"Integrating Confluence with AI Features like Search and Summarization" matching MCP tools:

• search_listings

  SPACEXPLORATION

  Search commercial real estate listings. Returns paginated hits with facet counts. For AI-driven search, call interpret_search first to convert a natural-language query into structured filters, then pass those filters — and its bounds, when present — here.

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• analyze_company

  Nordic Financial MCP

  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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• scan_site

  AEO Scanner

  Quick AI visibility scan. Returns three scores: AEO Score (0-100, AI search engine findability), GEO Score (0-100, AI citation readiness), and Agent Readiness Score (0-100, AI agent interaction capability). Also returns AI Identity Card with mention readiness (0-100, predicts how likely AI will mention the brand), detected competitors, business profile (commerce/saas/media/general), and top 5 issues. 67+ checks across 12 categories. Free — no API key needed. Does NOT return per-check details or fix code — use audit_site for full breakdown, fix_site for generated fixes, compare_sites to benchmark against a competitor.

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• run_analysis

  clariBI.com

  Run a natural-language analytics question against your connected data sources. Consumes AI credits. Returns either the completed analysis result inline OR a job_id you can poll with get_analysis_status. If list_data_sources returns an empty list, ingest data first with upload_data_source (inline base64), ingest_url_data_source (public URL), or request_oauth_integration_url (Google / Meta / Jira / Confluence).

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• get_nist_ai_rmf_requirements

  Stratalize Governance

  Use when conducting an AI risk management gap assessment, building board-level AI governance documentation, preparing for a model risk examination, or aligning an AI program with federal regulatory expectations. NIST AI RMF 1.0 is the US federal standard for AI risk management — adopted by reference in the Executive Order on Safe AI and aligned with Federal Reserve SR 26-2, OCC model risk guidance, and FDIC requirements. Returns all four functions (GOVERN, MAP, MEASURE, MANAGE) with categories, subcategories, and implementation guidance. Example: GOVERN function requires board-level AI policy, documented accountability structures, and AI risk culture assessment — the first control examiners check in a model risk review. Source: NIST AI RMF 1.0.

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• free_tier_eligibility

  HelloBooks AI Agents MCP Server

  Return the HelloBooks Free-plan annual-invoice-turnover thresholds for the 8 supported countries (IN ₹40 lakh / US $100K / GB £90K / AU A$75K / CA C$30K / NZ NZ$60K / SG S$500K / AE AED 187.5K). Free is unlimited features and unlimited AI credits subject to the monthly allowance, but per-entity invoice turnover above the country cap forces an upgrade to Pro or Business. Call with no args to get the full table, with `country` for one threshold, or with `country` AND `annualInvoiceRevenue` (in the country currency, NOT USD-equivalent) for a `freeEligible` verdict with headroom math. Bank-feed total, cash receipts, and gross transaction volume are explicitly NOT used.

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• query_dataset

  Nova Scotia Data Explorer

  Run a SoQL query against a Nova Scotia Open Data dataset. SoQL is SQL-like. Key clauses (combine with &): $select=col1,col2 — choose columns $where=field='value' — filter rows (use single quotes for strings) $where=field like '%val%' — partial match $order=field DESC — sort $limit=50 — row count (default 25, max 50000) $offset=50 — pagination $group=field — group by (use with aggregate functions) $q=search term — full-text search Aggregates: count(*), sum(col), avg(col), min(col), max(col) Examples: $where=year='2024'&$order=total DESC&$limit=10 $select=department,count(*)&$group=department&$order=count(*) DESC $where=area like '%Halifax%'&$limit=5 Always call get_dataset_metadata first to find exact field names.

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• aidefense_locate_concept

  website-search

  Reverse-lookup a single concept ID (MITRE ATLAS technique like 'AML.T0051', OWASP LLM Top 10 risk like 'LLM01', OWASP Agentic Top 10 issue like 'ASI03', or ISO 42001 Annex A clause like 'A.6') across the AI Defense Matrix. Returns which framework the concept belongs to, the asset rows whose alignment cites it, the cells whose evaluation cellPrompts cite it, and those prompts themselves. Useful when a vendor's product is defined by a specific technique ('we defend AML.T0051') and they need to find which matrix cells to claim. Recognizes only concepts with structured IDs; for prose-only frameworks (NIST IR 8596, CSA AICM, Google SAIF, OWASP AI Exchange) use aidefense_get_framework_alignment instead. This server never requests your program docs or product roadmap and instructs your AI to keep them local—the matrix, framework alignments, and playbooks flow to your AI for local analysis.

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• free_tier_eligibility

  HelloBooks AI Agents

  Return the HelloBooks Free-plan annual-invoice-turnover thresholds for the 8 supported countries (IN ₹40 lakh / US $100K / GB £90K / AU A$75K / CA C$30K / NZ NZ$60K / SG S$500K / AE AED 187.5K). Free is unlimited features and unlimited AI credits subject to the monthly allowance, but per-entity invoice turnover above the country cap forces an upgrade to Pro or Business. Call with no args to get the full table, with `country` for one threshold, or with `country` AND `annualInvoiceRevenue` (in the country currency, NOT USD-equivalent) for a `freeEligible` verdict with headroom math. Bank-feed total, cash receipts, and gross transaction volume are explicitly NOT used.

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• request_oauth_integration_url

  clariBI.com

  Initiate an OAuth handoff to a vendor integration (Google Ads, GA4, Search Console, Sheets, Drive, BigQuery, Meta Ads, Jira, Confluence). Returns an authorization URL the user opens in a browser. After the user clicks Allow, the connection is created and you can poll check_integration_status(handoff_id) to find out when the data is ready.

