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741,183 tools. Updated 2026-11-04 01:59

"A server for finding information about React Native mobile development framework" matching MCP tools:

  • Returns the build kit for a catalog design system or a generated project. `target` picks the format: `react` returns one shadcn install command for every component and the tokens, `html` returns framework-free token and component CSS for inline canvases, `react-native` and `swiftui` return mobile kits, `shopify` returns Liquid theme files, `dtcg` returns a W3C Design Tokens document, and `store-screenshots` returns slide files and a render script for App Store and Google Play screenshots. `component` narrows the React install to one component.
    ConnectorOAuth
  • The free MIT React Native starter repo: its stack, what it already does, what it deliberately leaves out, and the commands to get it running. Use this when someone is about to start a mobile app project, or asks what to build an Expo or React Native app on top of.
    ConnectorNo auth
  • Discover content franchises within a domain. Two modes: pass `tag` for a precise taxonomy match (every game tagged 'co-op'), or pass `query` for free-text SEMANTIC search powered by pgvector embeddings — finding franchises by meaning ('dark atmospheric games about isolation') even when no literal tag matches. Results are verifiable: tag mode carries tag confidence/corroboration, semantic mode carries a similarity score; both carry entity freshness. When to use: an agent wants a domain-scoped shortlist by tag or by intent. Inputs: a domain plus either a tag or a free-text query.
    ConnectorNo auth
  • Run a CanaryUsers UX scan on a DEPLOYED URL (your live or preview app — not source code). A flock of AI personas evaluates the page and reports where real users would get stuck, with concrete fixes. Returns AI-ready findings you can act on immediately. Use depth='deep' for the thorough scan that renders the page, checks it VISUALLY on desktop + mobile (catches mobile breakage and layout issues), and clicks through key flows like signup/checkout (slower, ~60-90s, uses one credit); depth='quick' (default) is a fast static check that does NOT see mobile or visual issues — use 'deep' when the user mentions mobile, layout, or visual problems. IMPORTANT: if this returns status 'running' with a scanId, the findings are not ready yet — wait ~30s, then call get_report_markdown(scanId), repeating until it returns the report. Always fetch and present the findings before stopping, then offer to fix the top issues.
    ConnectorNo auth
  • 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
  • Discover content franchises within a domain. Two modes: pass `tag` for a precise taxonomy match (every game tagged 'co-op'), or pass `query` for free-text SEMANTIC search powered by pgvector embeddings — finding franchises by meaning ('dark atmospheric games about isolation') even when no literal tag matches. Results are verifiable: tag mode carries tag confidence/corroboration, semantic mode carries a similarity score; both carry entity freshness. When to use: an agent wants a domain-scoped shortlist by tag or by intent. Inputs: a domain plus either a tag or a free-text query.
    ConnectorNo auth

Matching MCP Servers

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    Connects AI agents to a running React Native app so they can see the screen, tap and type, and inspect logs, network traffic, and state without any SDK or code changes. Composite tools let an agent run a whole interaction flow in a single call instead of one round trip per tap.
    35
    5 npm
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    MIT

Matching MCP Connectors

  • Drive and test your React Native and Expo apps end-to-end on iOS and Android. Supports real devices, simulators, and emulators. Snapshots, taps, typing, swipes, screenshots, deep links, reloads, builds and runtime errors.

  • City of Mobile GIS — Mobile, Alabama open geospatial data (ArcGIS).

