"How to create a table in React" matching MCP tools:

• scaffold_providerA

  UploadkitA

  Return a ready-to-paste snippet that wraps the Next.js root layout with `<UploadKitProvider>` so React components can talk to the upload route handler. When to use: right after scaffold_route_handler, to complete the wiring. The snippet goes in `app/layout.tsx`. Without the provider, UploadKit React components throw at runtime. Returns: a plain-text string containing a short explanatory note followed by a fenced tsx code block. Takes no parameters — the endpoint path is always `/api/uploadkit` since that is what scaffold_route_handler produces. Read-only, deterministic, idempotent.

  ConnectorNo auth

• create_rowA

  DockA

  Append a new row to a workspace's table surface. The data field is a JSON object with column-name keys. Status column accepts: drafted, queued, sealed, active, blocked. Works on any workspace; columns auto-seed on the first row if the table surface is empty. Multi-surface workspaces accept `surface_slug` to target a specific sheet (use `list_surfaces` to enumerate); omit it to fall through to the workspace's primary table surface. **Unmapped data fields:** Keys in `data` that don't match any existing column are still STORED on the row (nothing is dropped), but they won't render in the table UI until the column exists. The response carries an `unmapped_fields` array listing those keys plus a human-readable `warning` so an agent can decide whether to surface them, call `add_column`, or retry with `auto_create_columns: true`. **Auto-create columns:** Pass `auto_create_columns: true` to have the server append a fresh text column for every unmapped key in one atomic step (humanised label from the key, type `text`). The response then includes `created_columns: ColumnDef[]` with the new column metadata. Use this when you're appending machine-emitted rows whose shape you can't predict ahead of time; leave it omitted (default false) when you want explicit schema control.

  ConnectorNo auth

• migrate_to_propelauth_frontendA

  PropelAuth Integration MCP ServerA

  Returns instructions for migrating to PropelAuth in a frontend framework such as React, JavaScript, TypeScript, or when using Next.js for just the frontend (e.g. client-side rendered). Guidance includes migrating from several auth providers, such as Clerk or Auth0. Each guidance will include documentation from the auth provider and PropelAuth. It is important to follow the instructions carefully to ensure a successful integration. Make sure to use the 'Installation' guidance first. It is important to call every guidance to ensure a successful integration. Do not update a component/hook/etc from the auth provider until you receive guidance about that component/hook/etc. CRITICAL: If the current implementation uses a traditional OAuth/OIDC flow (e.g., via express-openid-connect, passport-auth0, or similar backend-managed session libraries), you MUST select 'OAuth' as the framework, regardless of the frontend library (React/Vue/etc.). Only select 'React' or 'Javascript' if the current implementation uses a frontend-only SDK (like @auth0/auth0-react) or if using fullstack Next.js.

  ConnectorNo auth

• build_knowledge_indexA

  agent4.ioA

  Build (or rebuild) the structured index for a knowledge base — the second leg beside vector search. Vector search answers "what does this passage say". It **cannot count, filter numerically or aggregate**, so "how many documents", "which ones are between 1000 and 2000 words", "how many per category" are not answered badly — they are structurally unanswerable. This builds a small per-KB table from whatever structured header the documents share, which the agent can then query with SQL via `query_knowledge_table`. Only worth it when the documents share a machine-readable header (a metadata table, YAML front matter, `Field: value` lines). **Prose gets declined, and that is the right answer** — a table of unique values makes statistics meaningless. `roles` names the fields that must be extracted **exactly** and never paraphrased. Use it when the answer has to quote something the model must not invent: - `identity` — what to call the item (book title, drug name, product name) - `link` — where to send the user - `image` — what to show the user - `code` — the unique identifier Which link is "the" link is a business fact the data does not state — only the customer knows. A declared role that cannot be found comes back in `roles.unresolved` **with candidate field names**: ask the user which one it is, do not guess. **Read `dropped` in the report and tell the user about it.** A column that was thrown out (coverage too low, two columns holding identical values) is invisible in later query results — the model simply works around it — so this report is the only place it is ever mentioned.

