"A server for finding information on quantitative trading systems" matching MCP tools:

• get_positions

  OneQAZ Trading Intelligence

  Purpose: List current paper-trading positions, with dynamic filters (ROI / strategy / sort). Triggers (casual questions too): "what are you holding?", "current positions?", "뭐 들고 있어?", "what's the exposure / portfolio?", "any winners / losers right now?", "how's the book doing?". Paper-trading positions (NOT real money). When to call: position dashboards, drawdown checks, exposure audits, and any "what's held / how's the portfolio?" question. Prerequisites: market://{market_id}/status recommended for context. Next steps: get_position_detail, get_strategy_distribution. Caveats: paper-trading data only. Positions are not real money holdings. Disclaimer: Information only, not investment advice. Args: market_id: Market ID (crypto, kr_stock, us_stock) min_roi: Min ROI % filter (e.g., -5.0) max_roi: Max ROI % filter (e.g., 10.0) strategy: Strategy filter (e.g., trend, scalping) sort_by: Sort field (profit_loss_pct, entry_timestamp, holding_duration, ai_score) sort_order: Sort direction (desc, asc) limit: Max results (default 1000)

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• system_reliability

  ReliaStats

  Given per-component reliabilities and a structure ('series' or 'parallel'), return the system reliability. Series = product (all must work). Parallel = 1 − product(1−Rᵢ) (at least one works). Useful for back-of-envelope RBD calcs before reaching for full RBD tooling. For mixed-structure systems (series with parallel sub-blocks), call this tool repeatedly on the sub-blocks. ANTI-FABRICATION: exact closed-form. Quote verbatim.

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• wash_check

  Name Whisper — ENS Intelligence Layer

  Assess whether an ENS name's sale(s) are WASH TRADING / fake / self-dealt / manipulated volume. THE tool for any "is this wash trading?", "is the sale history of X suspicious/fake/real?", "are these trades legit?", "is someone wash-trading this name?" question — route straight here, do NOT use get_name_details or get_market_activity for that (those return sale rows but make NO wash-trading judgment; only this tool scores it). Just pass `label` — the bare ENS name (e.g. "437", "coffee") is enough; the tool pulls that name's recent sale and analyzes it on demand. `tx_hash`, `buyer`, `seller`, `price_eth` are OPTIONAL enrichment for a specific sale — never block on them or ask the user for them. Returns a wash confidence score (0-1), a label (clean/suspicious/likely_wash), the detected signals (shared-funder, mint-flip, round-trip, fresh-wallet, cluster overlap…), seller profile, and a plain-English summary. A verdict is always ABOUT A SPECIFIC SALE — `sale_analyzed` names it, and `buyer`/`seller` are the parties scored. If the response has `assessable: false` there is NO score and NO verdict: the name has no analyzable sale on record, or the lookup failed. Report that the name could not be assessed and say why. Do NOT describe it as clean, low-risk, or free of red flags, and do NOT describe unrun checks (funding, cluster, round-trip) as having come back negative.

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• set_wallet_policy

  Crank Protocol

  Configure the caller's own trading limits and rules on an agent wallet. policies: a list of {policy_type, value, enabled?} objects. policy_type is one of max_trade_size {"usd"} | daily_limit {"usd"} | approved_tokens {"tokens"} | banned_tokens {"tokens"} | position_limit {"usd"} | kill_switch {"active"} | drawdown_limit {"max_pct"} | max_daily_loss {"usd"} | trade_velocity {"max_per_hour", "max_per_day"} | venue_allowlist {"venues"}. Upserts by policy_type; returns the full updated policy set. (Perp leverage cap is a separate follow-up, ENG-f3aacdf1 -- not a policy_type here.) On a Lane 2 wallet, a change that would LOOSEN a server-enforced default raises BROWSER_CONFIRMATION_REQUIRED -- call request_wallet_policy_loosening instead (ENG-45e5ea07).

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• get_llm_trading_decisions

  OneQAZ Trading Intelligence

  Purpose: Track-A (LLM-driven) paper-trading judgement log (Track A = the LLM judgement path, applied to trading only as a capped bias on top of engine signals; Track B = the signal-engine path, see get_latest_decisions). Triggers (casual questions too): "what does the AI think?", "AI는 뭘 사라고 해?", "show the LLM's trade calls", "AI 판단 근거 보여줘", "does the AI agree with the signals?". When to call: inspect LLM-generated reasoning and trade calls. Prerequisites: none. Next steps: get_latest_decisions to compare with Track B. Caveats: paper-trading only. Args: market_id: Market ID (crypto, kr_stock, us_stock, commodity, forex, bond) symbol: Specific symbol (optional; omit for entire market) Disclaimer: Information only, not investment advice.

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• get_signal_narratives

  supply-chain-intelligence

  Get plain-language explanations of active predictive signals. Each narrative explains the mechanism behind a signal — why the predictor leads the target, what economic logic connects them, and what the current reading implies. Designed for non-quantitative users who want to understand the 'why' behind each signal without reading F-statistics. Returns trigger context, predictor value, direction, and a narrative paragraph suitable for reports and briefings.

