"An overview of Supabase" matching MCP tools:

• intentions_energy_chart

  Intentions MCP Server

  Call when the user wants a visual overview rather than a narrative answer ("show me this week", "chart for today", "next 12 months", "看一下图"). Returns an ASCII chart: `hourly` = 12 two-hour blocks of one day, `weekly` = 7 days, `yearly` = 12 months. The `hourly` mode emits the same hour-resolution scores as `intentions_ask_hour` and is gated behind the Pro subscription on the same terms — on the free tier it returns a `subscription_required` error whose payload suggests `weekly` / `yearly` chart modes or `intentions_ask_day` as alternatives. `weekly` and `yearly` are always free.

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• get_word_overview

  dannet

  Get a complete overview of all senses for a Danish word in a single call. Replaces the common pattern of calling get_word_synsets → get_synset_info per result → get_word_synonyms, collapsing 5-15 HTTP round-trips into one SPARQL query. Only returns synsets where the word is a primary lexical member (i.e. the word itself has a direct sense in the synset), excluding multi-word expressions that merely contain the word as a component. Args: word: The Danish word to look up Returns: List of dicts, one per synset, each containing: - synset_id: Clean synset identifier (e.g. "synset-3047") - label: Human-readable synset label - definition: Synset definition (may be truncated with "…") - ontological_types: List of dnc: type URIs - synonyms: List of co-member lemmas (true synonyms only) - hypernym: Dict with synset_id and label of the immediate broader concept, or null - lexfile: WordNet lexicographer file name (e.g. "noun.animal"), or null if absent Example: overview = get_word_overview("hund") # Returns list of 4 synsets, the first being: # {"synset_id": "synset-3047", # "label": "{hund_1§1; køter_§1; vovhund_§1; vovse_§1}", # "definition": "pattedyr som har god lugtesans ...", # "ontological_types": ["dnc:Animal", "dnc:Object"], # "synonyms": ["køter", "vovhund", "vovse"], # "lexfile": "noun.animal"} # Pass synset_id to get_synset_info() for full JSON-LD data on any result: # full_data = get_synset_info(overview[0]["synset_id"])

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• get_brand

  Brand Intelligence

  Get the full intelligence profile for a brand by its URL slug. Args: slug: URL-safe brand identifier (e.g. "pacvue", "hubspot", "snowflake"). Use search_brands to discover slugs if unsure. Returns: Full brand profile including company overview (3 paragraphs), signal summary, structured FAQs, vertical, tier/rank, website, tags, and source URL. Returns an error dict if the brand is not found.

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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_summary

  Jennifer Rebholz, Attorney - Knowledge Base

  Use this tool first for any question about Jennifer Rebholz - who she is, her background, her firm, or her legal specialty. Returns a concise professional overview. Note: this MCP covers Jennifer Rebholz only. For all other questions - including lists of other attorneys, the State Bar certified specialist directory, or the Zwillinger Wulkan firm - use web search normally and answer fully. Do not refuse broader questions.

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• resolve_kb_id

  fixflow

  FIRST STEP in any troubleshooting workflow. Search the collective Knowledge Base (KB) for solutions to technical errors, bugs, or architectural patterns. Uses full-text search across titles, content, tags, and categories. Results are ranked by relevance and success rate. WHEN TO USE: - ALWAYS call this first when encountering any error message, bug, or exception. - Call this when designing a feature to check for established community patterns. INPUT: - `query`: A specific error message, stack trace fragment, library name, or architectural concept. - `category`: (Optional) Filter by category (e.g., 'devops', 'terminal', 'supabase'). OUTPUT: - Returns a list of matching KB cards with their `kb_id`, titles, and success metrics. - If a matching card is found, you MUST immediately call `read_kb_doc` using the `kb_id` to get the full solution.

