Get the Courier SDK installation guide for a specific platform. For client-side SDKs (React, iOS, Android, Flutter, React Native), also generates a sample JWT.
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261,244 tools. Last updated 2026-07-05 11:42
"Developing a Flutter application with embedded React Native" matching MCP tools:
- Generate cinematic video from a text prompt. Uses ByteDance Seedance 2.0 — #1 on the Artificial Analysis text-to-video leaderboard — with synchronized native audio. Async — returns requestId, poll with check_job_status. 480p/720p/1080p, 4-15 seconds, priced per second by resolution (BTC-pegged; native audio free). Pay per request with Bitcoin Lightning — no API key or signup needed. Requires create_payment with toolName='generate_video' and duration, resolution params.Connector
- Use this when the user wants to discover the canonical marketing reporting graph, available sources, supported metrics, supported dimensions, or which connectors are live today. Each source also reports a `passthrough` field describing whether native fields beyond the curated list are accepted (GA4 accepts any native dimension/metric; Search Console accepts any native dimension; Bing is limited to the curated fields). Do not use this for GA4 account discovery or data retrieval.Connector
- Extract the 10-character PAN embedded in a GSTIN (positions 3-12, 1-indexed). Throws if the GSTIN is the wrong length or the embedded PAN is malformed. Does NOT verify the check character — use validate_gstin for that.Connector
- Is this specific multi-package version combo verified to work together? USE WHEN: pinning a stack (next@15 + react@19 + node@22); before recommending a version matrix. RETURNS: {compatible, conflicts[], notes}.Connector
- Find which documentation SETS exist whose NAME matches a substring (e.g. "python" → Python 3.x, "react" → React). Returns doc SETS, NOT their content — this does NOT look up a function/method/API name. To search inside a doc for an entry like "Array.map" or "fetch", use search_index (slug + query).Connector
Matching MCP Servers
- Alicense-qualityDmaintenanceProvides AI agents with accurate, version-aware documentation for React Native, Expo, React Navigation, and Ignite by automatically detecting project dependencies and fetching matching documentation.Last updatedMIT
- AlicenseAqualityCmaintenanceStreamlines React Native CLI project upgrades by providing automated tools to generate detailed diffs and migration guidance between any React Native versions. Uses rn-diff-purge to help developers seamlessly upgrade or downgrade their projects with step-by-step instructions.Last updated41220MIT
Matching MCP Connectors
Page-cited retrieval for embedded docs, datasheets, MISRA, CMSIS, and RTOS references.
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- Create a frontend deployment and get an upload URL. Upload your built frontend as a zip file to the returned URL, then use manage_frontend (action: "start_deployment") to trigger the deploy. Steps: 1. Call this tool to get an upload URL 2. Upload your zip file to the URL (e.g. curl -X PUT "{uploadUrl}" -H "Content-Type: application/zip" --data-binary @frontend.zip) 3. Call manage_frontend (action: "start_deployment") with the returned deployment_id Example: Input: { app_id: "app_abc123", framework: "react-vite" } Output: { deployment_id: "uuid-1234", uploadUrl: "https://...", expiresIn: 900, maxSizeBytes: 104857600 } Prerequisites: - App must exist (use init_app to create) Free plan: 1 deployment per app. Deploying again automatically replaces the previous deployment (no need to delete first). Starter+: unlimited deployments. Framework options: - react-vite: React app built with Vite (zip the dist/ folder) - nextjs-static: Next.js static export (zip the out/ folder) - static: Plain HTML/CSS/JS - other: Any framework that produces static output SPA routing: For SPA frameworks (react-vite, nextjs-static, other), a _redirects file is auto-injected so all routes serve index.html. If your zip already includes a _redirects file, it is preserved. IMPORTANT — Zip file paths must use forward slashes (/), not backslashes (\). On Windows, zips created with built-in tools use backslashes, which causes all files to be served as text/html (breaking JS/CSS with MIME errors). On Windows use Git Bash or WSL to run: cd dist && zip -r ../frontend.zip . Common errors: - RESOURCE_NOT_FOUND: App doesn't exist Idempotency: Not idempotent — creates a new deployment each time (replaces existing on free plan). Your frontend will be deployed to https://<app-name>.butterbase.dev. Next steps: Upload your zip to the returned URL, then call manage_frontend (action: "start_deployment").Connector
