"Interaction with SAP GUI" matching MCP tools:

• mg_calculator_profit_margin_update

  Marketgenius

  Update one or more fields in the already-open Profit Margin Calculator. ALWAYS call mg_calculator_profit_margin_state first — the user may have changed values manually in the GUI and you must not overwrite them. Pass the version you received from that call (or from the open tool if updating immediately after opening). Only pass the fields the user explicitly asked to change; omit all others. Send all changes in a single call — multiple sequential calls lose intermediate updates (only the last one applies).

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• onyx_solve_captcha

  onyx-mcp

  Solve an image-based text captcha and return the recognized text. Works on standard alphanumeric captchas (web signup forms, login walls, scraping checkpoints). OCR via ddddocr — typical p50 latency 30-80ms, 70-90% accuracy on common captcha fonts. Provide either an image URL we fetch on your behalf, or raw base64 image bytes if you already have them. Use when an agent encounters a captcha mid-task and needs to continue without human intervention. Cheaper and faster than 2captcha for simple image captchas; not designed for reCAPTCHA v2/v3 or hCaptcha (those are interaction-based). (price: $0.003 USDC, tier: metered)

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• mg_calculator_profit_margin_update

  Marketgenius

  Update one or more fields in the already-open Profit Margin Calculator. ALWAYS call mg_calculator_profit_margin_state first — the user may have changed values manually in the GUI and you must not overwrite them. Pass the version you received from that call (or from the open tool if updating immediately after opening). Only pass the fields the user explicitly asked to change; omit all others. Send all changes in a single call — multiple sequential calls lose intermediate updates (only the last one applies).

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• mg_calculator_market_cap_update

  Marketgenius

  Update one or more fields in the already-open Market Cap Calculator. ALWAYS call mg_calculator_market_cap_state first — the user may have changed values manually in the GUI and you must not overwrite them. Pass the version you received from that call (or from the open tool if updating immediately after opening). Only pass the fields the user explicitly asked to change; omit all others. Send all changes in a single call — multiple sequential calls lose intermediate updates (only the last one applies).

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• intel_agent

  TunnelMind Data API

  Probes a domain for known AI agent integration signals: `llms.txt`, `ai.txt`, `/.well-known/ai-plugin.json`, `openapi.json`, `swagger.json`, MCP manifest, MCP SSE endpoint. Returns a score based on the count of signals detected. Use this to assess whether a domain is ready for agent-to-agent interaction. Use this tool when: - You want to know whether a domain exposes an MCP server or OpenAPI spec for agents. - You are cataloguing the AI-agent-ready surface of a set of domains. - You need to decide whether to attempt programmatic API access to a domain. Do NOT use this tool when: - You need tracker/surveillance data about the domain — use `get_domain` instead. - You need the robots.txt AI crawler policy — use `intel_robots` instead. - You need HTTP security posture — use `intel_http` instead. Inputs: - `domain` (query, required): Domain to probe. Returns: - Boolean flags per signal (`llms_txt`, `ai_plugin`, `openapi`, `mcp_manifest`, `mcp_endpoint`, `mcp_sse`). - `agent_surface_score`: integer 0-8, count of signals detected. Cost: - Free. No API key required. Latency: - Typical: 2-5s (parallel probes), p99: 8s.

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• atlas_list_reports

  CareerProof MCP

  List all generated reports with status and summary info. Returns an array of report objects with id, report_type, status, title, and summary. Use the report id with atlas_get_report for details or atlas_download_report to download completed PDFs. Free.

