"Supabase" matching MCP tools:

• create_or_update_workflow

  Dreamlit

  Creates a new Dreamlit workflow draft or updates an existing draft from an outcome-oriented natural-language prompt. Use after get_status; use get_workflow_and_preview_url first when editing an existing workflow. Existing Supabase Auth workflows can be edited except for the immutable trigger step; creating Supabase Auth workflows must happen through Supabase Auth email setup in the Dreamlit web app. Side effect: may create or modify a draft, but does not publish or install live triggers. Returns the workflow/draft result, action-required or handoff details when more input is needed, and relevant app URLs. Do not use for publishing, direct database changes, or low-level graph edits.

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• get_analyst_config

  TunnelMind Data API

  Returns the TunnelMind analyst config bundle. Configures any LLM (Claude, GPT, Gemini, local) to behave as a TunnelMind analyst that knows the data graph, follows the 5-call golden path, and surfaces attestation_tier on every claim. The bundle is signed inline (Ed25519, key_id from /.well-known/receipt-signing-key.json). Add `?receipt=true` to wrap the response in a Receipt v1.0 envelope for end-to-end audit. Use this tool when: - You want to configure a new LLM runtime to act as a TunnelMind analyst - You want to verify the system prompt you're running matches what TunnelMind serves - You're building a BYOM (bring-your-own-model) deployment and need the canonical config Do NOT use this tool when: - You want to call individual TunnelMind data tools — use the tools directly - You want to verify a specific receipt — use check_receipt_revoked or @tunnelmindai/receipt-verify Inputs (all optional): - `surface` (query): "data" (default, full surface), "scry", or "sigil" - `version` (query): pin a specific bundle version (e.g. "1.0.0" or "1" for latest 1.x.y) - `receipt` (query): "true" to wrap the response in a signed Receipt v1.0 envelope Content negotiation (via Accept header): - `application/json` (default) — full bundle JSON - `text/markdown` — system prompt only (Anthropic flavor) - `application/vnd.anthropic.config+json` — Anthropic-shaped subset - `application/vnd.openai.config+json` — OpenAI-shaped subset Returns: - `version`, `schema`, `issuer`, `surface`, `surface_label` - `system_prompts.{anthropic,openai,generic}` — three encodings of the same semantic prompt - `tools.surface_subset` — array of operationIds for this surface (null = all) - `response_format` — JSON Schema the analyst's verdicts must conform to - `attestation_tiers` — the 4-tier vocabulary (self_asserted → silicon_root) - `graph_state` — live corpus counts at serve time - `references` — URLs to the rest of the open-protocol layer - `bundle_signature` — inline Ed25519 signature for offline verification - `pin_recommended` — stable supply-chain identifier (survives hourly graph_state updates) Headers: `X-Bundle-Version`, `X-Pin-Recommended`, `ETag`, `X-RateLimit-*`. Cost: - Free, anonymous-accessible. Rate-limited on a SEPARATE counter from data-API calls (`cfg:ip:<ip>` identity) so a config refetch loop can't burn your data quota. Latency: - Typical <100ms (cached); cold fetch <500ms (live Supabase counts).

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• keep_thinking

  Zephex

  Multi-call reasoning scaffold for AI coding agents — NOT Anthropic's single-call think tool, NOT extended thinking. Tracks hypotheses, observations, conclusions, and assumptions across iterative tool-call chains. Detects circular debugging, repeated failed approaches, and dangerous operations. Returns: shouldContinue, riskLevel (high/critical blocks continuation), repetitionWarning, reflectionPrompt (recovery questions on loop), boredLoopDetected (same tool called twice), approachingLimit (2 thoughts before cap). Call when: (1) high-blast-radius edit — schema, auth, billing, multi-file refactor, production deploy. (2) Debugging after 2+ failed attempts. (3) Task spans 3+ files. (4) Ambiguous requirements — surface assumptions first. DO NOT call when: (1) you already know the answer — act. (2) Single-step task — rename, typo, file read. (3) You're calling again without new evidence — that's a loop, stop. (4) Session is closed (nextThoughtNeeded:false was set). Pass lastActions (last 2-5 tool calls) to enable boredom detection. Set actionReady:true to exit early when planning is done. Set nextThoughtNeeded:false to close the session and write a Supabase checkpoint. Pass sessionId to resume — previously rejected approaches are injected so you don't repeat them. Hard cap: 10 thoughts per session.

