"An open-source vector database for similarity search and AI applications" matching MCP tools:

• get_adoption_stage

  Stratalize Finance Intelligence

  Public mode returns FS AI RMF framework reference data only — not org-specific scoring. Use when assessing an organization FS AI RMF governance maturity stage or preparing a regulatory AI roadmap presentation. Returns INITIAL, MINIMAL, EVOLVING, or EMBEDDED classification with stage criteria and remediation priorities. Example: EVOLVING stage organizations have documented AI policies but lack systematic model validation — typical gap to EMBEDDED is 18-24 months and 12-15 additional controls. Connect org MCP for org-specific scoring. Source: FS AI Risk Management Framework.

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• search_regulations

  Velvoite — EU Financial Regulatory Compliance

  Search the regulatory corpus using keyword / trigram matching. Uses PostgreSQL trigram similarity on document titles and summaries. Returns documents ranked by relevance with summaries and classification tags. Prefer list_documents with filters (regulation, entity_type, source) first. Only use this for free-text keyword search when structured filters aren't sufficient. Args: query: Search terms (e.g. 'strong customer authentication', 'ICT risk', 'AML reporting'). per_page: Number of results (default 20, max 100).

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• ai_filters_list

  DialogBrain

  List all AI filters for the current workspace. AI filters are semantic intent-based message filters that use embeddings (vector representations) to detect whether an incoming message matches a specific intent or topic. Unlike keyword filters, they understand meaning: 'I need help with my order' and 'my package hasn't arrived' both match a 'shipping support' filter even without shared keywords. Each filter stores a reference embedding of its description. When a message arrives, its embedding is compared via cosine similarity against the filter's reference vector. If the similarity exceeds the threshold, the filter matches. When to use: - Check which semantic filters already exist before creating a new one - Get filter IDs for use in trigger conditions - Review thresholds and active status of existing filters Returns all filters with id, name, description, threshold, and is_active.

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• get_adoption_stage

  Stratalize AI Governance Intelligence

  Public mode returns FS AI RMF framework reference data only — not org-specific scoring. Use when assessing an organization FS AI RMF governance maturity stage or preparing a regulatory AI roadmap presentation. Returns INITIAL, MINIMAL, EVOLVING, or EMBEDDED classification with stage criteria and remediation priorities. Example: EVOLVING stage organizations have documented AI policies but lack systematic model validation — typical gap to EMBEDDED is 18-24 months and 12-15 additional controls. Connect org MCP for org-specific scoring. Source: FS AI Risk Management Framework.

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• run_code

  Hatchable

  Execute arbitrary JS in the project's isolate runtime. The SDK is pre-imported into local scope — `db`, `auth`, `email`, `storage`, `ai`, `agent`, `cache`, `vector`, `memory`, `tasks`, `scheduler`, `browser`, `images`, `run`, `approval`, `mcp` are ready to use without import. `process.env` and global `fetch` also work. `return` to produce the `result` field. Top-level `import` and dynamic `import('hatchable')` are NOT supported in this REPL — the bindings above are how you reach the SDK. Use this as a REPL: probe the database, verify a computation, test an API shape before committing it to a file. Nothing is persisted — the snippet runs once and disappears. Caps: 5s default timeout (max 30s), 256 KB max source length. Example: run_code({ project_id, code: ` const { rows } = await db.query("SELECT count(*) FROM users"); return rows[0]; `})

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• get_adoption_stage

  Stratalize Crypto and DeFi Intelligence

  Public mode returns FS AI RMF framework reference data only — not org-specific scoring. Use when assessing an organization FS AI RMF governance maturity stage or preparing a regulatory AI roadmap presentation. Returns INITIAL, MINIMAL, EVOLVING, or EMBEDDED classification with stage criteria and remediation priorities. Example: EVOLVING stage organizations have documented AI policies but lack systematic model validation — typical gap to EMBEDDED is 18-24 months and 12-15 additional controls. Connect org MCP for org-specific scoring. Source: FS AI Risk Management Framework.

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• semantic_search

  Zoning Signal

  Semantic search across the full corpus — every place dossier, corridor signal, meeting reading, and named-pattern brief. Returns results ranked by cosine similarity in a 1024-dimensional embedding space (Voyage AI 4 + Supabase pgvector). Use when the agent does not know the canonical entity slug or named-pattern title in advance — the search returns the readings whose semantic structure best matches the natural-language query, with type, title, similarity, and resolved URL per hit. Threshold 0.55, top 12.

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• get_colorado_ai_act_requirements

  Stratalize Finance Intelligence

  Use when building an AI governance compliance roadmap, advising on high-risk AI deployment obligations in Colorado, or briefing boards on upcoming US state AI regulatory requirements. Colorado SB 205 takes effect June 30, 2026 — the first comprehensive US state AI law. Returns developer and deployer obligations, high-risk AI system criteria, consumer rights, penalty structure ($20,000 per violation, AG enforcement), and comparison to EU AI Act. Example: AI-based loan underwriting system deployed in Colorado requires algorithmic impact assessment, plain-language consumer disclosure before first use, 3-year audit trail with AG access rights, and annual compliance certification — noncompliance triggers $20,000 per violation. Source: Colorado SB 205, enacted May 17, 2024.

