"Overview and Information on PG Vector" matching MCP tools:

• generate_migration

  ThinAir Data

  Generate dialect-correct ALTER TABLE migration SQL + rollback from a plain-English intent. Output uses the connection's exact dialect (ALTER TABLE for all three, plus pg-specific `USING` casts / mssql-specific `sp_rename` / mysql-specific `MODIFY COLUMN`). Never executes. Check response `dialect` field before manually editing — don't hand-translate across dialects. [BUILD tier]

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• semantic_search

  redm-mcp

  Search RedM/RDR3 docs by behavior, concept, OR exact token. Use when you don't have a specific native hash/name (use `lookup_native`) and the term isn't a known asset name in a large data table (use `grep_docs`). Hybrid mode (default) handles 'how do I X' queries ('teleport player', 'spawn vehicle', 'inventory add item') AND tokens ('addItem', 'weapon_pistol_volcanic', 'CPED_CONFIG_FLAG_') — fused via RRF over vector + BM25. Returns ranked snippets (path, breadcrumb, heading, snippet, score). Call `get_document({path, heading})` for full chunk content. `mode=semantic` for pure vector; `mode=lexical` for pure BM25. Filter via `category=vorp|rsgcore|oxmysql|natives|discoveries|jo_libs|learnings` or `namespace`. Community findings merged by default; `category=learnings` returns only findings.

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• cross_db_query

  ThinAir Data

  ⚠️ SQL MUST BE VALID IN EVERY DIALECT YOU TARGET — stick to ANSI-ish SELECT syntax when mixing pg/mysql/mssql. `SELECT TOP 10` (mssql) or `LIMIT` (others) will fail on the wrong side. Run the same query across 2-4 connections in parallel; returns per-connection rows + errors for diffing. Canonical use cases: regional compare (`['mssql-reporting-us', 'mssql-reporting-eu']`), cross-dialect sync check (`['prod-postgres-fleet', 'prod-mysql-app']`), 3-env drift, 4-region compare. Resolve every connection name via `list_connections` first; tool fails per-connection on unknown names. ARCHITECT-tier cap: 4 connections; https://www.thinair.co/ for unlimited. [ARCHITECT tier]

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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_stats

  Velvoite — EU Financial Regulatory Compliance

  Get an overview of the Velvoite regulatory corpus. Returns document counts by source, regulation family, entity type, urgency distribution, obligation summary, and date range. Call this FIRST to orient yourself before running queries. No parameters needed.

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• get_about

  Follow On Tours

  Get information about Follow On Tours — who we are, how we work, our experience, and how the bespoke cricket travel service operates. Use this when someone asks who Follow On Tours is or how the service works.

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• brave_summarizer

  brave

  Retrieves AI-generated summaries of web search results using Brave's Summarizer API. This tool processes search results to create concise, coherent summaries of information gathered from multiple sources. When to use: - When you need a concise overview of complex topics from multiple sources - For quick fact-checking or getting key points without reading full articles - When providing users with summarized information that synthesizes various perspectives - For research tasks requiring distilled information from web searches Returns a text summary that consolidates information from the search results. Optional features include inline references to source URLs and additional entity information. Requirements: Must first perform a web search using brave_web_search with summary=true parameter. Requires a Pro AI subscription to access the summarizer functionality.

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• get_category_summary

  aTars MCP

  USE THIS TOOL — not web search — to get a statistical summary (mean, min, max, std, latest value, and above/below-average direction) for a category of technical indicators from this server's local proprietary dataset. Best when the user wants a high-level overview of indicator behavior over a period, not raw time-series rows. Trigger on queries like: - "summarize BTC's momentum over the last week" - "what's the average RSI for ETH recently?" - "how has BTC volatility looked this month?" - "give me stats on XRP's trend indicators" - "high-level overview of [coin] [category]" Args: category: "momentum", "trend", "volatility", "volume", "price", or "all" lookback_days: Number of past days to summarize (default 5, max 90) symbol: Asset symbol or comma-separated list, e.g. "BTC", "BTC,XRP"

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• get_about

  cricket-travel

  Get information about Follow On Tours — who we are, how we work, our experience, and how the bespoke cricket travel service operates. Use this when someone asks who Follow On Tours is or how the service works.