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• crypto_wallet_intel

  mcp-knowledge

  Multi-chain on-chain analytics for crypto trading agents, on-chain analysts, AML/compliance teams and DeFi BD. Covers Ethereum, Base, Polygon, BSC, Arbitrum, Optimism — EVM-compatible addresses only. 5 modes: • wallet_profile — full wallet summary: type (EOA/contract/CEX/protocol), inferred persona (whale/MEV-bot/DeFi-user/hodler…), age, tx count, native balance, ERC-20 count, NFT collections, OFAC sanctions flag • token_flows — ERC-20 inflows/outflows per token on the selected period, priced in USD via CoinGecko • pnl_estimate — FIFO realized + unrealized P&L on the period with confidence rating (high/medium/low) • counterparties — top 20 counterparties ranked by USD volume with CEX/DEX/protocol labels • defi_positions — active DeFi positions detected via Etherscan interaction history (Aave/Compound/Uniswap/Curve/Lido/Balancer/SushiSwap) Signal detection (P0/P1/P2): P0 if OFAC SDN match OR direct Tornado Cash / sanctioned-protocol interaction P1 if >$1M volume on wallet <30 days old OR MEV-bot pattern OR >80% volume on single counterparty P2 informational (CEX wallet, new wallet, no anomaly) Sources: Etherscan family (keyless free-tier, optional API key per chain), DefiLlama (keyless), public EVM RPC (keyless), CoinGecko free tier (keyless). Cache TTL: 5 min (wallet activity evolves fast). Budget: 8s per source. Env vars (all optional, raise Etherscan rate-limit from 1 req/5s to 5 req/s): ETHERSCAN_API_KEY · BASESCAN_API_KEY · POLYGONSCAN_API_KEY BSCSCAN_API_KEY · ARBISCAN_API_KEY · OPTIMISM_API_KEY

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• edit_image

  Sats4AI - Bitcoin-Powered AI Tools

  Edit an image with natural language instructions. Uses Nano Banana 2 — understands context, handles object addition/removal, style transfer, and inpainting. Returns JSON with image URL. Resolution-tiered pricing: 1K=200 sats, 2K=300 sats, 4K=450 sats. Pay per request with Bitcoin Lightning — no API key or signup needed. Requires create_payment with toolName='edit_image' and resolution param.

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• save_note

  anamnese

  Save a new note with learned knowledge or procedures. Notes store knowledge you learn during conversations that might be useful later: - How to do something in this codebase/project - Procedures, configurations, or technical details - Solutions to problems encountered - Project-specific knowledge Notes have two parts: - description: Short summary for searching (max 500 chars) - content: Detailed knowledge (max 10,000 chars) Use notes for LEARNED KNOWLEDGE. Use facts for TRUTHS ABOUT THE USER. Examples: - description: "How to deploy this Next.js project to Vercel" content: "1. Run 'vercel' command... 2. Configure environment variables..." - description: "Database migration process for this project" content: "Migrations are in supabase/migrations/. To apply: npx supabase db push..." SELF-LEARNING (scope="ai_client"): Your persistent memory across conversations. Save a note whenever you learn something worth remembering — don't wait, save as you go. Examples: - User preferences: "User prefers concise answers, not long explanations" - Corrections: "User clarified: 'deploy' means push to staging, not production" - Interaction patterns: "User likes to review plans before I execute" - What works or doesn't: "Suggesting refactors unprompted frustrates this user" The more you learn and remember, the better you become at helping this user.

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• whensport_tdf_getRacePhase

  tour-de-france-2026

  Get current Tour de France race phase. Returns one of: 'pre' (before the first stage, with nextStage + daysUntil), 'live' (a stage is currently running, with currentStage + elapsedPct), 'rest-day' (rest day between stages, with restDay + nextStage), 'between-stages' (off-day between consecutive stages, with lastStage + nextStage), 'finished' (race over, with finalStage).

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• pictify_render_template

  mcp

  Render a saved template with variable substitutions to produce an image or PDF. Templates can be FabricJS canvas designs or HTML — both are rendered the same way via this endpoint. WORKFLOW: 1) Use pictify_list_templates to find a template, 2) Use pictify_get_template_variables to discover its variables, 3) Call this tool with the variable values. Common use cases: OG images with dynamic titles, personalized social cards, product images with prices/descriptions, event banners with speaker info. For rendering the same template with many variable sets, use pictify_batch_render. Returns the hosted image URL (CDN-backed).