  ConnectorNo auth

• count_firms_byA

  Analytics Legends — SAP Analytics IntelligenceA

  Answer a COUNTING question about the published firm directory in one call: how many organisations per country, per kind, per declared SAP module, or per SAP signal band — with the same `country`/`kind`/`module`/`query` filters `search_firms` takes, so you can count a slice as easily as the whole. Use this instead of paging `search_firms` and tallying rows: the directory holds thousands of organisations, and reading them all to produce a table of counts costs hundreds of calls and megabytes of rows for numbers Postgres computes in one scan. Every bucket is a value the directory actually stores; `value: null` is a real bucket meaning the field is unknown for those rows, and it is served rather than hidden — a country table that silently drops the rows with no country adds up to less than the population and says nothing about it.

  ConnectorNo auth

• sample_rowsA

  Mostly RightA

  The publisher's materialized preview of a table — real rows, no account, no query cost. 20 rows by default, 100 at most, and they are always the same rows: this is a sample for understanding shape and values, NOT a query. Example: {"table_id": "0f2f6bfa-4a63-4f75-9a0b-1a7d9c5b2e10", "limit": 20}. Returns {table, columns, rows, row_count, total_row_count, sample_truncated} — total_row_count is how many rows the whole table holds, which is usually far more than the sample. To filter, sort, aggregate or read beyond the sample, use query_table, which needs a workspace key.

  ConnectorNo auth

• write_filesA

  KleapA

  Write WHOLE files DIRECTLY — YOUR model generates the code, Kleap stores, builds and deploys it as-is. To change something in a file that ALREADY EXISTS, use edit_files instead (read_files → edit_files): it replaces just the lines you name, while write_files makes you retype the entire file and silently drops whatever you leave out — on a 30KB shared layout that is how headers and footers get wiped. No Kleap-AI step, so what ships is byte-for-byte what you wrote — the right choice when a phrase, a URL or a schema must be exact. Publishing still audits the result (see publish_app). Best for scaffolding exact pages/components — e.g. programmatic-SEO routes. Astro paths (src/pages/*.astro, src/data/*.json, src/components/*.astro, public/*). Overwrites by path. NPM PACKAGES: do not write package.json (the build replaces it) — the build installs whatever your code IMPORTS, so `import { jsPDF } from "jspdf";` is all it takes. Supported on import: @tiptap/*, jspdf, pdf-lib, html2canvas, papaparse, file-saver, jszip, @ffmpeg/*, howler, wavesurfer.js, browser-image-compression, react-dropzone, recharts, chart.js, d3, @tanstack/*, react-hook-form, three, @react-three/*, leaflet, maplibre-gl, gsap, framer-motion, zustand, date-fns, react-markdown, axios, socket.io-client, radix-ui/*, next-themes, lucide-react, @tabler/*, openai, @ai-sdk/*; anything else is refused at build with a message naming it. A client-side router is never the answer — a route is a FILE (src/pages/about.astro → /about). IMAGES AND BINARIES: set encoding:"base64" on the file and send the bytes — that is how you put a logo, a photo, an OG image, a favicon or a font on the site (png/jpg/webp/svg/ico/mp4/woff2/pdf, 512KB max each decoded). Without it you can only write text, and a site with no images looks unfinished. To ADD an image from a text prompt WITHOUT sending any bytes (a real photo's base64 is too big to emit reliably), use generate_image — Kleap generates it and stores it for you. To REMOVE a page or asset, use delete_files — overwriting it with empty content leaves a URL that answers 200 with nothing, which is worse than a 404. DATA & ACCOUNTS: write_files only STORES files — it cannot provision the Kleap Database, so DB or auth code pushed here has no backend and silently does nothing. Stand the feature up with modify_app first, then edit those pages here. After writing, call publish_app to build & go live.

