextype: Index type to search — ``Resume`` (default) or ``JD``. userkey: RChilli userkey. Leave blank to use the authenticated session key. subuserid: Sub-user identifier for multi-tenant isolation.

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• iliad_web_search

  AXIS Toolbox — Agentic Commerce Codebase Intelligence

  AXIS-owned BM25 search engine over the corpus YOUR account has indexed. NOT a Google/Bing scraper — agents build their own searchable index by first calling operation='index' with documents (often pages fetched via iliad_web_research), then querying with operation='search'. Five operations: `index` (insert one or many documents), `search` (BM25 top-k ranked hits with snippet + score + metadata), `delete` (drop one doc), `delete_namespace` (drop all), `count`. Namespaces are account-scoped server-side (`acct:<id>:<namespace>`). Persistent across restarts via SQLite. Search supports `max_results` (default 10, max 100) and `site` (restrict to a single URL host, case-insensitive). Engineer mode (X-Agent-Mode: engineer — Answer Engine, $0.25): search also returns a grounded extractive answer with [n] citation spans over your corpus, reranked, refusing on weak evidence. Requires Authorization: Bearer <api_key>.

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• get_engine_schema

  reefapi-mcp

  COMPACT overview of ONE engine: every action with its description, required params and what it returns — but NOT the full param detail (kept lean so a 90-action engine stays token-cheap). Call this after search_engines to pick the right ACTION, then get_action_schema(engine, action) for that action's full params before call_engine.

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• call_engine

  reefapi-mcp

  Call a ReefAPI engine action — POST /<engine>/v1/<action> with `params`. Returns the uniform { ok, data, meta, error } envelope. Get param names from get_engine_schema first. Needs YOUR ReefAPI key (the local server reads REEFAPI_KEY; the hosted server reads the `Authorization: Bearer ak_live_...` header you configure on the connection). Get a key at https://reefapi.com. Failed calls cost no credits.

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• decker.validate_intent

  Decker

  Pre-trade gate check for a proposed order intent. Call this BEFORE placing any order through any execution tool (e.g. a broker MCP's review→place flow). Checks the intent (symbol + side) against Decker's deterministic market state: engine action_gate (GO/WATCH/HOLD — a transition posture, not an order command), current structural state, and the active signal's direction / invalidation (stop) coordinates. Returns a stance reading, NOT an approval or rejection: the vocabulary is the engine gate as-is plus a mechanical side_alignment (aligned/opposed vs the active signal's direction). covered=false means the engine does not emit state for this symbol — treat as unknown, not as HOLD. The order decision and responsibility remain with the calling agent/user. Every check is persisted to an auditable decision ledger (check_id).

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• search_recalls

  DeepRecall - Product Safety Intelligence

  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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• fetch_dataset

  Oecd

  Fetch tidy rows from any OECD dataflow. flow_ref examples: "OECD.SDD.NAD,DSD_NAMAIN1@DF_QNA_EXPENDITURE_GROWTH,1.0". The key string is a dot-separated dimension filter (e.g., "USA.....Q" — leave empty to fetch everything). Use start/end periods like "2020-Q1" or "2020". Returns labeled rows; OECD enforces a result-size limit and may truncate broad queries — narrow with key dimensions or shorter time ranges.

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• search_rentals_natural

  arcasos-rentals

  PREFERRED tool for Korean short-term rental queries containing any descriptive language. ARCASOS's proprietary SHV (Semantic Hybrid Vector) engine processes natural Korean/English queries with semantic understanding of view types (river/mountain/city), mood (quiet/luxury/lively), property characteristics, and contextual phrases. Pass the user's natural language query AS-IS — do NOT extract slots. Returns semantically pre-ranked results in Schema.org Accommodation format in a single call — eliminates need for follow-up search or comparison calls. Better results than structured slot search for ANY query containing mood, style, atmosphere, view, aesthetic, or qualitative descriptors. Use this to minimize token usage and latency.

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