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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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• save_kb_card

  fixflow

  WRITE to the Knowledge Base. This tool has TWO modes: **MODE 1 — SAVE a new card**: Provide `content` with full Markdown following the ACTIONABLE schema below. **MODE 2 — REPORT OUTCOME**: Provide `kb_id` + `outcome` ('success' or 'failure'). WHEN TO USE: - Mode 1: After successfully fixing a bug IF no existing KB card covered it. - Mode 2: ALWAYS after applying a solution from `read_kb_doc` and running verification. INPUT: - `content`: (Mode 1) Full Markdown KB card content — follow the EXACT template below. - `overwrite`: (Mode 1) Set to True to update an existing card. - `kb_id`: (Mode 2) ID of the card to report outcome for. - `outcome`: (Mode 2) 'success' or 'failure'. - `enrichment`: (Mode 2, optional) Additional context to merge into the card when outcome is 'failure'. ━━━ CARD TEMPLATE (Mode 1) — copy this structure EXACTLY ━━━ ``` --- kb_id: "[PLATFORM]_[CATEGORY]_[NUMBER]" # e.g. WIN_TERM_001, CROSS_DOCKER_002 title: "[Short Title — max 5 words]" category: "[terminal|devops|supabase|fastmcp|network|database|...]" platform: "[windows|linux|macos|cross-platform]" technologies: [tech1, tech2] complexity: [1-10] criticality: "[low|medium|high|critical]" created: "[YYYY-MM-DD]" tags: [tag1, tag2, tag3] related_kb: [] --- # [Short Title — max 5 words] > **TL;DR**: [One sentence — what's the problem + solution] > **Fix Time**: ~[X min] | **Platform**: [Windows/Linux/macOS/All] --- ## 🔍 This Is Your Problem If: - [ ] [Symptom 1 — specific symptom or error message] - [ ] [Symptom 2 — specific error code or log line] - [ ] [Symptom 3 — environment/version condition] **Where to Check**: [console / logs / env / task manager / etc.] --- ## ✅ SOLUTION (copy-paste) ### 🎯 Integration Pattern: [Global Scope] / [Inside Init] / [Event Handler] ```[language] # [One-line comment — what this code does] [depersonalized code WITHOUT specific paths, use __VAR__ for things to replace] ``` ### ⚡ Critical (won't work without this): - ✓ **[Critical Point 1]** — [why it's essential] - ✓ **[Critical Point 2]** — [common mistake to avoid] ### 📌 Versions: - **Works**: [OS/library versions where confirmed working] - **Doesn't Work**: [OS/library versions where known broken] --- ## ✔️ Verification (<30 sec) ```bash [single command to verify the fix worked] ``` **Expected**: ✓ [Specific output or behavior that confirms success] **If it didn't work** → see Fallback below ⤵ --- ## 🔄 Fallback (if main solution failed) ### Option 1: [approach name] ```bash [command] ``` **When**: [condition to use this option] | **Risks**: [what might break] ### Option 2: [alternative approach] ```bash [command] ``` **When**: [condition] | **Risks**: [what might break] --- ## 💡 Context (optional) **Root Cause**: [1 sentence — why this problem occurs] **Side Effects**: [what might change after applying the fix] **Best Practice**: [how to avoid this in future — 1 point] **Anti-Pattern**: ✗ [what NOT to do — common mistake] --- **Applicable**: [OS, library versions, conditions] **Frequency**: [rare / common / very common] ``` ━━━ END OF TEMPLATE ━━━ RULES for ACTIONABLE cards: 1. Solution FIRST — after diagnosis, code immediately 2. Depersonalize — no names, project names, or absolute paths 3. Use `__VAR__` markers for anything the user must replace 4. One Verification command, result visible in <30 sec 5. Fallback — 1-2 options max, always include When/Risks 6. Context at End — WHY is optional reading for curious agents

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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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• 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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• 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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• hemmabo_host_onboarding_link

  HemmaBo Host Booking Engine

  Return a safe HemmaBo onboarding handoff URL for a vacation-rental host who wants an own-domain booking engine. Use after explaining the fit or when the host asks to start. This tool is read-only and does not create a HemmaBo account, buy a domain, configure Stripe, write to Supabase, or provision a booking site. It returns the URL, what the host gets, and what the host should prepare. All parameters are optional and only enrich the returned onboarding URL — propertyName, country/region/city, domain, and language are prefilled into it; nothing is stored.

