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298,188 tools. Last updated 2026-07-14 11:30

"supabase" matching MCP tools:

  • 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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  • Publish a post immediately to the user's connected social accounts (Instagram, TikTok, LinkedIn, X, Threads). ALWAYS confirm with the user first (target platforms + caption). Instagram and TikTok require at least one image or video in media_urls; LinkedIn, X, and Threads allow text only. Caption limits: X 280 chars, Threads 500 chars. X has a monthly allowance of 50 posts included in the add-on. Pass generated asset URLs (Supabase-hosted) as media_urls. LinkedIn also supports DOCUMENT posts (rendered as a swipeable carousel): pass exactly ONE .pdf/.ppt/.pptx/.doc/.docx URL in media_urls, targeting linkedin only. Document rules: one document per post, never mixed with images or videos, max 100 MB and 300 pages, password-protected PDFs are not supported, and the URL must serve the file bytes directly (Supabase-hosted works; Drive/Dropbox/OneDrive share links do not). Set document_title for document posts, and put any external link in first_comment instead of the caption (LinkedIn suppresses captions with links). 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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  • Compare 2-3 developer tools side by side. Returns each tool's full Markdown-KV entry separated by "===". Alternatives and worksWith are enriched with tagline + agent-readiness for resolved slugs. If any requested slugs are not found, they appear in a trailing "Note: slugs not found: ..." line; the comparison still returns for the ones found. Examples: - Three search engines: {slugs: ["meilisearch-oss", "algolia", "elasticsearch-oss"]} - Two ORMs: {slugs: ["drizzle-orm", "prisma"]} - Three auth providers: {slugs: ["auth0", "clerk", "keycloak"]} - Hosted vs self-hosted for the same vendor: {slugs: ["redis-cloud", "redis-oss"]} — shows deployment trade-off - Postgres engine vs hosted offerings: {slugs: ["postgresql", "supabase-cloud", "cockroachdb-cloud"]} Edge cases: - Cross-category comparisons (e.g., {slugs: ["auth0", "redis-cloud"]}) are allowed but rarely useful. Same-category comparisons answer "which should I pick?" better; cross-category answers "these coexist in my stack" — a compatibility question. - Minimum 2 slugs, maximum 3. Four or more is a validation error; for more, run pairs. - Invalid or unknown slugs are listed under "slugs not found"; the partial comparison returns for valid ones. - Duplicate slugs in the array are deduplicated. - A few tools are single entries (no -cloud/-oss split): stripe, auth0, firebase, twilio, openai-api, pinecone, algolia. Don't pass "stripe-cloud" — it doesn't exist. Risk: read-only, closed-world, idempotent — no state change possible.
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  • Returns 10 HBM market sub-tables: accelerators, specs, marketShare, spotPrices, leadingIndicators, qualificationFeed, revenueForecast, supplierRevenue, validationChecks, bitDemand. Optional `table` parameter narrows to a single sub-table; omitting returns all 10. USE THIS for: HBM3/3e/4 generation specs, SK Hynix/Samsung/Micron market share, spot vs. contract pricing, derived HBM bit demand by SKU class and customer type (bitDemand, EB ranges, monthly). bitDemand is NOT a workload split — it is a SKU-class/customer-type cut. Dominant HBM SKUs are dual-use, so a training-vs-inference HBM attribution would be dishonest; no public source publishes one. 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. Research tables refresh Mon/Wed/Fri; bitDemand refreshes monthly (1st).
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  • 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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  • 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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  • 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 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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  • 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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  • 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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  • 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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  • 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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  • 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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  • Query Zephex's expert developer knowledge base — not the user's repo. operation search finds entries by natural-language query; operation get returns full guidance by slug from search results. Returns topic, summary, principles, snippets, checklist. Call when user needs standard-pattern playbooks (auth, payments, security headers, deploy, Supabase RLS) after repo tools if local context is insufficient — no local project path required. Requires KB_SERVICE_URL on server (hosted zephex.dev includes this; self-hosters deploy /kb). DO NOT call for user's codebase structure (get_project_context), symbol search (find_code), file bodies (read_code), package registry intel (check_package), test health (check_test), or live URL audit (audit_headers). Workflow: search first, then get with returned slug. Optional category filter narrows search. Read-only.
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  • Run a free SimplyScan security & speed scan of a deployed web app URL. Returns a 0-100 score and findings (exposed secrets, missing Supabase RLS, frontend leaks, speed issues). Best for apps built with Lovable, Bolt, v0, Cursor, Replit, etc.
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  • 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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  • 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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  • Orientation for wiring a redu.cloud capability (backups, DNS, extra storage, a managed DB, ...) INTO an app already deployed on redu, e.g. 'add a backup feature to the Supabase I deployed on redu'. Explains the pattern: mint a LEAST-PRIVILEGE scoped API key (with the user's approval via create_api_key), inject it into the app, and call the redu API from the app. Call this when a user asks to add/integrate a redu feature into a running deployment and you are unsure how.
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  • Put one or more media files into your workspace media library (max 10, uploaded sequentially). This is step one of attaching media to a node - follow up with clipform_attach_node_media to place the returned media_asset_id on a node. When a public URL is provided, the media is fetched and stored automatically. For video: ingested via Mux. For image: stored in Supabase. Captions from clipform_generate_tts enable per-word highlighting in the viewer once attached.
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  • 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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