- Get a single video by platform + native post_id — your own or a public/analyzed one. 404s if the post isn't owned by you and hasn't been analyzed yet; ingest it first with analyze_post(url).Connector
- Deterministic critique for APPLICATION UI (dashboards, admin panels, SaaS views): runs the app-UI slop rulebook against React/JSX/HTML source (radius chaos, card-in-card, gray-on-gray text, raw palette classes, missing empty/loading/error states, clickable divs, killed focus rings) and, when a Standout app theme is installed, a theme-conformance pass (foreign colors, missing semantic token classes). Returns a 0-100 UI score with a ship verdict and a prioritized fix list. Use after building every view; re-run until the score clears 85. For marketing/landing PAGES use critique_design instead.Connector
- List every React upload component shipped by @uploadkitdev/react with its name, category, one-line description, and design inspiration. When to use: before recommending or scaffolding any UploadKit component, to confirm the exact name exists and to pick the right variant for the user's context (e.g. browse all "dropzone" variants when the user wants a drag-and-drop area). Returns: JSON { count, components: [{ name, category, description, inspiration }] }. Read-only, no side effects, idempotent.Connector
- Public mode returns FS AI RMF framework reference data only — not org-specific scoring. Use when assessing an organization FS AI RMF governance maturity stage or preparing a regulatory AI roadmap presentation. Returns INITIAL, MINIMAL, EVOLVING, or EMBEDDED classification with stage criteria and remediation priorities. Example: EVOLVING stage organizations have documented AI policies but lack systematic model validation — typical gap to EMBEDDED is 18-24 months and 12-15 additional controls. Connect org MCP for org-specific scoring. Source: FS AI Risk Management Framework.Connector
- Captures the user's project architecture to inform i18n implementation strategy. ## When to Use **Called during i18n_checklist Step 1.** The checklist tool will tell you when to call this. If you're implementing i18n: 1. Call i18n_checklist(step_number=1, done=false) FIRST 2. The checklist will instruct you to call THIS tool 3. Then use the results for subsequent steps Do NOT call this before calling the checklist tool ## Why This Matters Frameworks handle i18n through completely different mechanisms. The same outcome (locale-aware routing) requires different code for Next.js vs TanStack Start vs React Router. Without accurate detection, you'll implement patterns that don't work. ## How to Use 1. Examine the user's project files (package.json, directories, config files) 2. Identify framework markers and version 3. Construct a detectionResults object matching the schema 4. Call this tool with your findings 5. Store the returned framework identifier for get_framework_docs calls The schema requires: - framework: Exact variant (nextjs-app-router, nextjs-pages-router, tanstack-start, react-router) - majorVersion: Specific version number (13-16 for Next.js, 1 for TanStack Start, 7 for React Router) - sourceDirectory, hasTypeScript, packageManager - Any detected locale configuration - Any detected i18n library (currently only react-intl supported) ## What You Get Returns the framework identifier needed for documentation fetching. The 'framework' field in the response is the exact string you'll use with get_framework_docs.Connector
- 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.Connector
- Find content entities similar to a given one. For embedded franchises this uses SEMANTIC vector similarity (pgvector) over the enrichment profile — surfacing entities that feel alike even when their tags differ literally. Falls back to shared enrichment-tag overlap for works or non-embedded entities. Each result carries a similarity score and its entity-level freshness/confidence (verifiable, sourced). When to use this tool: an agent wants recommendations or lookalikes for a franchise or work. Input: an entity_id and its type.Connector