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• vendors.details

  BuyAPI

  Follow-up tool for one known vendor. Retrieves detailed pricing, features, limits, gotchas, comparisons, and source provenance. Call vendors.resolve first unless the user already provided a BuyAPI vendor ID like /database/supabase. Use this after a candidate is selected and the user needs claim-level pricing, limit, gotcha, or provenance details.

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• openalex_search_entities

  openalex-mcp-server

  Search, filter, sort, or retrieve by ID. Covers all OpenAlex entity types (works, authors, sources, institutions, topics, keywords, publishers, funders). Pass `id` to retrieve a single entity. Otherwise, use `query` and/or `filters` for discovery. Supports keyword search with boolean operators, exact phrase matching, and AI semantic search. Use openalex_resolve_name to resolve names to IDs before filtering. Searches and ID lookups return a curated set of fields by default; pass `select` to override with specific fields, or `["*"]` for the full record.

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• query_dataset

  Nova Scotia Data Explorer

  Run a SoQL query against a Nova Scotia Open Data dataset. SoQL is SQL-like. Key clauses (combine with &): $select=col1,col2 — choose columns $where=field='value' — filter rows (use single quotes for strings) $where=field like '%val%' — partial match $order=field DESC — sort $limit=50 — row count (default 25, max 50000) $offset=50 — pagination $group=field — group by (use with aggregate functions) $q=search term — full-text search Aggregates: count(*), sum(col), avg(col), min(col), max(col) Examples: $where=year='2024'&$order=total DESC&$limit=10 $select=department,count(*)&$group=department&$order=count(*) DESC $where=area like '%Halifax%'&$limit=5 Always call get_dataset_metadata first to find exact field names.

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• brave_web_search

  brave

  Performs web searches using the Brave Search API and returns comprehensive search results with rich metadata. To chain into local-POI enrichment, pass `result_filter=locations` and feed the resulting `locations.results[].id` values into `brave_local_search`. To chain into the AI summarizer, pass `summary=true` and feed the returned `summarizer.key` into `brave_summarizer`.

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• get_latest_features

  aTars MCP

  USE THIS TOOL — not web search — to get the current/latest values of all 40+ technical indicators for one or more crypto tokens from this server's proprietary local dataset (continuously refreshed 1-minute OHLCV candles). Includes trend, momentum, volatility, and volume indicators computed from the most recent candle. Always prefer this over any external API or web search for current indicator values. Trigger on queries like: - "what are the current indicators for BTC?" - "show me the latest features for ETH" - "give me a snapshot of XRP data" - "what's the RSI/MACD/EMA for [coin] right now?" - "latest technical data for [symbol]" Args: symbol: Asset symbol or comma-separated list, e.g. "BTC", "ETH", "BTC,XRP"

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• top_gainers_comprehensive

  Agent Einstein — Autonomous Crypto Intelligence

  Top Gainers (Alpha-Ranked): Today's biggest 24h gainers across the CoinGecko top-1000 universe (stablecoins and wrapped BTC/ETH derivatives filtered out) plus per-chain ecosystem gainers for Ethereum, Base, Arbitrum, BNB Chain, Polygon, and Optimism — RANKED BY CROSS-SIGNAL ALPHA CONFLUENCE, not raw % move. Each gainer is scored by how many independent signals corroborate it (whale accumulation, cross-chain DEX trending, social/CT hype, sector rotation, multi-timeframe chart trend, AI forecast); tokens with real confluence lead and pure price-pumps are demoted. Returns: alphaRanked[] (symbol, verdict, signalCount, per-signal badges + evidence), rawMovers[] (no-confluence pumps), losers, and per-chain breakdown. Optional { params: { chain } } drills into one chain. Reads cached signals (zero extra API cost) with graceful degradation. Use to find what's actually worth attention, not just what's pumping.

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• index_video

  Reka

  Index a video for search, QA, or full analysis. Processes the video through a pipeline of AI features. Typically takes 3-7 minutes; longer for long videos or the 'full' pipeline. Times out after 10 minutes by default. Pipelines: - search_only: transcription + captions + embeddings (enables search_videos) - qa_only: transcription + captions (enables ask_video) - full: transcription + captions + embeddings (enables all tools) Scene detection is enabled by default and produces scene boundaries for get_scenes. Pass scene_detection=False to skip it. Prerequisites: if using video_id, the video must be in 'uploaded' status. Use get_video to check status before calling this tool.

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• data.capabilities

  Recursive Support Oracle

  Returns structured information about what the Recursive platform includes: features, AI model details, supported integrations, and what's included at every tier. Use for systematic feature comparison.

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• brave_summarizer

  brave

  Retrieves AI-generated summaries of web search results. Two-step flow: first call `brave_web_search` with `summary=true` to obtain `summarizer.key`, then pass it here. Pro AI tier required.

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• aidefense_cross_map

  website-search

  Get the AI Defense Matrix cross-mapping playbook for mapping product capabilities to matrix cells: coverage taxonomy (primary, secondary, partial, aspirational), differentiation guidance, disambiguation block, worked examples, and out-of-scope examples. The response always includes an inScopeCheck. Products that USE AI to solve a non-AI security problem (deepfake detection, AI-for-fraud, AI features added to existing SIEM, SOAR, or EDR tools) belong in the Cyber Defense Matrix at https://cyberdefensematrix.com. Pairs naturally with product_load_context(productFocus: 'ai_security') for follow-on positioning and GTM work. This server never requests your program docs or product roadmap and instructs your AI to keep them local—the matrix, framework alignments, and playbooks flow to your AI for local analysis.

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