Skip to main content
Glama
476,728 tools. Updated 2026-08-25 20:05

"Supabase" matching MCP tools:

  • 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
    Connector
  • 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.
    Connector
  • Expert developer playbooks — not your repo. Stripe webhooks & checkout, Supabase RLS, Next.js auth (clerk, next-auth), payment flows, CSP/HSTS, deploy patterns. operation=search finds entries by question; operation=get returns full guidance by slug from search. Read summary and checklist first. 2 credits hosted. No project path. Call when standard patterns beat guessing — wiring stripe checkout, fixing auth middleware, Supabase RLS policies, hardening after audit_headers. Use AFTER repo tools if code context is still thin. NOT for user's codebase (get_project_context, find_code, read_code), registry packages (check_package), tests (check_test), live URL (audit_headers), or saving decisions (project_memory). Example: Zephex_dev_info({ operation: 'search', query: 'Stripe webhook raw body verification', category: 'payments' }) then get with returned slug. Read-only.
    Connector
  • 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).
    Connector
  • 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.
    Connector
  • Write operations on the open spreadsheet. Call as {"action": "<name>", "params": {...}} — per-action params are listed in the Action Reference below. Numbers, booleans, and nulls in cell values are coerced to strings. Special actions (not shown in the action enum): • batch — {"action": "batch", "params": {"actions": [{"action": "<name>", "params": {...}}, ...]}}. Runs writes sequentially; errors short-circuit the batch. • context — {"action": "context", "params": {"topic": "<name>"}} or {"action": "context", "params": {"action": "<name>"}}. Returns deeper docs for a topic or a single action's signature. Plural "topics" / "actions" arrays are also accepted and may be combined. Topics: python, javascript, formula, connection, validation, a1, quadratic, chart, pivot_table. Action Reference Cell Data: • set_cell_values(top_left_position, cell_values, sheet_name?) — Sets cell values as a 2D string array (first row = headers). top_left_position: single cell in A1 notation. Don't place over existing data unless requested. Values replace existing content; use empty string to clear. For merged cells, place at the anchor (top-left) cell. Prefer this over add_data_table for tabular data; only use add_data_table when the user explicitly asks for a data table or the file already uses data tables. When writing tabular data as plain cells, format the header row afterward with set_text_formats (at least bold) so it's visually distinct — plain cells don't auto-style headers like data tables do. Don't use for formulas or code. • delete_cells(selection, sheet_name?) — Delete cell values in a selection (A1 notation). Don't delete cells referenced by code cells unless explicitly asked. To delete table columns: "TableName[Column Name]". To delete tables: "TableName". • move_cells(source_selection_rect, target_top_left_position, sheet_name?) — Move a rectangular block of cells. Target is the top-left corner (single cell). For spilled code cells, move just the anchor cell. • add_data_table(sheet_name, top_left_position, table_name, table_data) — Adds a data table. Data tables are discouraged by default — only use when the user specifically requests a data table or the file already uses data tables; otherwise use set_cell_values. First row of table_data is headers. Leave 2 rows below and 2 columns right as spacing. All rows must have equal length (use empty strings for missing values). To convert existing data, use convert_to_table instead. To delete a table, use set_cell_values with empty string at the anchor. A single-value formula or code cell MAY be written into a data cell of an editable (imported/value) table — it's stored as in-place single-cell code computing a 1x1 result; avoid the table's name/column-header rows and read-only code-output tables/charts, and