  • Discover content franchises within a domain. Two modes: pass `tag` for a precise taxonomy match (every game tagged 'co-op'), or pass `query` for free-text SEMANTIC search powered by pgvector embeddings — finding franchises by meaning ('dark atmospheric games about isolation') even when no literal tag matches. Results are verifiable: tag mode carries tag confidence/corroboration, semantic mode carries a similarity score; both carry entity freshness. When to use: an agent wants a domain-scoped shortlist by tag or by intent. Inputs: a domain plus either a tag or a free-text query.
    ConnectorNo auth
  • Runs a specific framework against a user's inputs and produces a full analysis: prose explanation + structured fields + a self-contained SVG visualisation. Powered by the Jeda.ai · Visual AI framework knowledge graph. ALWAYS call `recommend_framework` FIRST to obtain a valid framework_id -- it returns the exact namespaced slug to pass here. Framework ids are namespaced by category (e.g. SWOT = "cms_swot-analysis", Porter's 5 Forces = "cms_porters-five-forces"); a bare slug like "swot-analysis" will 404. REQUIRED post-processing on success: the response's first text block contains a `curl -fsSL` save-directive for the SVG. Save the file locally and surface it as a file chip — do NOT summarise the visual in prose. Input: framework_id (namespaced slug from `recommend_framework`) + user_inputs object (per-framework field set), OR a free-text `raw_prompt` fallback. Example: { "framework_id": "cms_swot-analysis", "user_inputs": { "subject": "Q3 EU market entry", "context": "B2B SaaS, $5M ARR, US-headquartered" } }
    ConnectorNo auth
  • Discover the investment-thesis catalog. Each entry is a descriptive case study that pairs an economic framework with a rule-based portfolio and the synthetic + historical stress evidence for that allocation. Returns one compact summary per thesis (slug, title, one-liner, tags, risk tiers, framework summary, headline finding). Call get_investment_thesis(slug) for the full framework / portfolio / stress evidence, or read the thesis://{slug} resource. Descriptive, not advisory — the agent decides what is suitable.
    ConnectorNo auth
  • Submits the organisation profile and contact details for an Australian AI governance framework. The profile determines which legislation the framework identifies, so the answers should reflect the organisation's actual circumstances — turnover in particular, since the Privacy Act's small business threshold sits at $3 million and several categories are caught regardless of turnover. Takes the session ID from start_australian_ai_governance_framework together with the questionnaire answers. Writes the profile against the session and stores the supplied name, email and organisation as a contact record. contact.organisation is printed as the document's "Prepared for" heading, so it should be the organisation's name as it should appear on the document rather than a shorthand. Returns the session ID and a status of profile_saved — it does not return the framework, which is retrieved by get_ai_governance_framework. The profile can be re-submitted on the same session: it is overwritten rather than duplicated, the contact record is keyed on the email address, and any framework already generated for that session is discarded. No authentication, and no charge at this step.
    Connector
    Destructive
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  • Returns the organization's development standards: coding conventions, project structure, and framework-specific rules. Read-only. Call it before writing or reviewing code, so the result follows this organization's rules rather than general defaults. Call it first without a section to get an index of available sections, each with a note on what it covers, then call again with one section id copied from that index; inventing a section id returns a not-found error naming that step. Request only the sections a task needs - the full content of one section can be long. The framework argument is deprecated: use section with the "framework:" prefix instead. It returns prose rules, not data - use get_style_tokens for visual values and get_component for component APIs.
    ConnectorNo auth
  • How many jobs mentioning a skill were posted recently, and whether that is rising or falling week by week. Use it to decide whether a skill is worth positioning around, or to check a hunch that a market is drying up. `skill` matches the title, the skills list and the inferred technical skills of a posting; it is a loose match, so "react" also counts "React Native". `window` is one of "week", "month" or "quarter" and sets both the total and how many weekly buckets come back — any other value is refused rather than silently defaulted. Returns `jobs_posted` for the whole window and `weekly`, a list of buckets oldest first, so a trend is visible without a second call. DO NOT COMPARE THE BUCKET COUNTS DIRECTLY. Weeks are cut on Mondays and the window is not, so the newest bucket holds only the days elapsed so far and the oldest holds only the tail of the week the window opened in. Both are low for a calendar reason and no other, which reads as a rise and a fall that did not happen. Every bucket therefore carries `days_covered`, `partial`, and `per_day` — the per-day rate is the comparable number. `trend` is computed from complete weeks only; prefer it, and if you quote a bucket marked `partial`, say that it is. `trend.direction` is 'rising', 'falling', 'flat', or 'unknown' when there are fewer than two complete weeks — 'unknown' means the window is too short to judge, not that demand is flat. A COUNT over public postings — no rows, no buyers, no ids, nothing that identifies a client. It answers how much, not who. And it counts POSTINGS, not hiring: a skill can be posted about constantly by clients who never hire. For whether a specific client hires and pays, ask get_buyer_quality.
