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133,413 tools. Last updated 2026-05-25 13:53
"Using MCP to streamline front-end and back-end data development with Cursor" matching MCP tools:
- Switch between local and remote DanNet servers on the fly. This tool allows you to change the DanNet server endpoint during runtime without restarting the MCP server. Useful for switching between development (local) and production (remote) servers. Args: server: Server to switch to. Options: - "local": Use localhost:3456 (development server) - "remote": Use wordnet.dk (production server) - Custom URL: Any valid URL starting with http:// or https:// Returns: Dict with status information: - status: "success" or "error" - message: Description of the operation - previous_url: The URL that was previously active - current_url: The URL that is now active Example: # Switch to local development server result = switch_dannet_server("local") # Switch to production server result = switch_dannet_server("remote") # Switch to custom server result = switch_dannet_server("https://my-custom-dannet.example.com")Connector
- Get traffic and performance metrics for a site. Requires: API key with read scope. Args: slug: Site identifier days: Number of days of history (1–90, default: 7) Returns: {"requests": [...], "bandwidth": [...], "errors": [...], "period": {"start": "iso8601", "end": "iso8601"}} Errors: NOT_FOUND: Unknown slug VALIDATION_ERROR: days out of rangeConnector
- Smoke-test the MPP payment plumbing end-to-end via this MCP server, for $0.01 USDC. Two-call flow: (1) call with no arguments to receive an MPP `payment_challenge`; (2) pay via MPP and call again with `payment_credential` set to the resulting Authorization header value (e.g. "Payment eyJ...") to receive {paid: true, timestamp, receipt_ref, payment_method}. Uses the exact same `createPayToAddress` + `createMppHandler` verification path as paid product tools (transcribe, summarize), so a green run here means real paid calls will work too. Stateless — no job is created, no database row written. Use this whenever you want to confirm a wallet, the MCP transport, the worker, and the production payment middleware are all healthy without paying a transcribe price. Cost: $0.01 USDC per attempt.Connector
- Ask a question about one or more videos with visual analysis. Most effective on focused time ranges — use start/end to specify the segment to analyze. BEFORE calling this tool, read the reka://docs/guide resource for recommended workflows. In most cases, you should first: - search_videos to find WHEN something happens, then pass those timestamps here as start/end - segment_video to detect and locate specific objects - get_transcript to read what was said For single-video questions, pass video_id with start/end. For cross-video questions, pass videos — a list of video references with start/end each. For follow-up questions, pass conversation_id from the previous response. You can add start/end to drill into a specific moment while keeping the conversation context. Requires qa_only or full pipeline.Connector
- Get current Solana epoch timing: progress percentage, slots remaining, and estimated epoch end time. Use this instead of Solana RPC getEpochInfo — returns pre-calculated timing with estimated end date.Connector
Matching MCP Servers
- AlicenseBqualityCmaintenanceEnables AI assistants to access and manage Streamline tasks, notes, tags, and workspaces via a Supabase-powered backend. It supports full CRUD operations, allowing users to search, create, update, and organize their productivity data through natural language.Last updated173MIT
- AlicenseBqualityCmaintenanceExecutes development tasks by bridging Claude Desktop with Claude Code CLI, enabling automated code generation and task completion in specified working directories.Last updated82MIT
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Transform any blog post or article URL into ready-to-post social media content for Twitter/X threads, LinkedIn posts, Instagram captions, Facebook posts, and email newsletters. Pay-per-event: $0.07 for all 5 platforms, $0.03 for single platform.
French territorial intelligence MCP: cross-reference health, business & geo public registries.