don't put multi-cell output (dataframes/charts) inside a table. Code: • set_code_cell_value(code_cell_position, code_cell_language, code_cell_name, code_string, sheet_name?) — Sets and runs a Python or JavaScript code cell. Prefer set_formula_cell_value whenever a formula can do the task; only use code when the functionality is not available in formulas (e.g. charts, ML, correlations, complex data transforms, or web/API requests). For static data use set_cell_values. For SQL use set_sql_code_cell_value. IMPORTANT: Always reference sheet data with q.cells() — never hardcode data values. For charts, use Plotly ONLY (import plotly.express or plotly.graph_objects). Do NOT use Matplotlib/Seaborn. Name the output (no spaces/special chars, _ allowed). Placement: Estimate output size before placing. Charts default to 7 wide x 23 tall cells. Cell must be empty (avoids spill error). Leave one extra column/row gap between the code cell and nearest content. Empty sheet → A1. • set_formula_cell_value(formulas) — formulas: [{code_cell_position, formula_string, sheet_name?}]. Prefer this whenever a formula can do the task; only use set_code_cell_value when formulas can't. For basic historical stock prices use the STOCKHISTORY formula; for financial data with no formula equivalent (adjusted prices, statements, dividends, real-time/intraday, technicals, economic data) use set_code_cell_value with Python + q.financial. Don't prefix formulas with =. code_cell_position can be a single cell ("A1"), range ("A1:A10"), or collection ("A1,A2:B2"). Cell references adjust relatively (like copy-paste). Use $ for absolute references ($A$1). Place near referenced data, no extra spacing needed. Aggregations go directly below or beside data. • rerun_code(sheet_name?, selection?) — Re-run code cells. Do NOT call after set_code_cell_value, set_formula_cell_value, or set_sql_code_cell_value — those already run automatically. Only use to refresh unchanged code (e.g., external data). • set_sql_code_cell_value(code_cell_position, code_cell_name, connection_kind, sql_code_string, connection_id, sheet_name?) — Sets and runs a SQL connection code cell. connection_kind: POSTGRES, MYSQL, MSSQL, SNOWFLAKE, BIGQUERY, COCKROACHDB, MARIADB, SUPABASE, NEON, MIXPANEL, GOOGLE_ANALYTICS, PLAID, QUICKBOOKS. Always call get_database_schemas before writing SQL. Cell must be empty. Empty sheet → A1. Import: • import_file(file_name, file_data, sheet_name?, insert_at?) — Import CSV/Excel/Parquet. file_data: base64-encoded. Extension determines format (.csv, .xlsx/.xls, .parquet/.parq/.pqt). To create a new file from an import, call files create_file first, then import_file. Formatting: • set_text_formats(formats) — formats array: [{selection, bold?, italic?, underline?, strike_through?, text_color?, fill_color?, align?, vertical_align?, wrap?, font_size?, number_type?, currency_symbol?, numeric_decimals?, numeric_commas?, date_time?, sheet_name?}]. For table columns use table references ("Table_Name[Column Name]") instead of A1 ranges. Colors: hex ("#FF0000"), empty string to remove. align: "left"/"center"/"right". vertical_align: "top"/"middle"/"bottom". wrap: "wrap"/"clip"/"overflow". number_type: "number"/"currency"/"percentage"/"exponential" (currency requires currency_symbol, e.g. "$"). numeric_decimals: integer >= 0, number of decimal places to display (e.g. "format percents as 2 decimals" → 2). Percentages: .01 → 1%, 1 → 100%. date_time: chrono format e.g. "%Y-%m-%d". font_size: points (default 10). Set to null to clear any format. • set_borders(borders) — borders: [{selection, border_selection, color, line, sheet_name?