- Label by MBID: type (Original Production, Reissue, Imprint, …), country, life span, label code (the LC number), area, aliases, tags, and external links (url-rels — Wikidata, Discogs, official site). A label's releases are a potentially huge linked set (a major label can have tens of thousands), so they are NOT embedded here — enumerate them with musicbrainz_browse_entities (target_type=release, link.label).Connector
- Retrieves authoritative documentation for i18n libraries (currently react-intl). ## When to Use **Called during i18n_checklist Steps 7-10.** The checklist tool will tell you when you need i18n library documentation. Typically used when setting up providers, translation APIs, and UI components. If you're implementing i18n: Let the checklist guide you. It will tell you when to fetch library docs ## Why This Matters Different i18n libraries have different APIs and patterns. Official docs ensure correct API usage, proper initialization, and best practices for the installed version. ## How to Use **Two-Phase Workflow:** 1. **Discovery** - Call with action="index" 2. **Reading** - Call with action="read" and section_id **Parameters:** - library: Currently only "react-intl" supported - version: Use "latest" - action: "index" or "read" - section_id: Required for action="read" **Example:** ``` get_i18n_library_docs(library="react-intl", action="index") get_i18n_library_docs(library="react-intl", action="read", section_id="0:3") ``` ## What You Get - **Index**: Available documentation sections - **Read**: Full API references and usage examplesConnector
- Search the web for any topic and get clean, ready-to-use content. Best for: Finding current information, news, facts, people, companies, or answering questions about any topic. Returns: Clean text content from top search results. Query tips: describe the ideal page, not keywords. "blog post comparing React and Vue performance" not "React vs Vue". Use category:people / category:company to search through Linkedin profiles / companies respectively. If highlights are insufficient, follow up with web_fetch_exa on the best URLs.Connector
- Public mode returns FS AI RMF framework reference data only — not org-specific scoring. Use when assessing an organization FS AI RMF governance maturity stage or preparing a regulatory AI roadmap presentation. Returns INITIAL, MINIMAL, EVOLVING, or EMBEDDED classification with stage criteria and remediation priorities. Example: EVOLVING stage organizations have documented AI policies but lack systematic model validation — typical gap to EMBEDDED is 18-24 months and 12-15 additional controls. Connect org MCP for org-specific scoring. Source: FS AI Risk Management Framework.Connector
- Transcription and chapterization of long-form media (YouTube, podcasts, direct audio/video) for content marketing teams, podcast publishers, edu tech, journalists and accessibility/compliance. Pipeline: • YouTube → timedtext captions (keyless) + oEmbed metadata + native timecode chapters from description • Podcast RSS → episode description + duration + timecodes if embedded in show notes • Direct media → partial (requires Whisper API via OPENAI_API_KEY + force_whisper:true) • Chapters: native YouTube timecodes preferred; heuristic TF-IDF segmentation as fallback • Summary: extractive TF-IDF top-sentences (no LLM required) • Language detection: character-set heuristic (CJK→zh, kana→ja, hangul→ko, accents→fr/de/es) Output formats: json (full structured object) | text (plain transcript) | srt | vtt SLA: ≤15s budget total. Cache: 24h TTL.Connector
- Transcription and chapterization of long-form media (YouTube, podcasts, direct audio/video) for content marketing teams, podcast publishers, edu tech, journalists and accessibility/compliance. Pipeline: • YouTube → timedtext captions (keyless) + oEmbed metadata + native timecode chapters from description • Podcast RSS → episode description + duration + timecodes if embedded in show notes • Direct media → partial (requires Whisper API via OPENAI_API_KEY + force_whisper:true) • Chapters: native YouTube timecodes preferred; heuristic TF-IDF segmentation as fallback • Summary: extractive TF-IDF top-sentences (no LLM required) • Language detection: character-set heuristic (CJK→zh, kana→ja, hangul→ko, accents→fr/de/es) Output formats: json (full structured object) | text (plain transcript) | srt | vtt SLA: ≤15s budget total. Cache: 24h TTL.Connector