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• mg_calculator_dividend_update

  Marketgenius

  Update one or more fields in the already-open Dividend Calculator. ALWAYS call mg_calculator_dividend_state first — the user may have changed values manually in the GUI and you must not overwrite them. Pass the version you received from that call (or from the open tool if updating immediately after opening). Only pass the fields the user explicitly asked to change; omit all others. Send all changes in a single call — multiple sequential calls lose intermediate updates (only the last one applies).

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• query_memory

  VaultCrux Memory Core

  Search the user's conversation memory. Returns ranked results with content, source timestamps, and confidence scores. For KNOWLEDGE UPDATE questions ('current', 'now', 'most recent'): make two calls — one with scoring_profile='balanced' and one with scoring_profile='recency' — then use the value from the most recent source_timestamp. For COUNTING questions ('how many', 'total'): results may not be exhaustive — search with varied terms and enumerate explicitly before counting. If all results score below 0.3, reformulate with synonyms or specific entity names from the question.

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• search_authorities

  What Do They Know?

  Search WhatDoTheyKnow public authorities by name. Returns up to `limit` authorities whose name or short_name contains `query` (case-insensitive). Use the `slug` field with authority_json or build_request_url as the next step. Example: search_authorities("Liverpool") → slug "liverpool_city_council" Then: authority_json with that slug, or build_request_url with it.

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• mg_calculator_market_cap_update

  Marketgenius

  Update one or more fields in the already-open Market Cap Calculator. ALWAYS call mg_calculator_market_cap_state first — the user may have changed values manually in the GUI and you must not overwrite them. Pass the version you received from that call (or from the open tool if updating immediately after opening). Only pass the fields the user explicitly asked to change; omit all others. Send all changes in a single call — multiple sequential calls lose intermediate updates (only the last one applies).

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• list_categories

  opendata-cat

  List all dataset categories and themes with counts per portal. Great first step to discover what data types are available before searching with search_datasets. Returns total datasets, count per portal and category list with counts. No parameters required.

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• mg_calculator_dividend_update

  Marketgenius

  Update one or more fields in the already-open Dividend Calculator. ALWAYS call mg_calculator_dividend_state first — the user may have changed values manually in the GUI and you must not overwrite them. Pass the version you received from that call (or from the open tool if updating immediately after opening). Only pass the fields the user explicitly asked to change; omit all others. Send all changes in a single call — multiple sequential calls lose intermediate updates (only the last one applies).

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• mg_calculator_cagr_update

  Marketgenius

  Update one or more fields in the already-open CAGR Calculator. ALWAYS call mg_calculator_cagr_state first — the user may have changed values manually in the GUI and you must not overwrite them. Pass the version you received from that call (or from the open tool if updating immediately after opening). Only pass the fields the user explicitly asked to change; omit all others. Send all changes in a single call — multiple sequential calls lose intermediate updates (only the last one applies).

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• get_protocol_registry

  MegaChad

  Canonical machine-readable registry of every MEGA Protocol contract: tokens (MEGACHAD, MEGAGOONER), AMM pair (MC/MG), staking (MoggerStaking, JESTERGOONER), governance (Jestermogger, NFTVetoCouncil, Framemogger), emissions (EmissionController), and safety (CircuitBreaker). Includes addresses, proxy/impl, ABIs, known gotchas, and direct links to all agent endpoints. Pull this FIRST for any DeFi interaction.

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• scan_site

  AEO Scanner

  Quick AI visibility scan. Returns three scores: AEO Score (0-100, AI search engine findability), GEO Score (0-100, AI citation readiness), and Agent Readiness Score (0-100, AI agent interaction capability). Also returns AI Identity Card with mention readiness (0-100, predicts how likely AI will mention the brand), detected competitors, business profile (commerce/saas/media/general), and top 5 issues. 67+ checks across 12 categories. Free — no API key needed. Does NOT return per-check details or fix code — use audit_site for full breakdown, fix_site for generated fixes, compare_sites to benchmark against a competitor.

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