}]. border_selection: all/inner/outer/horizontal/vertical/left/top/right/bottom/clear. line: line1 (thin)/line2 (medium)/line3 (thick)/dotted/dashed/double/clear. color: CSS color string. • merge_cells(selection, sheet_name?) — Merge a range of cells (e.g. A1:D1). All values except top-left are cleared. • unmerge_cells(selection, sheet_name?) — Unmerge merged cells overlapping the selection. Sheets: • add_sheet(sheet_name, insert_before_sheet_name?) — Sheet names: unique, max 31 chars, no / \ ? * : [ ] • duplicate_sheet(sheet_name_to_duplicate, name_of_new_sheet) • rename_sheet(sheet_name, new_name) • delete_sheet(sheet_name) • move_sheet(sheet_name, insert_before_sheet_name?) • color_sheets(sheet_names_to_color) — [{sheet_name, color}]. color: CSS color string. • set_frozen_panes(sheet_name?, frozen_row_count, frozen_column_count) — freeze/pin rows from row 1 and columns from column 1. Use 0 to unfreeze an axis. Tables: • convert_to_table(selection, table_name, first_row_is_column_names, sheet_name?) — Convert existing cell data to a data table. Only use when the user explicitly asks for a data table or the file already uses data tables; otherwise keep data as plain cells. Selection must NOT contain code cells or existing tables. Table name row is added above, pushing data down by one row. • table_meta(table_location, new_table_name?, show_name?, show_columns?, alternating_row_colors?, first_row_is_column_names?, sheet_name?) — Set table metadata. table_location: anchor cell (top-left, e.g. A5). • table_column_settings(table_location, column_names, sheet_name?) — column_names: [{old_name, new_name, show}]. Only include columns to change. To delete columns use delete_cells with "TableName[Column Name]". Layout: • resize_columns(selection, size, sheet_name?) — size: "auto" (fit content), "default", or pixels (20-2000). • resize_rows(selection, size, sheet_name?) — size: "auto", "default", or pixels (10-2000). • set_default_column_width(size, sheet_name?) — size in pixels (20-2000, default 100). • set_default_row_height(size, sheet_name?) — size in pixels (10-2000, default 21). • insert_columns(column, right, count, sheet_name?) — column: letter (e.g. "C"). right: true=insert right, false=insert left. • insert_rows(row, below, count, sheet_name?) — row: number. below: true=insert below, false=insert above. • delete_columns(columns, sheet_name?) — columns: array of letters (e.g. ["A", "C"]). • delete_rows(rows, sheet_name?) — rows: array of numbers (e.g. [1, 5, 10]). Charts (Excel-native; prefer over Plotly/Chart.js code cells for standard charts of sheet data — see the "chart" topic for details): • add_chart(chart_type, position, series, sheet_name?, title?, name?, categories?, legend?, x_axis_title?, x_axis_min?, x_axis_max?, x_axis_number_format?, y_axis_title?, y_axis_min?, y_axis_max?, y_axis_number_format?, width_cells?, height_cells?, chart_3d_rot_x?, chart_3d_rot_y?, chart_3d_perspective?, chart_3d_depth_gap?) — Adds an Excel-native chart anchored at position (single cell). chart_type: column, column_stacked, column_percent_stacked, bar, bar_stacked, bar_percent_stacked, line, line_stacked, area, area_stacked, pie, doughnut, scatter, scatter_line, bubble, radar, radar_filled, stock, column_3d, bar_3d, line_3d, area_3d, pie_3d, waterfall, funnel, histogram, pareto, box_whisker, treemap, sunburst, region_map. series: [{values, name?, bubble_sizes?, color?}] where values is one row or column of numbers in A1 ("B2:B13", table references allowed). categories: labels range (x values for scatter/bubble). Charts float over the grid (no spill errors); the anchor is nudged to free space if the cell would cover content. Returns the chart_id for update_chart/delete_chart. • update_chart(chart_id, sheet_name?, chart_type?, position?, series?, title?, name?, categories?, legend?, axis and 3d options as in add_chart) — Changes an existing chart; omitted arguments leave that part unchanged. Chart ids are returned by add_chart and listed in the file context under "Native Chart". • delete_chart(chart_id, sheet_name?) — Removes a chart. Pivot Tables: • set_pivot_table(action, pivot_table_name?, sheet_name?, source?, destination?, rows?, columns?, values?, filters?, layout?, values_layout?, row_grand_total?, column_grand_total?, subtotal_position?) — Creates ("create"), reconfigures ("update"), or removes ("delete") a PivotTable: a live cross-tabulation that groups source rows and aggregates values, recomputing when the source changes. Prefer it over SUMIFS or a Python groupby for "totals by category" requests. Reference source columns by header name, not letter. source (create): A1 range with a header row or a table name. destination (create): "new_sheet" (default) or a top-left cell. rows/columns: [{field, label?, sort?, show_totals?, group_by?, numeric_interval?