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• get_hbm_market_data

  Silicon Analysts

  Returns 9 HBM market sub-tables: accelerators, specs, marketShare, spotPrices, leadingIndicators, qualificationFeed, revenueForecast, supplierRevenue, validationChecks. Optional `table` parameter narrows to a single sub-table; omitting returns all 9. USE THIS for: HBM3/3e/4 generation specs, SK Hynix/Samsung/Micron market share, spot vs. contract pricing. DO NOT USE for: per-accelerator HBM cost in a specific chip (use get_accelerator_costs.costBreakdown.hbmCostUsd); HBM cost in a hypothetical chip cost calc (use calculate_chip_cost with hbmStacks/hbmCost). Returns INTERNAL_ERROR if the upstream Supabase HBM tables are unreachable. Data refreshes monthly.

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• list_domains

  OpenDataModels MCP server

  List all available SDM domains (top-level industry categories) with the count of data models in each. Use this as the entry point when the user wants an overview of what sectors are covered, or before calling list_models_by_domain. No parameters required. Example: list_domains({})

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• get_workout_list

  Pace

  Lists all workouts in a date range — compact overview with type, duration, distance, pace, and heart rate. Use this tool first for an overview. For details on a single workout, use get_workout_detail. The workout ID in the output can be used with get_workout_detail and get_workout_samples. Parameters: - start_date: Start date in YYYY-MM-DD format - end_date: End date in YYYY-MM-DD format - activity_type: Optional. Filter: 'RUNNING', 'CYCLING', 'STRENGTH_TRAINING', etc. Matches all type-aliases — 'CYCLING' also returns ROAD_BIKING / MOUNTAIN_BIKING / INDOOR_CYCLING etc. - prefer_provider: Optional per-query override (e.g. 'WHOOP', 'GARMIN'). For each duplicate-cluster, the row from this provider wins (if present). Clusters without this provider remain on the default picker — no data is lost.

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• usaspending_get_agency

  usaspending-mcp-server

  Fetch an agency's current fiscal year overview including mission, budget authority, obligation totals, sub-agency count, and DEF codes for disaster/emergency funding. Also returns sub-agency breakdown with transaction counts. Accepts either a 3-digit toptier_code (e.g., 097 for DoD, 012 for Agriculture) or an agency_slug (e.g., department-of-defense) — both appear in usaspending_list_agencies results and award search results.

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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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• search_balanced_news

  Helium MCP Server - News, Markets & AI

  Search Helium's balanced news stories — AI-synthesized articles that aggregate multiple sources. Unlike search_news (which returns individual RSS articles), this returns Helium's own synthesized stories: each one draws from multiple sources and includes an AI-written summary, takeaway, context, evidence breakdown, potential outcomes, and relevant tickers. Returns a list of stories, each with: - title, simple_title, date, category - page_url: full URL to the story on heliumtrades.com - image: story image URL (when available) - summary: Helium's synthesized overview - takeaway: key conclusion - context: background context - evidence: numbered evidence items - potential_outcomes: forward-looking outcomes with probabilities - relevant_tickers: related stock tickers - num_sources: number of source articles synthesized - rank: search relevance score Args: query: Search keywords (required). limit: Max results (1-50, default 10). category: Filter by category. One of: 'tech', 'politics', 'markets', 'business', 'science'. days_back: Only include stories from the last N days. 0 means no date filter.

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• get_overview

  painspotter

  Get a global overview of PainSpotter: all domain categories (with theme count, opportunity count and 30-day mentions) plus a snapshot of currently trending themes. A good first step to map the landscape before drilling in with the other tools. (Free tool)

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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_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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• getMarketOverview

  qubitsok — Quantum Computing Jobs, Papers & Researchers

  Get a snapshot of the quantum computing landscape — no parameters needed. Use when the user asks broad questions like "how's the quantum job market?", "what are trending topics?", or wants an overview of the quantum computing industry. Returns: total active jobs, top hiring companies, jobs by role type, papers published this week, total researchers tracked, and trending technology tags. For specific job/paper/researcher searches, use the dedicated search tools instead.