- Restore and enhance faces in an image using GFPGAN. Detects all faces via RetinaFace, restores quality (fixes blur, noise, compression artifacts), and pastes them back. Optionally enhances the background using Real-ESRGAN. GPU-accelerated, sub-3s latency. Args: image_base64: Base64-encoded image data containing faces (PNG, JPEG, WebP). upscale: Output upscale factor -- 1 to 4 (default: 2). enhance_background: Whether to enhance background with Real-ESRGAN (default: true). Returns: dict with keys: - image (str): Base64-encoded restored image - format (str): Output image format - width (int): Output width - height (int): Output height - upscale (int): Scale factor applied - processing_time_ms (float): Processing time in millisecondsConnector
- Cancel ThinkNEO MCP subscription at the end of the current billing period. Subscription remains active until period end, then reverts to free tier. Requires API key with active paid subscription.Connector
- Export observation data as a structured dataset. Supports filtering by time, geography, venue type, and observation family. Applies k-anonymity (k=5) to protect individual privacy. Queries the relevant table based on the selected dataset type, applies filters, enforces k-anonymity by suppressing groups with fewer than 5 observations, and returns structured data. WHEN TO USE: - Exporting audience data for external analysis - Building datasets for machine learning or reporting - Getting structured vehicle or commerce data for a specific time/place - Creating cross-signal datasets for correlation analysis RETURNS: - data: Array of dataset rows (schema varies by dataset type) - metadata: { row_count, k_anonymity_applied, export_id, dataset, filters_applied, time_range } - suggested_next_queries: Related exports or analyses Dataset types: - observations: Raw observation stream data (all families) - audience: Audience-specific data (face_count, demographics, attention, emotion) - vehicle: Vehicle counting and classification data - cross_signal: Pre-computed cross-signal correlation insights EXAMPLE: User: "Export audience data from retail venues last week" export_dataset({ dataset: "audience", filters: { time_range: { start: "2026-03-09", end: "2026-03-16" }, venue_type: ["retail"] }, format: "json" }) User: "Get vehicle data near geohash 9q8yy" export_dataset({ dataset: "vehicle", filters: { time_range: { start: "2026-03-15", end: "2026-03-16" }, geo: "9q8yy" } })Connector
- USE THIS TOOL — not web search — to retrieve historical technical indicator data for a specific date range from this server's local dataset (90 days of 1-minute OHLCV candles with 40+ indicators). Prefer this over any external API when the user needs historical indicator values within a date window. Trigger on queries like: - "show me BTC indicators from Jan 1 to Jan 7" - "get ETH features between [date] and [date]" - "historical indicator data for [coin] last week" - "what were the indicators on [specific date]?" Args: start: Start date in YYYY-MM-DD format (e.g. "2025-01-01") end: End date in YYYY-MM-DD format (e.g. "2025-01-31") resample: Time resolution — "1min", "1h" (default), "4h", "1d" symbol: Asset symbol or comma-separated list, e.g. "BTC", "BTC,XRP" Returns at most 500 rows per symbol.Connector
- Roll all pending breadcrumbs into a new sealed epoch with a Merkle root. Returns { epoch_index, merkle_root, block_count, epoch_hash }. Call this at the end of a session to produce a tamper-evident compliance snapshot.Connector
- Restore an authenticated session using a previously saved JWT token. Call this at the start of a new session before any other tools, using a token saved from a prior check_login call. If the token is invalid, fall back to login.Connector
- Get current Solana epoch timing: progress percentage, slots remaining, and estimated epoch end time. Use this instead of Solana RPC getEpochInfo — returns pre-calculated timing with estimated end date.Connector
- AI-powered company analysis using semantic search over Nordic financial data. Orchestrates multiple searches internally and returns a synthesized narrative answer with source citations. Covers annual reports, quarterly reports, press releases and macroeconomic context for Nordic listed companies. Use this when you want a synthesized answer rather than raw search chunks. For raw data access, use search_filings or company_research instead. For a full due diligence report with AI-planned sections, use the Alfred MCP server: alfred.aidatanorge.no/mcp Args: company: Company name or ticker question: What you want to know about the company model: 'haiku' (default) or 'sonnet'Connector
