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466,466 tools. Updated 2026-08-19 15:30

"How to make crypto trades using MetaMask" matching MCP tools:

  • US congressional stock trades (Senate + House) - the "trade like Congress" signal, made agent-callable. Returns recent member stock transactions with ticker, buy/sell direction, dollar-amount range, and both the transaction and disclosure dates, sourced from STOCK Act filings. Pass symbol=<ticker> to filter to trades in one stock (e.g. NVDA); omit for the most recent trades across all members. Note: STOCK Act disclosures are lagged (median ~25 days, up to 45+ by law), so this is a disclosure-based signal, not real-time. Companion to InsiderFlow (corporate insiders). $0.05 via x402.
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  • Convert an amount from one crypto or fiat to another at the current rate, e.g. 'how much is 0.5 BTC in USD', 'convert 100 USDC to EUR'. For a plain coin price without an amount, use getTickersById. Read-only; baseCurrencyId and quoteCurrencyId are canonical ids and amount is the quantity to convert. No API key required.
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  • Latest quote for one ticker — last trade plus the current daily bar. Call this when the user asks what the price is now or how the stock is moving today; for history use /fundamentals/prices. CRYPTO: pass asset_class=crypto for BTC/ETH/SOL/LTC/LINK etc. Several crypto symbols are ALSO US-listed equity tickers (BTC is a Grayscale trust at ~$29; LINK is Interlink Electronics), so a bare ticker returns the EQUITY. Never use an equity price for a crypto asset. Check is_stale before using the price.
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  • Purpose: Aggregate paper trades by day / pattern / symbol. Triggers (casual questions too): "how's the week been?", "이번 주 매매 성적 어때?", "which patterns are working?", "어떤 종목이 제일 잘 벌었어?", "break down the trades", "daily P&L summary?". When to call: pattern audits, period-over-period performance review. Prerequisites: get_trade_history recommended for raw rows first. Next steps: market://{market_id}/signals/feedback for the upstream signals. Caveats: max 30 days; empty result when no trades in the window. Args: market_id: Market ID (crypto, kr_stock, us_stock; aliases coin/kr/us accepted) days: Analysis period in days (default 7, max 30) Disclaimer: Information only, not investment advice.
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  • Purpose: Cursor-paginated bulk export of the prediction -> trade -> outcome chain — paper trades with realized P&L, each linked (best-effort, same-symbol 2h window) to the signal prediction that preceded entry. Built for pipeline consumers who need offline backtesting data, not conversational snippets. Triggers: "give me your full trade history for backtesting", "bulk export trades", "예측이 실제 매매 성과로 이어졌는지 원데이터로 검증하고 싶다", "download outcomes". When to call: offline verification, periodic ingestion into a research pipeline, or auditing whether signals translate into realized outcomes. Prerequisites: none. For the prediction ledger itself use get_resolved_predictions. Next steps: follow next_cursor until has_more=false; get_resolved_predictions to cross-check linked predictions against the tamper-evident ledger. Caveats: linkage is temporal matching, NOT a foreign key (see meta.linkage). Paper trading only — envelope carries the standard disclaimer once per page. Output: full_data { market, trades[] {id, symbol, action, entry/exit price+ts, profit_loss_pct, holding_duration, entry_signal_score, regime fields, policy_version, sizing fields, linked_prediction{...}|null}, count, linked_prediction_count, next_cursor, has_more, meta }. Args: market: "crypto" (default) / "kr_stock" / "us_stock" cursor: last trade id from previous page (0 = start) limit: page size (max 500) days: exit-time window in days (max 120) Disclaimer: Information only, not investment advice.
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  • Visualize a trained model's backtest — a cumulative-return chart + trade log + stats. Use after `one_shot` / `list_models` with the model's `stem` to SHOW the user how it traded (the "is it actually any good" view). In ChatGPT this renders an interactive widget. In Claude, render an interactive **artifact** from this tool's structured output: a line chart of the cumulative return plus a table of the trades. Args: stem: The model stem (e.g. "14_EURUSD_15min_Model_24") from `list_models` / `one_shot`. Returns: dict with: ok, stem, symbol, timeframe, stats {ret, wr, pf, n, mdd, sharpe}, and trades [{type, entry_time, exit_time, entry_price, exit_price, pnl, pnl_pct, exit_reason, period}] (most recent ~200). exit_reason is one of TP / SL / close_only / signal / end. ret/mdd/wr are fractions; pnl_pct is percent.
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    A minimal MCP server with get_weather and create_ticket tools, used for testing MCP servers across protocol, unit, eval, transport, and auth layers.
    MIT