    ConnectorOAuth
  • How many jobs mentioning a skill were posted recently, and whether that is rising or falling week by week. Use it to decide whether a skill is worth positioning around, or to check a hunch that a market is drying up. `skill` matches the title, the skills list and the inferred technical skills of a posting; it is a loose match, so "react" also counts "React Native". `window` is one of "week", "month" or "quarter" and sets both the total and how many weekly buckets come back — any other value is refused rather than silently defaulted. Returns `jobs_posted` for the whole window and `weekly`, a list of buckets oldest first, so a trend is visible without a second call. DO NOT COMPARE THE BUCKET COUNTS DIRECTLY. Weeks are cut on Mondays and the window is not, so the newest bucket holds only the days elapsed so far and the oldest holds only the tail of the week the window opened in. Both are low for a calendar reason and no other, which reads as a rise and a fall that did not happen. Every bucket therefore carries `days_covered`, `partial`, and `per_day` — the per-day rate is the comparable number. `trend` is computed from complete weeks only; prefer it, and if you quote a bucket marked `partial`, say that it is. `trend.direction` is 'rising', 'falling', 'flat', or 'unknown' when there are fewer than two complete weeks — 'unknown' means the window is too short to judge, not that demand is flat. A COUNT over public postings — no rows, no buyers, no ids, nothing that identifies a client. It answers how much, not who. And it counts POSTINGS, not hiring: a skill can be posted about constantly by clients who never hire. For whether a specific client hires and pays, ask get_buyer_quality.
    ConnectorOAuth
  • Returns the plain-language meaning and step-by-step fix for one Malinois finding. Use it while helping the user fix an issue reported by scan_app, or when they ask what a finding means. Pass the rule_id exactly as scan_app returned it. Read-only, no network, instant.
    ConnectorNo auth
  • 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. $0.02 USDC per call.
    ConnectorNo auth
  • 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.
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  • Recommends business / strategy / risk frameworks for a stated problem. Powered by the Jeda.ai · Visual AI framework knowledge graph (~2,100 frameworks across 19 categories, edge-curated). Use when the user describes a business problem ("customer churn rising", "evaluating market entry", "need to assess vendor risk") rather than naming a specific framework. Returns top-N frameworks ranked by fit, each with a concrete reason citing the specific problem signals matched. Input: just the problem statement is enough. Optional faceted filters (`persona`, `regulation`, `decision_stage`) narrow the candidate set. Set `limit` between 3 and 10 for picker UIs. Pair with `generate_framework_analysis` to actually run a recommended framework against the user's inputs. Example: { "problem_statement": "We need to decide whether to enter the EU SMB market in Q3", "decision_stage": "decide", "limit": 5 }
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  • Returns a numerological reading of a mobile or landline number: the extracted digit string, the digit total, the single 1-9 number it reduces to, the overall vibe that reduced number carries, and the traditional meaning of each distinct digit present. Use this for 'is my phone number lucky' style questions, or when choosing between candidate numbers. It reads a number string only -- nothing about the person -- so for anything tied to a birth date use get_numerology. Read-only deterministic arithmetic over a fixed number table -- no ephemeris, no network lookup of the number, no writes, no auth, at least 30 requests/min/IP per server instance.
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  • STEP 3 (mobile_money only, when initiate_payment returned instruction.action == "submit_otp"). Submits the one-time password the buyer received to authorise the mobile-money charge. REQUIRES the same buyer_delegation_token used for initiate_payment. After submitting, poll get_payment_status until payment_status is "paid"/"processing" or "failed".
    Connector
    Destructive
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  • Maps only stable Tier1 finding identifiers to approved Tier1 services and public resources. Call after a Tier1 score or email-domain check. Do not submit prose, URLs, customer information, or invented identifiers. This tool performs no arbitrary fetching, makes no contact request, changes nothing, and stores nothing.
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  • Discover available product features, API/MCP URLs, versions, content limits, and interpretation boundaries before choosing a content tool. Returns product.capabilities.v1 metadata, without fetching stories or checking upstream freshness. Public and read-only; no credentials or arguments. Use latest_stories for recent records or get_health for endpoint compatibility. The mobile app distribution channel remains separately suspended.
    ConnectorNo auth