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• pentest_analyze_response

  pentest-mcp-server

  Analyze a server response from authorized probing for information leakage, fingerprinting signals, and exploitation opportunities scoped to the authorized engagement. Accepts raw HTTP response headers and body (paste from Burp, curl, or any HTTP client) and returns structured findings grouped by category. Each finding includes: what was detected, why it matters for an authorized tester, how a defender detects misuse, and recommended remediation. Identifies version disclosures, stack traces, debug headers, internal paths, JWT/cookie patterns, CORS misconfigurations, and other common leakage patterns. Use as the bridge between recon/probing output and the methodology and payload tools.

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• get_dasha

  Jagannatha Hora — Vedic Astrology (Jyotish, Kundli, Horoscope, Panchang)

  Return a dasha (planetary period) timeline as a nested tree anchored on a date — past, present and future in ONE call. Works for BOTH planetary (graha) and sign (rasi) dasha systems; just name the system and the tool routes it automatically. Every period node has the SAME shape: 'level' (maha/antar/pratyantar/sookshma), 'ruler' (planet for graha, sign for rasi), 'start', 'end' (YYYY-MM-DD) and 'relation' (past/current/future). The result has 'dasha_type' ('graha' or 'rasi'), 'system', 'as_of' (anchor date), 'depth', and: 'current' — a ready-made summary of the running period ('maha'/'antar'/'pratyantar', a 'path' string, and 'current_period_ends'); 'maha_timeline' — every Maha-dasha over the life; 'current_maha' with its 'antars'; for graha systems 'current_antar' with its 'pratyantars'; and, at depth 4, 'current_pratyantar' with its 'sookshmas'. Rasi systems have two levels (no pratyantar). To drill into a SPECIFIC period regardless of date, pass 'maha' (and optionally 'antar') as a ruler name — the matching branch comes back under 'selected_maha'/'selected_antar' with a 'selection' echo. HOW TO REQUEST: send only birth details for the default (vimsottari, anchored today, depth 3). Optionally set 'system' to EXACTLY one enum token. Planetary (graha) systems: vimsottari (standard 120-yr), ashtottari, yogini, shodasottari, dwadasottari, panchottari, satabdika, chaturaaseeti_sama, shashtisama, shattrimsa_sama, dwisatpathi, kaala, buddhi_gathi, naisargika, aayu, tara, karaka, tithi_ashtottari, tithi_yogini, karana_chaturaaseeti_sama, saptharishi_nakshathra, rasi_bhukthi_vimsottari, yoga_vimsottari, ashtaka_varga_planet, ashtaka_varga_sign, ashtaka_varga_pinda, moola_graha, rashmi. Sign (rasi) systems: narayana, chara, kendraadhi_rasi, sudasa, drig, nirayana, shoola, kendraadhi_karaka, lagnamsaka, padhanadhamsa, mandooka, sthira, tara_lagna, brahma, varnada, yogardha, navamsa, paryaaya, trikona, kalachakra, chakra, sandhya_panchaka, chathurvidha_utthara, karaka_kendraadhi, lagna_kendraadhi, niryaana, raashiyanka. Optionally set 'as_of_date' (YYYY-MM-DD, separate from birth 'date') to anchor on another time, e.g. '2030-01-01'. Data only — no interpretation.

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• list_skills

  mcp

  START HERE for any open-ended request. Lists the task playbooks this server supports — systematic learning from bookmarks, organising into themes, cleaning up, X-list intelligence, exporting data out, finding a half-remembered save, digests, and diagnosing sync. Each names when to use it; call get_skill for the exact tool sequence.

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• finnhub_stock_quote

  Inferventis MCP Server

  Retrieves real-time stock price quotes and company information for any publicly traded company via the Finnhub API. Returns current price, intraday high and low, percentage change from previous close, previous close price, sector, and exchange. Use stock_quote when an agent needs to look up a stock price, check intraday market performance, retrieve company sector data, monitor equity portfolio values, or answer any question about the current trading price of a publicly listed company. Prefer stock_quote over stock_price_lite when the agent needs price change, intraday range, company name, or sector — stock_price_lite returns only the raw current price with no additional context. Do not use for cryptocurrency prices — use crypto_price (CoinGecko, 10,000+ assets) or crypto_price_lite for a lightweight variant. Do not use for fiat currency conversion — use currency_convert or currency_fx_lite. Requires a Finnhub API key to be configured on the server.