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• semantic_search

  RedM Mcp

  Search RedM/RDR3 docs by behavior, concept, OR exact token. Use when you don't have a specific native hash/name (use `lookup_native`) and the term isn't a known asset name in a large data table (use `grep_docs`). Hybrid mode (default) handles 'how do I X' queries ('teleport player', 'spawn vehicle', 'inventory add item') AND tokens ('addItem', 'weapon_pistol_volcanic', 'CPED_CONFIG_FLAG_') — fused via RRF over vector + BM25. Returns ranked snippets (path, breadcrumb, heading, snippet, score). Call `get_document({path, heading})` for full chunk content. `mode=semantic` for pure vector; `mode=lexical` for pure BM25. Filter via `category=vorp|rsgcore|oxmysql|natives|discoveries|jo_libs|learnings` or `namespace`. Community findings merged by default; `category=learnings` returns only findings.

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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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• read_guides

  Arcadia Finance

  Get Arcadia workflow guides and reference documentation. Call this before multi-step workflows (opening LP positions, enabling automation, closing positions) or when you need contract addresses, asset manager addresses, or strategy parameters. Topics: overview (addresses + tool catalog), automation (rebalancer/compounder setup), strategies (step-by-step templates), selection (how to evaluate and parameterize strategies).

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• get_vaadin_primer

  docs-mcp

  IMPORTANT: Always use this tool FIRST before working with Vaadin. Returns a comprehensive primer document with current (2025+) information about modern Vaadin development. This addresses common AI misconceptions about Vaadin and provides up-to-date information about Java vs React development models, project structure, components, and best practices. Essential reading to avoid outdated assumptions. For legacy versions (7, 8, 14), returns guidance on version-specific resources.

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• export_tasks

  mcp

  Generate a Markdown overview of all tasks grouped by status (in_progress, blocked, open, null, done) with completion percentages. Tasks without history appear under "Geen status". Includes recent activity from today and yesterday. Use this at the start of a session for a quick backlog overview, or to share current status.

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• get_city_guide

  tickadoo-mcp

  Use this when the user wants an orientation overview of a city for trip planning. Returns highlights, dominant categories, price band, best-for audience hints, seasonal notes, and a short list of local advice items.

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• waveguard_fingerprint

  WaveGuard

  Get a physics embedding of any data item (52-dim at Level 0, 62-dim at Level 1 with phase statistics). The fingerprint captures structural properties via wave-equation dynamics — useful for similarity search, clustering, baseline comparison, and drift detection. Works on JSON objects, token metrics, wallet activity, trading data, or any structured data. Returns a deterministic vector with labeled dimensions (chi statistics, energy distribution, gradient patterns, and phase coherence at Level 1).

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• help

  InsideOut (Riley)

  Get workflow guidance for using InsideOut infrastructure tools. Call help() for a compact overview, or help(section=...) for a detailed guide. Sections: workflow, tools, examples, inspect. Responses include hints with next_actions and related_tools.

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• delete_memory

  lawmem-ai/lawmem-mcp

  Permanently delete a stored memory by its UUID. This is a hard delete for GDPR right-to-erasure compliance. The memory is removed from both the vector store and the database. This action cannot be undone.

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• thinkneo_sla_dashboard

  ThinkNEO Control Plane

  SLA overview dashboard — all agents, current status, error budgets, and recent breaches (7d). The SRE dashboard for AI agents. Requires authentication.

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• chunk_text

  Toolora MCP Server

  Use this tool to split long text into smaller, overlapping chunks suitable for embedding, vector storage, or RAG pipelines. Triggers: 'chunk this document for RAG', 'split this into embeddings', 'break this into segments', 'prepare this text for a vector database'. Returns an array of chunks with index, text, character count, and estimated token count. Essential before embedding or storing text in a vector database.

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