  Connector

  Destructive

  OAuth

• write_filesA

  KleapA

  Write WHOLE files DIRECTLY — YOUR model generates the code, Kleap stores, builds and deploys it as-is. To change something in a file that ALREADY EXISTS, use edit_files instead (read_files → edit_files): it replaces just the lines you name, while write_files makes you retype the entire file and silently drops whatever you leave out — on a 30KB shared layout that is how headers and footers get wiped. No Kleap-AI step, so what ships is byte-for-byte what you wrote — the right choice when a phrase, a URL or a schema must be exact. Publishing still audits the result (see publish_app). Best for scaffolding exact pages/components — e.g. programmatic-SEO routes. Astro paths (src/pages/*.astro, src/data/*.json, src/components/*.astro, public/*). Overwrites by path. NPM PACKAGES: do not write package.json (the build replaces it) — the build installs whatever your code IMPORTS, so `import { jsPDF } from "jspdf";` is all it takes. Supported on import: @tiptap/*, jspdf, pdf-lib, html2canvas, papaparse, file-saver, jszip, @ffmpeg/*, howler, wavesurfer.js, browser-image-compression, react-dropzone, recharts, chart.js, d3, @tanstack/*, react-hook-form, three, @react-three/*, leaflet, maplibre-gl, gsap, framer-motion, zustand, date-fns, react-markdown, axios, socket.io-client, radix-ui/*, next-themes, lucide-react, @tabler/*, openai, @ai-sdk/*; anything else is refused at build with a message naming it. A client-side router is never the answer — a route is a FILE (src/pages/about.astro → /about). IMAGES AND BINARIES: set encoding:"base64" on the file and send the bytes — that is how you put a logo, a photo, an OG image, a favicon or a font on the site (png/jpg/webp/svg/ico/mp4/woff2/pdf, 512KB max each decoded). Without it you can only write text, and a site with no images looks unfinished. To ADD an image from a text prompt WITHOUT sending any bytes (a real photo's base64 is too big to emit reliably), use generate_image — Kleap generates it and stores it for you. To REMOVE a page or asset, use delete_files — overwriting it with empty content leaves a URL that answers 200 with nothing, which is worse than a 404. DATA & ACCOUNTS: write_files only STORES files — it cannot provision the Kleap Database, so DB or auth code pushed here has no backend and silently does nothing. Stand the feature up with modify_app first, then edit those pages here. After writing, call publish_app to build & go live.

  Connector

  Destructive

  No auth

• execute_sqlA

  Google Big QueryA

  Run a SQL query in the project and return the result. Prefer the `execute_sql_readonly` tool if possible. This tool can execute any query that bigquery supports including: * SQL Queries (`SELECT`, `INSERT`, `UPDATE`, `DELETE`, `CREATE`, etc.) * AI/ML functions like `AI.FORECAST`, `ML.EVALUATE`, `ML.PREDICT` * Any other query that bigquery supports. Example Queries: ```sql -- Insert data into a table. INSERT INTO `my_project.my_dataset`.my_table (name, age) VALUES ('Alice', 30); -- Create a table. CREATE TABLE `my_project.my_dataset`.my_table ( name STRING, age INT64); -- DELETE data from a table. DELETE FROM `my_project.my_dataset`.my_table WHERE name = 'Alice'; -- Create Dataset CREATE SCHEMA `my_project.my_dataset` OPTIONS (location = 'US'); -- Drop table DROP TABLE `my_project.my_dataset`.my_table; -- Drop dataset DROP SCHEMA `my_project.my_dataset`; -- Create Model CREATE OR REPLACE MODEL `my_project.my_dataset.my_model` OPTIONS ( model_type = 'LINEAR_REG' LS_INIT_LEARN_RATE=0.15, L1_REG=1, MAX_ITERATIONS=5, DATA_SPLIT_METHOD='SEQ', DATA_SPLIT_EVAL_FRACTION=0.3, DATA_SPLIT_COL='timestamp') AS SELECT col1, col2, timestamp, label FROM `my_project.my_dataset.my_table`; ``` Queries executed using the `execute_sql` tool will always have the default job label `goog-mcp-server: true` automatically set in addition to any custom `labels` provided in the request. Queries are charged to the project specified in the `project_id` field. Query Execution Behavior: * If the query completes within the synchronous timeout (default 20 seconds or custom `timeout_ms`), the tool returns `job_complete: true` and the initial result rows directly. For fast queries, `job_id` may be omitted as no persistent background job is created; no further action or polling is needed. * If the query takes longer than `timeout_ms`, the tool returns `job_complete: false` and a `job_id`. In this case, use the `get_query_results` tool with `job_id` to poll until `job_complete: true`, or use `cancel_job` to abort the running query. * You can optionally specify `timeout_ms` to configure the maximum synchronous wait time in milliseconds (defaults to 20,000 ms), and `job_timeout_ms` to enforce a hard server-side timeout after which BigQuery automatically terminates the job.