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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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• search_icons

  Supericons

  Search 20,000+ free icons across 10 libraries by meaning, label, visual description, tags, and synonyms. Use this when the user describes an icon concept such as "database", "user profile", "chill", "security", or "AI model". Returns matching icons with SVG code and public semantic guidance.

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• planning_search

  UK Property Data

  Find the planning portal URL for a UK postcode. Returns council info and portal search URLs. Does not scrape planning applications -- use the returned URLs to search directly.

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• get_adoption_stage

  Stratalize Real Estate Intelligence

  Public mode returns FS AI RMF framework reference data only — not org-specific scoring. Use when assessing an organization FS AI RMF governance maturity stage or preparing a regulatory AI roadmap presentation. Returns INITIAL, MINIMAL, EVOLVING, or EMBEDDED classification with stage criteria and remediation priorities. Example: EVOLVING stage organizations have documented AI policies but lack systematic model validation — typical gap to EMBEDDED is 18-24 months and 12-15 additional controls. Connect org MCP for org-specific scoring. Source: FS AI Risk Management Framework.

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• originators_like

  Quotewise

  Find originators similar to the given one using vector similarity (quote themes). Use after finding an author to discover related thinkers. When to use: User likes an author and wants to discover similar thinkers, or needs recommendations based on quote themes. Returns originators with similarity scores (0-100%). Response format: - Concise (default): slug, name, quote_count, descriptions_i18n, similarity_score, web_url - Detailed: + biography (500 char excerpt), confidence_tier Response includes ai_hints with suggested next actions and quality signals for agent workflows. Examples: - `originators_like(originator="Marcus Aurelius")` - similar philosophers - `originators_like(originator="Oscar Wilde")` - similar wits - `originators_like(originator="African Proverbs")` - similar proverb collections

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• tascan_list_issues

  TaScan

  List all issues for a task list (event). Returns open, acknowledged, and resolved issues with severity, type, and category. Use this to discover issues that need AI analysis via tascan_analyze_issue.

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• get_colorado_ai_act_requirements

  Stratalize Crypto and DeFi Intelligence

  Use when building an AI governance compliance roadmap, advising on high-risk AI deployment obligations in Colorado, or briefing boards on upcoming US state AI regulatory requirements. Colorado SB 205 takes effect June 30, 2026 — the first comprehensive US state AI law. Returns developer and deployer obligations, high-risk AI system criteria, consumer rights, penalty structure ($20,000 per violation, AG enforcement), and comparison to EU AI Act. Example: AI-based loan underwriting system deployed in Colorado requires algorithmic impact assessment, plain-language consumer disclosure before first use, 3-year audit trail with AG access rights, and annual compliance certification — noncompliance triggers $20,000 per violation. Source: Colorado SB 205, enacted May 17, 2024.

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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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• get_market_read

  kr-crypto-intelligence

  AI-powered Korean crypto market analysis. Combines Kimchi Premium, stablecoin premium, FX rate, Upbit/Bithumb volume rankings, Binance funding rate, open interest, BTC dominance, and Fear & Greed index. Returns AI-generated signal (BULLISH/BEARISH/NEUTRAL), confidence score, actionable summary, and all raw data. Price: $0.10 via x402.

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• currency_convert_open

  Inferventis MCP Server

  Converts a monetary amount between two fiat currencies using live exchange rates from an open currency exchange API. Returns the converted amount and the rate applied. Use currency_convert_open as an alternative live-rate source when currency_convert (Frankfurter/ECB) or currency_fx_lite are unavailable or rate-limited. The underlying source is an open public exchange rate feed suitable for informational use. Prefer currency_convert or currency_rates when ECB-auditable Frankfurter rates are required for accounting or compliance. Prefer currency_convert_lite for the same minimal output (amount + rate) backed by ECB/Frankfurter rates. Prefer currency_fx_lite for lightweight mid-market conversions. Does not support cryptocurrency pairs — use crypto_fx_rates for any conversion involving a digital asset.

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• run_ai_report

  xmagnet

  Run an AI-powered analytics report on any CRM data. Ask any question in plain English — contacts, campaigns, deals, credits, bounces, or unsubscribes. Returns a data table with numbers. Use for: 'Show contacts by industry', 'Top campaigns by open rate', 'Deal pipeline value by stage', 'Credit usage this month', 'Bounce rate by domain', 'Contacts added this week', 'Campaign performance comparison', 'Sequence step funnel', 'Win rate by deal source'.

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• ceevee_chat

  CareerProof MCP

  Send a message to CeeVee AI assistant for CV optimization guidance (2 credits). Requires a cv_version_id (use ceevee_upload_cv or ceevee_list_versions to get one). Returns AI response with optional edit suggestions, source citations, and a conversation_id. Omit conversation_id to start a new conversation; include it to continue a thread.

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