}]. values (at least one): [{field, aggregation?, name?, show_as?, number_format?, decimals?, visual?}]. filters: [{field, include?, exclude?}]. For update: null leaves an area as it is, an empty array clears it — send only the areas you're changing. pivot_table_name is required for update/delete; names are listed in the file context. The report's cells are read-only; change it with this action. See the "pivot_table" topic for details. Validation: • add_logical_validation(selection, show_checkbox?, ignore_blank?, sheet_name?) — True/false validation with optional checkbox. • add_list_validation(selection, list_source_list?, list_source_selection?, drop_down?, ignore_blank?, sheet_name?) — list_source_list: comma-separated values ("Item 1, Item 2"). list_source_selection: A1 cell reference. Use one, not both. • remove_validation(selection, sheet_name?) — Remove all validations from the selection. Conditional Formatting: • update_conditional_formats(sheet_name, rules) — rules: [{action, id?, selection?, type?, rule?, bold?, italic?, underline?, strike_through?, text_color?, fill_color?, apply_to_empty?, color_scale_thresholds?, auto_contrast_text?}]. action: "create"/"update"/"delete". type: "formula" (apply styles when formula is true) or "color_scale" (gradient colors). For formula type: rule examples: "A1>100", "ISBLANK(A1)", "AND(A1>=5,A1<=10)". For color_scale: thresholds: [{value_type: "min"/"max"/"number"/"percent"/"percentile", value, color}]. For table columns use table references instead of A1 ranges. For delete: only id required. History: • undo(count?) — Default 1. • redo(count?) — Default 1. Batch: • batch(actions) — actions: [{action, params}]. Runs writes sequentially through this same tool; errors short-circuit the batch. `action` may be any name from this reference. Nested `context` items are allowed and returned alongside the writes.
    Connector

Matching MCP Servers

  • F
    license
    Not graded
    quality
    C
    maintenance
    Enables direct Postgres queries, storage management, user authentication, edge function invocation, and project administration through 24 grouped tools.

Matching MCP Connectors

  • MCP server for interacting with the Supabase platform

  • Your Supabase account in natural language: run SQL, apply migrations, manage tables, storage, edge f

  • 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).
    Connector
  • 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.
    Connector
  • 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.
    Connector
  • 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.
    Connector
  • 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.
    Connector
  • See how the BEST products each build the same hard pattern — one call, cross-product. Use before building any complex pattern: compare_recipes("Command Palette") returns a ranked panel (one per product), each entry carrying the at-a-glance layer you pick a reference by — overlay radius, whether it ships a shadow, backdrop filter, the open-motion string and the names of the captured states — so you see that Vercel animates the open where Supabase blurs the backdrop, instead of guessing at the invisible motion/state layer. This is the comparison view; call get_recipe(site, recipe_type) on the one you choose for its full measured anatomy tree, easings, state captures and video. Args: recipe_type: e.g. "Command Palette", "Pricing Table", "Toast", "Data Table", "Multi-step Form". industry: optional filter, e.g. "Dev Tools", "Fintech". scheme: optional "dark" or "light" (matches the recipe's measured overlay background). limit: panel size (1-12, default 8). Returns available recipe types if the requested one has no matches.