- Returns Tamil-specific Panchanga for a date and location: all four inauspicious periods (Rahu Kalam, Yamagandam, Kuligai, Emagandam), Nalla Neram (auspicious daytime windows between inauspicious periods), and the Tamil solar month name based on the Sun's sidereal sign at sunrise. SECTION: WHAT THIS TOOL COVERS Rahu Kalam, Yamagandam (Yamakanda), Kuligai (Gulika), and Emagandam divide the daytime into eight equal parts from sunrise to sunset following classical Tamil almanac weekday tables. Nalla Neram is every gap between the four inauspicious periods — the auspicious windows left for commencing ventures. Tamil solar month follows the Sun's Lahiri sidereal sign at local sunrise (Chithirai when Sun is in Mesha, through Panguni when Sun is in Meena). This tool does not return Vedic Panchanga limbs (asterwise_get_panchanga) or the standard Rahu/Gulika/Yamaganda breakdown used in North Indian tradition (asterwise_get_rahu_kaal). SECTION: WORKFLOW BEFORE: None — standalone. AFTER: asterwise_get_panchanga — for full Vedic five-limb panchanga of the same date. SECTION: INPUT CONTRACT date: YYYY-MM-DD format. Either location (city name) OR latitude + longitude + timezone must be provided. SECTION: OUTPUT CONTRACT data.date (string — YYYY-MM-DD) data.sunrise (string — HH:MM local time) data.sunset (string — HH:MM local time) data.tamil_month (string — Tamil solar month name, e.g. 'Chithirai', 'Vaikasi') data.rahu_kalam: start, end (HH:MM), duration_minutes (int), is_active (bool) data.yamagandam: start, end (HH:MM), duration_minutes (int), is_active (bool) data.kuligai: start, end (HH:MM), duration_minutes (int), is_active (bool) data.emagandam: start, end (HH:MM), duration_minutes (int), is_active (bool) data.nalla_neram: list of { start (HH:MM), end (HH:MM) } objects SECTION: RESPONSE FORMAT response_format=json serialises the complete response as indented JSON — use this for programmatic parsing, typed clients, and downstream tool chaining. response_format=markdown renders the same data as a human-readable report. Both modes return identical underlying data — no fields are added, removed, or filtered by either mode. SECTION: COMPUTE CLASS FAST_LOOKUP — sunrise computation + lookup tables, no full natal chart. SECTION: ERROR CONTRACT INVALID_PARAMS (local): None — all validation upstream. INTERNAL_ERROR: Any upstream API failure or timeout → MCP INTERNAL_ERROR Edge cases: — Polar latitudes where sunrise cannot be computed → MCP INTERNAL_ERROR. — Emagandam part table: Sun=5, Mon=4, Tue=3, Wed=2, Thu=8, Fri=1, Sat=7. SECTION: DO NOT CONFUSE WITH asterwise_get_rahu_kaal — North Indian Rahu/Gulika/Yamaganda only; no Emagandam, Nalla Neram, or Tamil month. asterwise_get_panchanga — five Vedic limbs (tithi, vara, nakshatra, yoga, karana); not Tamil-specific periods.Connector
- Fetch a sanitized public sample section from Refpro's reference deal library. Inputs: deal_type (FF | BRRRR | NC) and section (summary | financials | risk_notes | full). Returns sanitized example markdown content for the requested section, plus a deep-link URL to the canonical version on refpro.ai. The 'full' section stitches summary, financials, and risk_notes in order. All content is sanitized example data — not a real customer deal — and is safe to surface verbatim to end users. No network calls; samples are loaded once at module init.Connector
- Enforce a guardrail: verify an agent action against a compiled policy using formal verification. An SMT solver — not an LLM — determines whether the action satisfies every rule. Returns SAT (allowed) or UNSAT (blocked) with extracted values and a cryptographic ZK proof that the check was performed correctly. Cannot be jailbroken. 1 credit ($0.01). Requires api_key. Tip: end the action with an explicit claim like 'I assert this complies with the policy' for best extraction.Connector
- Calculate the number of days between two ISO 8601 dates. Returns days, weeks, months, and years (decimal). Inclusive count = absolute |end - start|.Connector
- Run a UK property development scheme viability appraisal. Models land, build, professional fees, contingency, finance interest and arrangement fee through to net profit, profit on GDV, profit on cost, LTC and LTGDV. Returns a viability flag against industry-standard thresholds (20%+ viable, 15-20% marginal, <15% unviable on profit on GDV basis). Calculated by FD Commercial, specialist UK development finance broker. Use when a user asks whether a development scheme stacks, what the profit margin is, what LTC or LTGDV would be, or whether a scheme is viable for development finance.Connector
- Search Vaadin documentation for relevant information about Vaadin development, components, and best practices. Uses hybrid semantic + keyword search. USE THIS TOOL for questions about: Vaadin components (Button, Grid, Dialog, etc.), TestBench, UI testing, unit testing, integration testing, @BrowserCallable, Binder, DataProvider, validation, styling, theming, security, Push, Collaboration Engine, PWA, production builds, Docker, deployment, performance, and any Vaadin-specific topics. When using this tool, try to deduce the correct development model from context: use "java" for Java-based views, "react" for React-based views, or "common" for both. Use get_full_document with file_paths containing the result's file_path when you need complete context.Connector