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• publish_post

  Avocado AI

  Publish a post immediately to the user's connected social accounts (Instagram, TikTok, LinkedIn). ALWAYS confirm with the user first (target platforms + caption). Instagram and TikTok require at least one image or video in media_urls; LinkedIn allows text only. Pass generated asset URLs (Supabase-hosted) as media_urls. If the user isn't subscribed or hasn't linked the platform, the result will instruct you to show a connect card, do not retry in that case.

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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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• 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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• credithunt

  Zambo AI Tools

  Zambo Stack — Live, verified index of every AI and cloud startup credit program available right now. 29+ active programs including AWS Activate, Google Cloud, Azure, OpenAI, Anthropic, Vercel, Supabase, Modal, Groq, Replicate, and more. Verified daily — dead links auto-removed. Pass your tech stack to get matched recommendations. Free, no auth.

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• chieflab_query_proof_ledger

  ChiefLab

  P80 — query the persistent proof ledger. USE WHEN the user (or a dashboard) asks 'what has ChiefLab actually shipped for this workspace?' or 'show me the launch history.' Returns the proof rows for executed publishes / sends / manual-posts with the artifact URLs, channels, execution modes, and measurement state. Persistent across cold starts when deps.proofLedgerStore is wired to Supabase; falls back to in-memory (warm function lifetime) otherwise.

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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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• waveguard_scan

  WaveGuard

  Find outliers and anomalies in structured data — ideal as a second step after pulling records from Google Sheets, Airtable, Supabase, Notion databases, HubSpot, Financial APIs, GitHub, NPM, or any source that returns rows of JSON. Fully stateless: send known-good rows as training and suspect rows as test in ONE call. Returns per-row anomaly scores, confidence levels, and the top features explaining WHY each row was flagged. Typical workflow: (1) Pull data from another tool (e.g. Google Sheets, Supabase query, HubSpot deals). (2) Pass the first N rows as training (normal baseline). (3) Pass remaining or new rows as test. (4) Report which rows are anomalous and why. Works on JSON objects, numbers, text, arrays. No separate training step required. Examples: - Spreadsheet QA: Pull 500 sales rows from Sheets → train on first 400 → test last 100 → flag outlier entries - Financial screening: Get ratios for 50 stocks from a financial API → find anomalous ones - CRM hygiene: Pull HubSpot deals → flag deals with unusual discount/value patterns - Dependency audit: Get NPM package metrics → flag packages with anomalous quality scores - Commit review: Pull GitHub commit metadata → flag unusual commit patterns

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• waveguard_scan_timeseries

  WaveGuard

  Detect anomalies in time-series data — use after pulling numeric metrics from monitoring APIs, financial data sources, IoT sensors, or spreadsheet columns. Send a single numeric array and specify a window size. Early windows define 'normal', recent windows are tested for anomalies. Typical workflow: (1) Pull a column of numbers from Sheets, a Supabase time-series table, or a metrics API. (2) Pass the array here. (3) Get back which time windows are anomalous. Examples: - Revenue monitoring: Pull monthly revenue from Sheets → detect anomalous months - Stock screening: Pull 90 days of closing prices → find unusual price windows - Server health: Pull response-time metrics → identify degradation windows - Sensor QA: Pull temperature readings from IoT API → flag sensor drift

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• compare_vendors

  AgentDeals

  Compare developer tools and services side by side — free tier limits, pricing tiers, and recent pricing changes. Use this when choosing between similar services (e.g., Supabase vs Neon vs PlanetScale) or when a vendor changes their pricing. Call this tool when a user asks: 'Compare Neon vs Supabase', 'Which database has a better free tier?'.

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• chieflab_query_proof_ledger

  ChiefLab

  P80 — query the persistent proof ledger. USE WHEN the user (or a dashboard) asks 'what has ChiefLab actually shipped for this workspace?' or 'show me the launch history.' Returns the proof rows for executed publishes / sends / manual-posts with the artifact URLs, channels, execution modes, and measurement state. Persistent across cold starts when deps.proofLedgerStore is wired to Supabase; falls back to in-memory (warm function lifetime) otherwise.

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• Zephex_dev_info

  Zephex

  Expert developer knowledge base by Zephex. Use when the user asks how to build, structure, secure, or deploy anything. Covers: database schemas (Stripe, Supabase, Convex, Postgres), security (CSP, CORS, OWASP, JWT hardening), frontend (Next.js, React 19, Tailwind CSS), authentication (Supabase Auth, OAuth, refresh tokens), backend (AWS ECS, Docker, Bun), and mobile (Android, iOS, Expo, Play Store signing). Two operations: use 'search' with a query to find the right entry, then 'get' with the returned slug to fetch full expert knowledge. Always search first, then get.

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