Matching MCP Connectors

  • Stock trades of U.S. Congress & executive-branch officials, with conflict flags. Read-only.

  • Crypto MCP — cryptocurrency prices and currency conversion

  • Count vehicles registered in Texas from the Texas DMV (TxDMV) registration series: total vehicles registered statewide in a fiscal year, split into passenger cars, pickup trucks of one ton or less, and motorcycles, each with its share of the fleet. Answers "how many vehicles are registered in Texas", "how many motorcycles are registered in Texas", "how many pickup trucks are registered in Texas", and growth questions across years such as how the Texas fleet changed from 2001 to 2021. TxDMV publishes this series as one statewide row per fiscal year, covering fiscal years 2001 through 2021, so every response reports its fiscal year and vintage. For a ZIP-code or county breakdown of a registered fleet, ca_dmv_vehicle_registrations covers California at ZIP × make × model-year × fuel grain.
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  • The front door to buying real estate with crypto through RealOpen. Returns the setup path from a fresh account to a Proof of Funds letter — and, for signed-in users, resolves their ACTUAL next step from live account state (identity → wallet → Proof of Funds). Call this when: the user is new, asks what they can do here, or connects without a specific request; the user is considering using crypto for a property purchase; the user asks how to become offer-ready or how to get/verify a proof of funds letter; or a knowledge answer (fees, supported assets, service areas, closing process) leads the user to express clear intent to actually transact. Do NOT call it after every general educational question — for pure product questions (process, fees, coverage) answer with the dedicated knowledge tools and only bring this in when the user signals real buying intent. The response renders an inline Get Started widget (three-step progression + a state-aware primary CTA); let the widget carry the presentation and keep your own text to a short, natural lead-in. The structured activation.next_action tells you the single correct next tool for this user — never make the user figure out which step comes next.
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  • Turn one outreach channel on or off — WhatsApp, SMS or push. This changes how the account actually delivers messages, so it affects live campaigns and autopilot runs, not just future ones. DISABLING a channel silently stops delivery over it; enabling one that has no credentials configured will not make it work. Call get_channels_status first to see where things stand. Handles one channel per call. Safe to repeat: setting a channel to the state it is already in changes nothing. Requires an API key.
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  • Get a humantaste.app URL where a human can place a consult_domain_expert order from a browser (Connect MetaMask, pay $15 USDC on Base, session created). Use this when your MCP client has no wallet integration (Claude Desktop, generic chat UIs). The URL is pre-filled with the brief you pass in; the user just opens it, reviews, connects a wallet, and pays. Returns the payment URL and the price. Free.
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  • 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.
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  • Detect objects in a video segment using text prompts. Describe what to look for and get per-frame detections with bounding boxes and confidence scores. Prompt tips: - Use broad, visual categories: 'animal', 'vehicle', 'person', 'text on screen' - Specific labels ('rabbit', 'Toyota') are less reliable — the detector matches visual patterns, not semantic concepts - Best for confirming whether a category of object appears in a time window, not for precise identification How to pick a time range: - Use search_videos to find WHEN something appears, then pass those timestamps here - Use get_scenes to scan systematically — call segment_video once per scene (scenes typically fit in the 15s window) - Or pass any range you already know Maximum range is 15 seconds per call; for longer spans, make multiple calls with consecutive windows. Does NOT require any feature indexing — works on any uploaded video.