  Connector

  Destructive

  No auth

• how-toA

  Fast.ioA

  Built-in product help — ask a natural-language "how do I…" question about Fastio and get a grounded, product-aware answer (or a short clarifying question) back in one call. EXPLAIN-ONLY / ADVISORY: it returns GUIDANCE TEXT and performs NO platform action (it will not create shares, move files, or change anything) — read the guidance, then act with the other tools. Answers are grounded in Fastio's own how-to knowledge AND phrased in terms of these MCP tools — they name the concrete `<tool> action="…"` calls to make — so prefer this over guessing endpoints or burning exploratory calls. For Q&A over YOUR uploaded files (RAG) use the `ai` tool instead — `how-to` answers questions about Fastio ITSELF. FREE and requires only an authenticated user (no org, no plan gate, no billing). Call action='describe' for the full action/param reference.

  ConnectorNo auth

• create_workspaceA

  DockA

  Create a new workspace in the caller's org. Works for both user and agent callers; agent-created workspaces attribute to the agent and enroll the agent's owning user as a co-owner so the human sees it in their dashboard. The new workspace is seeded with one primary surface matching `mode`: `doc` → a Notes tab (for prose), `table` → a Sheet tab (for records), `html` → a Mockup tab (sandboxed HTML preview). Decide the surface before you create: prose (briefs, notes, summaries, drafts) → `doc`; records with shared columns (tasks, leads, rows) → `table`; a deliverable that IS html (a page, mockup, dashboard, or visual meant to be seen or shared) → `html`, then write it with `update_html` — never to a local file, which the human can't see. If you omit `mode`, pass `initial_markdown` to signal a `doc`; with neither `mode` nor `initial_markdown`, an agent caller gets a guided error asking it to choose `doc` or `table` (so you never silently land on the wrong surface). An explicit `mode` is always honored. `html` is opt-in — never inferred for ambiguous content — so pass it explicitly when the deliverable is html (a mockup, page, or dashboard the user asked for), and only then. Add more tabs of any kind later via `create_surface`. Agent-created workspaces default to org-visibility so sibling agents in the same org aren't 403'd. For prose content (briefs, summaries, changelogs) pass `initial_markdown` to seed the doc body in one call; the markdown is converted server-side, no need to hand-build ProseMirror JSON.

  ConnectorNo auth

• execute_sqlA

  flootA

  Run a WRITE SQL statement against the project's Postgres database — CREATE/ALTER TABLE, INSERT, UPDATE, DELETE, DROP, migrations. Destructive statements are allowed but your MCP client will show the user the SQL and ask them to approve it (they can allow once or for the session). Schema-changing statements (CREATE/ALTER/DROP of tables, types, …) automatically re-pull the typed schema helper and return the updated schema — no separate pull_database_schema call needed. Pass `database` only if the project has more than one. The query runs in a single transaction by default; set no_transaction for statements that cannot run inside a transaction block (VACUUM, CREATE INDEX CONCURRENTLY, …). Queries are killed after 90 seconds either way.

  Connector

  Destructive

  OAuth

• commuting_allowance_exemptionA

  日本の給与・社会保険・労働法A

  Works out how much of a commuting allowance escapes income tax, and states the amount that still counts as remuneration for social insurance. These are two different bases, and that asymmetry is the part people get wrong. Social insurance counts a commuting allowance in full — it is 報酬 under 健康保険法第3条第5項 regardless of the tax treatment — while income tax is charged only on what exceeds the ceiling. So a 15,000 yen allowance on a 300,000 yen salary makes the standard-remuneration basis 315,000 and the taxable pay 300,000. Never answer with a single figure that is meant to serve both. The ceiling is 150,000 a month for public transport. For a car or bicycle it is set by one-way distance, with nothing exempt under two kilometres, and up to 5,000 more a month when the employee pays for parking. Using both adds them together, still capped at 150,000. Do not answer this from memory. The table moved twice in twelve months: a cabinet order promulgated 19 November 2025 raised every band over ten kilometres and applied retroactively to allowances payable from 1 April 2025, and 1 April 2026 added four bands above 65km along with the parking addition. Figures learnt before those dates are wrong, and wrong in a direction that under-states the exempt amount. Call with no arguments to read the current table and both revisions.