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• general_search

  Nansen

  General search tool. This is your FIRST entry point to look up for possible tokens, entities, and addresses related to a query. Do NOT use this tool for prediction markets. For Polymarket names, topics, event slugs, or URLs, use `prediction_market_lookup` instead. Nansen MCP does not support NFTs, however check using this tool if the query relates to a token. Regular tokens and NFTs can have the same name. This tool allows you to: - Check if a (fungible) token exists by name, symbol, or contract address - Search information about a token - Current price in USD - Trading volume - Contract address and chain information - Market cap and supply data when available - Search information about an entity - Find Nansen labels of an address (EOA) or resolve a domain (.eth, .sol)

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• general_search

  Nansen

  General search tool. This is your FIRST entry point to look up for possible tokens, entities, and addresses related to a query. Do NOT use this tool for prediction markets. For Polymarket names, topics, event slugs, or URLs, use `prediction_market_lookup` instead. Nansen MCP does not support NFTs, however check using this tool if the query relates to a token. Regular tokens and NFTs can have the same name. This tool allows you to: - Check if a (fungible) token exists by name, symbol, or contract address - Search information about a token - Current price in USD - Trading volume - Contract address and chain information - Market cap and supply data when available - Search information about an entity - Find Nansen labels of an address (EOA) or resolve a domain (.eth, .sol)

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• dispatch_quantitative_researcher_async

  Heista

  Dispatch to the QUANTITATIVE RESEARCHER — numerical analysis with full methodology context. Use for: briefs that turn on numbers done rigorously — "what is the documented effect size of X / what does the data say about Y / quantify the impact of Z". Every load-bearing number carries sample frame, sample size, measurement instrument, time window. Often answers with insufficient-evidence when underlying data is thin (negative findings are deliverable). Returns: 4-axis Quantitative summary (Value / Methodology rigor / Effect size / Robustness) + Numerical findings table + Methodology gaps + Sources. NOT for: topic landscapes (use dispatch_desk_researcher) / community language patterns (use dispatch_qualitative_researcher). ASYNC version: returns { job_id } immediately, the specialist runs durably on a Vercel Workflow (no 300s timeout). Use this version when the specialist is expected to take >90s. Call get_dispatch_result(job_id) periodically (respect wait_ms_hint in the response) until status === 'completed' or 'failed'. Idempotent: same brief + same org reuses the same job_id, so retries don't fan out duplicate runs.

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• stocktoken_list

  AI Agent Tokenized Stock OS

  AI Agent Tokenized Stock OS: list canonical tokenized stocks (Robinhood Stock Tokens), ETFs, USDG, and WETH on Robinhood Chain ID 4663. Use for AI agents trading tokenized equities/RWAs. Do NOT use for US brokerage equities (use Robinhood Trading MCP). Only registry addresses are real tokenized stocks.

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• search_statistics

  Fodda Topic & Trend Research

  HARD NUMBERS only: specific figures, market sizes, growth rates, and quantitative data points across Fodda's knowledge graphs. Each result links back to the expert trend it supports. Use when a question asks for a number or statistic — try this BEFORE supplemental data tools, as Fodda's experts may have already curated the answer. For expert quotes, editorial analysis, and narrative interpretation, use search_insights instead. Works on ALL graphs — domain, expert, and report. Search multiple graphs for best coverage. Price: $0.50 per search.

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• tengu_snapshot

  tengu-firm

  Live price snapshot for one ticker: latest price plus basic trading stats. Call it when the user asks 'where is X trading right now?' or needs a current quote before any single-name analysis. Heavy endpoint — fetch one ticker per call.

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• tengu_v3_tape_bars

  tengu-firm

  Intraday minute bars for one equity from FIRM's own market-data capture (~10.5k tickers, extended hours included). Use when an agent needs REAL intraday price/volume history — how a ticker traded through an event, VWAP context, or a finer-grained chart than daily bars. Archive begins 2026-05-10; pass date=YYYY-MM-DD (or start+end, max 5 trading days). 1m is the native grain; 5m/15m/1h are resampled server-side.

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• execute_onchain_swap

  Otto Data — Robinhood Chain

  Execute a REAL on-chain swap on Robinhood Chain via the Rialto router, signed by Otto's server-side trading wallet. side=buy spends `usd_amount` USDG to buy the ticker's Hood token; side=sell sells `qty` tokens back to USDG. The server enforces hard guardrails independently of the caller: USDG<->allowlisted-token pairs only, per-trade and daily USD caps, and a slippage cap. On success the matching order_filled/position_closed event (payload mode=onchain, with tx_hash) is written automatically — the position book, performance stats and trade chart update without a separate report_agent_status call. key: the operator's OTTO_AGENT_KEY. agent: gap-trader | narrative-trader. Returns tx_hash, fill qty/price/usd and pnl_bps (sells with a known entry).

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• competition_paper_trade

  Otto Data — Robinhood Chain

  Place a PAPER fill in Otto's trading competition on behalf of the key's owner. No real funds move — the fill is priced server-side at the latest Chainlink price (callers can never supply a price). api_key: the personal otto_pk_ key issued when the wallet joined at ottodata.app/competition. side: buy | sell. qty: token quantity (shares). Include a short `reason` — it shows up in the owner's trade history. Rules enforced server-side: $10,000 starting budget, long-only, max $2,000 notional per trade, max 10 open positions, max 40 trades/day. On a rule violation the response is {"error": "..."} — read it and adjust instead of retrying blindly.

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• list_26_systems

  mythsensus-mcp

  Return the canonical list of 26 ancient divination systems Mythsensus implements (slug, English + Thai name, region, required inputs). Use first when asked "what systems do you support?".

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