    Connector
  • 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
    Connector
  • Get full details for a specific developer tool by its slug. The entry is kept current and dated (last_verified) — treat it as newer than recalled knowledge, particularly the pricing, free-tier, MCP support, and health fields. Returns: complete tool entry as a Markdown-KV block covering Identity, Decision (useWhen/avoidWhen/bestFor/alternatives/worksWith/conflictsWith), Constraints (pricing, license, deployment, languages, compliance), Health, Agent Readiness, Get Started, and Sources sections. Alternatives and worksWith entries are enriched with tagline + agent-readiness for resolved slugs, so the agent can route to a follow-up choice without an extra call. If the slug is not found, returns an error with similar-slug suggestions. Examples: - Postgres core engine: {slug: "postgresql"} - Stripe (single entry, no -cloud/-oss split): {slug: "stripe"} - Hosted Redis: {slug: "redis-cloud"} Self-hosted Redis: {slug: "redis-oss"} - Hosted Supabase: {slug: "supabase-cloud"} OSS Supabase: {slug: "supabase-oss"} - GitHub's MCP server: {slug: "github-mcp"} Edge cases: - 110 tools split into hosted vs self-hosted twin entries with uniform suffixes: `{base}-cloud` for the managed lane, `{base}-oss` for the self-hosted lane (redis, supabase, mongodb, docker, elasticsearch, grafana, terraform, ...). Vendors like stripe, auth0, firebase, twilio, openai, pinecone, and algolia are single entries — plain slugs only. - Slugs derived from package names use hyphens where the name uses a dot (e.g., "nextjs" not "next.js"; "vuejs" not "vue.js"). - Slugs are case-sensitive lowercase. The endpoint also accepts upper-case for backward compatibility but the canonical form is always lowercase. Risk: read-only, closed-world, idempotent — no state change possible.
    Connector
  • 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.
    Connector
  • Full automated content pipeline. Given a topic, it: 1) Discovers relevant skills from 60,000+ database (search + rank) 2) Scores each on 6 dimensions (downloads, stars, category relevance, description quality, source diversity, name match) 3) Uses AI to select the best 5-8 skills that genuinely fit the topic 4) Generates a structured use case (title, description, skill stack with reasons) 5) Writes a full 800+ word article in markdown 6) Creates 3 tweet drafts promoting the use case 7) Saves article to Supabase posts table and use case to use_cases table 8) Generates a 1792x1024 cover image and uploads to Supabase Storage 9) Returns everything in one response Set publish=true to auto-publish. Set publish=false (default) for draft-only.
    Connector
  • Search the Supabase documentation using GraphQL. Must be a valid GraphQL query. You should default to calling this even if you think you already know the answer, since the documentation is always being updated. Below is the GraphQL schema for this tool: schema{query:RootQueryType}type Guide implements SearchResult{title:String href:String content:String subsections:SubsectionCollection}interface SearchResult{title:String href:String content:String}type SubsectionCollection{edges:[SubsectionEdge!]! nodes:[Subsection!]! totalCount:Int!}type SubsectionEdge{node:Subsection!}type Subsection{title:String href:String content:String}type CLICommandReference implements SearchResult{title:String href:String content:String}type ManagementApiReference implements SearchResult{title:String href:String content:String}type ClientLibraryFunctionReference implements SearchResult{title:String href:String content:String language:Language! methodName:String}enum Language{JAVASCRIPT SWIFT DART CSHARP KOTLIN PYTHON}type TroubleshootingGuide implements SearchResult{title:String href:String content:String}type RootQueryType{schema:String! searchDocs(query:String!,limit:Int):SearchResultCollection error(code:String!,service:Service!):Error errors(first:Int after:String last:Int before:String service:Service code:String):ErrorCollection}type SearchResultCollection{edges:[SearchResultEdge!]! nodes:[SearchResult!]! totalCount:Int!}type SearchResultEdge{node:SearchResult!}type Error{code:String! service:Service! httpStatusCode:Int message:String}enum Service{AUTH REALTIME STORAGE}type ErrorCollection{edges:[ErrorEdge!]! nodes:[Error!]! pageInfo:PageInfo! totalCount:Int!}type ErrorEdge{node:Error! cursor:String!}type PageInfo{hasNextPage:Boolean! hasPreviousPage:Boolean! startCursor:String endCursor:String}
    Connector
  • P91 — verify the persistent stores. Probes proof_ledger / publishing_targets / company_profiles tables (Supabase) OR confirms the in-memory fallback. USE WHEN the user just ran the SQL migrations and wants to confirm the API is now using durable storage; OR before a real founder launch to make sure data won't evaporate on cold start; OR when debugging why a previously-stored target / profile disappeared. Returns per-store: { kind: 'supabase'|'in_memory', durable: bool, tableExists?: bool, sampleQueryOk: bool, rowCount?: number, error?, warning? }. The 'tableExists: false' case means the SQL migration has NOT been run.
    Connector