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  • Returns the canonical guide for using TMV from a coding-agent context. Covers the fix-test-retest loop, how to write a good test prompt, how to read the actionTrail / consoleErrors / failedRequests outputs, and common gotchas. Call this first if you're a new agent on a project — it'll save you a debug session. The same content is served at https://testmyvibes.com/docs/coding-agents.
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  • Purpose: Winning paper trades only (P&L > 0). Convenience wrapper around get_trade_history(min_pnl=0.01). Triggers (casual questions too): "what worked?", "뭐가 제일 잘 벌었어?", "show me the winners", "best trades lately?", "수익 난 거래 보여줘". When to call: success-pattern review. Prerequisites: none. Next steps: analyze_trades for breakdowns. Caveats: paper-trading data only. Args: market_id: Market ID (crypto, kr_stock, us_stock; aliases coin/kr/us accepted) limit: Max results (default 10) Disclaimer: Information only, not investment advice.
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  • Purpose: Losing paper trades only (P&L < 0). Convenience wrapper around get_trade_history(max_pnl=-0.01). Triggers (casual questions too): "어디서 잃었어?", "show me the losses", "what went wrong?", "worst trades?", "손실 난 거래 뭐야?". When to call: failure-pattern review. Prerequisites: none. Next steps: analyze_trades for breakdowns. Caveats: paper-trading data only. Args: market_id: Market ID (crypto, kr_stock, us_stock; aliases coin/kr/us accepted) limit: Max results (default 10) Disclaimer: Information only, not investment advice.
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  • Purpose: Expose OneQAZ's pre-defined causal hypothesis map. Each macro category (bonds, forex, vix, credit, liquidity, inflation, commodities, energy) is mapped to a target market with lag_hours + sensitivity. Highest-transparency tool — the causal reasoning is visible and measurable. Triggers (casual questions too): "how do rates affect crypto?", "금리가 코인에 어떻게 영향 줘?", "what's your causal model?", "예측 논리가 뭐야?", "which macro drives which market?". When to call: when an AI wants to understand WHY we make certain predictions. Prerequisites: none. Next steps: get_backtest_tuning_state for runtime calibration of these hypotheses. Caveats: static hypothesis only; see tuning state for current adjustments. Args: market_id: Optional target market filter (coin_market, kr_market, us_market) Disclaimer: Information only, not investment advice.
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  • Get a wallet's individual trades on a single chain — DEX swaps (spot) or Hyperliquid perpetual trades, newest first. Wallet-centric companion to `token_dex_trades` (which is token-centric). Chain: one per call ('all'/'evm' unsupported). EVM wallets MUST pass `chain` explicitly (e.g. ethereum, base, arbitrum); non-EVM (e.g. Solana) is auto-detected. Use 'hyperliquid' for Hyperliquid perpetual trades (requires an EVM address). Call once per chain to span multiple networks. Spot columns: Time, Bought / Bought Amount, Sold / Sold Amount, Value USD, Tx Hash. Perp columns: Time, Token, Side, Action, Size, Price, Value USD, Fee USD, Closed PnL, Tx Hash. Sort (`order_by`, asc/desc): timestamp, value. Filter by `valueUsd` range. Example: { "address": "0x1f2f10d1c40777ae1da742455c65828ff36df387", "chain": "ethereum", "dateRange": {"from": "7D_AGO", "to": "NOW"} }
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  • How many trades happened and how much value moved over a window of up to 24 hours, plus how many distinct wallets were involved. Use for how busy the market or a single token is, rather than for the individual trades. blockchain: solana, bnb, base, eth or rh wallet_type: kol, smart or whale (default kol) hours: window in hours, at most 24 (default 1) mint: restrict to one token
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  • Returns market shape / structure signals for a ticker (concentration, venue fragmentation, settlement patterns). Excellent for understanding *how* a market actually trades on-chain. Tickers are prediction-market event tickers (e.g. KXUSNFP-26MAY01). On failure returns a structured {status:"error", kind, retryable, detail} envelope.
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  • Fuzzy-match a vehicle by make (and optional model/year) to confirm it exists and correct spelling before a plan search. Use this when the user's make/model looks misspelled or uncertain.
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