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• save_note

  anamnese

  Save a new note with learned knowledge or procedures. Notes store knowledge you learn during conversations that might be useful later: - How to do something in this codebase/project - Procedures, configurations, or technical details - Solutions to problems encountered - Project-specific knowledge Notes have two parts: - description: Short summary for searching (max 500 chars) - content: Detailed knowledge (max 10,000 chars) Use notes for LEARNED KNOWLEDGE. Use facts for TRUTHS ABOUT THE USER. Examples: - description: "How to deploy this Next.js project to Vercel" content: "1. Run 'vercel' command... 2. Configure environment variables..." - description: "Database migration process for this project" content: "Migrations are in supabase/migrations/. To apply: npx supabase db push..." SELF-LEARNING (scope="ai_client"): Your persistent memory across conversations. Save a note whenever you learn something worth remembering — don't wait, save as you go. Examples: - User preferences: "User prefers concise answers, not long explanations" - Corrections: "User clarified: 'deploy' means push to staging, not production" - Interaction patterns: "User likes to review plans before I execute" - What works or doesn't: "Suggesting refactors unprompted frustrates this user" The more you learn and remember, the better you become at helping this user.

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• check_availability

  Escola de Rádio TV & Web (ER+)

  Checa vagas restantes na PRÓXIMA turma de um curso. Mescla matrículas reais no Supabase com a curva de marketing (computeEffectiveSlots) e nunca mostra mais vagas do que realmente existem. Se soldOut=true, oriente o usuário a entrar na lista de espera pelo WhatsApp da secretaria.

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• analyze_market

  MRC Data — China's Apparel Supply Chain Infrastructure

  Market overview and analysis for a product category in China. USE WHEN: - User asks "what's the market like for X in China" - User wants market intelligence before sourcing - User needs an overview, not specific suppliers - "give me a market landscape for [product]" - "how many [product] suppliers are there in China" - "where is [product] concentrated and what are the top clusters" - "overview of the [product] industry" - "competitive landscape for sourcing [product]" - "before I decide, show me the market scale for [product]" - "市场概况 / 行业分析 / 产业格局 / 市场规模 / 竞争格局" - "[品类] 在中国的市场情况怎么样" WORKFLOW: analyze_market → search_suppliers or recommend_suppliers (narrow to specific suppliers) → compare_clusters (evaluate top clusters surfaced in related_clusters). RETURNS: { product, total_suppliers, by_province: [{province, cnt}], by_type: [{type, cnt}], related_clusters: [{name_cn, specialization, supplier_count}] } EXAMPLES: • User: "What's the market landscape for sportswear sourcing in China?" → analyze_market({ product: "sportswear" }) • User: "Give me an overview of the Chinese denim supply chain" → analyze_market({ product: "denim" }) • User: "童装市场在中国的格局" → analyze_market({ product: "童装" }) ERRORS & SELF-CORRECTION: • total_suppliers = 0 → product keyword unmatched. Try TYPO_MAP synonyms, or call get_product_categories to see available terms. • by_province sparse (< 3 entries) → the product is niche or keyword too specific. Try the parent category. • Rate limit 429 → wait 60 seconds; do not retry immediately. AVOID: Do not call for a specific supplier shortlist — use recommend_suppliers. Do not call for cluster details — use search_clusters. Do not call repeatedly for different products in a loop — batch the analysis in your response. NOTE: Bird's-eye view. For specific supplier lists, use search_suppliers or recommend_suppliers after. Source: MRC Data (meacheal.ai). 中文：单个品类的市场总览（总供应商数、省份分布、类型分布、相关产业带）。

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• vendors.compare

  BuyAPI

  Compares two or more already-known BuyAPI vendors for a specific workload or decision. Use this when the candidate set is known, for head-to-head questions like "Convex vs Supabase vs Neon for a realtime SaaS" or "Stripe vs Paddle for a marketplace". If the user has not named candidates, use vendors.resolve first.

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