  ConnectorNo auth

• get_league_tableA

  footballcharts-mcpA

  Standings for one league season: one row per team with position, played, W/D/L, goals, points, last-five form, plus expected points and a luck category (how far results run ahead of or behind the underlying numbers). Use for "who is top", "how many points", "what is the form", or any question about the table as ranked by points. view="luck" re-orders the same rows by over/under-performance (who is lucky, unlucky, flattered by the table); view="goals" by scoring. For one team in depth use get_team; for how the season is projected to END use get_season_projection. Omit season for the current one. Example: "Is Hull really a top-four side?" → get_league_table premier, view=luck, compare points with expected_points.

  ConnectorNo auth

• get_componentA

  UploadkitA

  Fetch full metadata plus a ready-to-paste React usage example for one specific UploadKit component. When to use: once you know the exact component name (from list_components or search_components) and need to show the user how to drop it into their code. The returned "usage" field is copy-pasteable TSX including the correct import line and the styles.css import. Returns: JSON { name, category, description, inspiration, usage }. If the name does not match any component, returns a suggestion message with the 5 closest matches. Read-only, idempotent.

  ConnectorNo auth

• delete_tableA

  mcpA

  Delete a table. The request requires the 'name' field to be set in the format 'projects/{project}/instances/{instance}/tables/{table}'. Example: { "name": "projects/my-project/instances/my-instance/tables/my-table" } The table must exist. You can use `list_tables` to verify. Before executing the deletion, you MUST confirm the action with the user by stating the full table name and asking for "yes/no" confirmation.

  Connector

  Destructive

  No auth

• get_workspace_schemaA

  DockA

  Return a table surface's column definitions so an agent knows what keys create_row/update_row will accept. Each column has `key` (the field name in row.data), `label` (human-readable), `type` (text | longtext | url | status | owner | date | number), `position`, and, for status/owner columns, the allowed `options`. Empty array on doc-only workspaces; callers should still be able to write rows (columns auto-seed on first write). Multi-surface workspaces accept `surface_slug` to scope to a specific table sheet (use `list_surfaces` to enumerate); omit to fall through to the workspace's primary table surface.

  ConnectorNo auth

• get_notificationsA

  glimindA

  Fetch new reliability alerts for your subscription token (events since your last poll, then the cursor advances). Use this after watch_tool without a webhook to react to outages/recoveries — no public endpoint required. Returns events with tool id, severity, and a link.

  ConnectorNo auth

• project_clicks_from_ctr_curveA

  serp-metricsA

  Project how many clicks a set of query rows would earn at a target position instead of their current position, using a CTR curve THAT YOU SUPPLY. This server deliberately ships no built-in CTR table: every published average-CTR-by-position table is a third-party estimate over someone else's traffic, so applying one silently would disguise a guess as a measurement. Pass your own measured curve (Search Console clicks/impressions by position is the honest source). Positions between curve points are linearly interpolated; positions beyond the curve clamp to the last point rather than extrapolate, and are counted in clamped_row_count. If any row carries observed clicks, the output also reports how far the supplied curve is from your reality.

  ConnectorNo auth

• get_cpln_rulesA

  Control PlaneA

  Returns the Control Plane operating guide — the resource model, how secrets/images/workloads/domains fit together, production-grade defaults, how to verify a change landed, and how to handle failures. Read it once per session before the first create/update/delete, and any time a multi-resource task spans unfamiliar ground.

  ConnectorOAuth

• cogdepot_get_startedA

  cogDepotA

  Explains, in order, how to obtain a cogDepot API key and become able to trade. Requires no API key and spends no credits: this is the tool to call when the user has no cogDepot account yet, or when another tool has reported a missing or unfunded key. Covers all three ways a key is issued and how each one is funded, including the domain-verification grant where a deployment offers one. Returns instructions for a human or agent to follow. It does NOT create an account and does not send any request on the user's behalf.

  ConnectorNo auth