"Financial models for trading stocks, puts and calls" matching MCP tools:

• compare

  XFMS — Xpansion Framework Model Source

  Run a live A/B test between 2–5 user-specified models for a stated purpose. NO ranking step — the supplied model_ids ARE the candidate set. Generates 5 representative test queries from the purpose, runs them through every named model in parallel, and returns real cost, latency, and plain-English commentary on who won what. Unknown IDs are dropped with a note; if fewer than 2 IDs resolve, the call refuses. Use this whenever the user names specific models to compare (e.g. 'A/B test X and Y'). For engine-chosen candidates, use `benchmark` instead. Costs more than `rank` (10+ live LLM calls). Free-tier note: when any candidate ends in ':free', the probe is capped at 3 queries (no adaptive expansion) because free-tier rate limits often push longer probes past the deploy's 5-minute ceiling — evidence will be shallower. The commentary surfaces this when it happens.

  Connector

• estimate-llm-cost

  OptimToken

  Cost a workload with EXACT numbers the caller supplies: arbitrary token counts per request and any monthly volume, not just the 10k/100k/1m presets the other cost tools use. Use this for 'about 800 in and 200 out, 4 million calls a month', or to price one named model across every use-case profile. To compare 2-4 named models like for like at a preset volume, use compare-models-side-by-side instead. Provide a model name to get detailed cost breakdowns, or compare costs across all use case presets. Each figure comes twice: list price, and the optimized price achievable with prompt caching and the batch API. IMPORTANT: Report all cost figures EXACTLY as returned. Do NOT add commentary or recommendations beyond the data.

  Connector

• get_available_models

  Gonka Second Opinion (multi-model)

  List all AI models available on Gonka Network with live pricing. Models work as drop-in replacements for OpenAI and Anthropic — same SDK, same API calls. Use this when user asks which model to use or wants alternatives to GPT-4o / Claude. Returns: model IDs (use directly in openai.chat.completions.create), status, USD per 1M tokens. After this: call calculate_savings() to see annual savings with these models.

  Connector

• compare

  XFMS — Model Source

  Run a live A/B test between 2–5 user-specified models for a stated purpose. NO ranking step — the supplied model_ids ARE the candidate set. Generates 5 representative test queries from the purpose, runs them through every named model in parallel, and returns real cost, latency, and plain-English commentary on who won what. Unknown IDs are dropped with a note; if fewer than 2 IDs resolve, the call refuses. Use this whenever the user names specific models to compare (e.g. 'A/B test X and Y'). For engine-chosen candidates, use `benchmark` instead. Costs more than `rank` (10+ live LLM calls). Free-tier note: when any candidate ends in ':free', the probe is capped at 3 queries (no adaptive expansion) because free-tier rate limits often push longer probes past the deploy's 5-minute ceiling — evidence will be shallower. The commentary surfaces this when it happens.

  Connector

• GetCorrelatedStocks

  equibles

  Get the stocks whose daily price returns are most (or least) correlated with one stock — Pearson correlation of daily log returns on comparable raw closes (dividends excluded), computed over the trading days both stocks priced, never on raw price levels. Scope picks the candidate universe: Industry (default) ranks the subject's direct industry peers; Sector widens to sibling industries; Market ranges across the ~1,500 largest listed names and surfaces cross-industry relationships the classification misses (suppliers, commodity proxies). direction=Negative flips the ranking to the strongest inverse movers (hedge candidates). Candidates need a $100M market cap and enough overlapping trading days with the subject; each row reports the observation count behind its coefficient. Use GetStockPrices for the underlying series and the screener for fundamentals-based peer sets.

  Connector

• list_models

  BridgeNode MCP

  List available models and their prices — free, no payment, no authentication required. Read-only: no state changes; data is served from the server's local config, so repeated calls return identical results (idempotent). Accepts no parameters: the input schema is an empty object, and any arguments passed are ignored. Calling it without arguments returns the complete catalog with per-token prices; there is no filtering, pagination, or configuration. Use this tool to inspect models and prices before calling the paid chat_completions tool. Same data as GET /v1/models (§5.2). Do not use it to generate text (use chat_completions) or to estimate a specific request's cost (use get_price_estimate).

  Connector

• 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.

  Connector

• benchmark

  XFMS — Xpansion Framework Model Source

  Run a live A/B test against the engine's TOP 3 PICKS for a stated purpose — the engine chooses the candidates from the full catalog. Generates 5 representative test queries (auto-expands to 10 or 15 if results are too close to call), runs them through the picked models in parallel, and returns real cost, latency, and plain-English commentary on who won what. Use AFTER `pick` or `rank` when the user wants the engine's own picks stress-tested with live data. DO NOT use this when the user has already named specific candidate models — the engine will ignore the names and test its own picks. Use `compare` instead in that case. Costs more than `rank` (15+ live LLM calls).

  Connector

• arena_compare_strategies

  Backtesting Arena

  Which of these strategies performed best on the same data? Run 2–5 strategies against the SAME pair, interval and date range and return per-strategy metrics plus a comparison summary (best by CAGR, best by win-rate, worst by drawdown). Use this when the user asks which of several strategies fits a market — it holds the pair, interval and requested date range fixed, which a series of separate arena_run_backtest calls does not guarantee. What it does NOT equalize is the EVALUATION window: a strategy with a long warmup starts trading later, so compare actual_date_from across the runs and check result.benchmark before ranking by CAGR. For one strategy across many pairs use arena_run_universe_backtest instead. Caveat worth passing on: comparing N strategies and reporting the winner IS multiple testing — the winner’s edge is upward-biased. arena_get_robustness_field puts a counted N on that. Sequential, expect 10–50s. Per-day quota: Pro=20, Power=200. [API Pro tier]

  Connector

• get_market_context

  Helious market intelligence

  START HERE for any 'why is the market doing this' question. One call gives the whole cross-asset board (equities, the Treasury curve, FX, volatility, commodities), every scored economic release behind it, and the Helious desk's read on what is actually driving the session: whether it is event-driven, flow-driven or quiet, the evidence that read rests on, and the regime yields and stocks have actually been trading in. Use this before the individual board tools: it saves five calls, and it characterises the session rather than leaving you to guess a cause. Market data: answers with numbers on Pro and Ultimate.

  Connector

• get_options

  Quantic Finance

  The signed-in user's options positions — the income "wheel" of sold puts and calls — newest first. Each row carries the symbol, put/call type, strike, expiry, contracts, premium collected, fees, status (open/expired/assigned/ closed), net premium, break-even price, annualized premium yield (ratio) and currency. Optionally filter by `symbol` (e.g. "AAPL") and `status` ("open", "history" for everything resolved, or "all"), and/or pass `portfolio` (one of your portfolio names or slugs, case-insensitive — see `list_portfolios`) to narrow to that portfolio; omitted, positions aggregate across all your portfolios (rows then carry a `portfolio` name when you have more than one). Premium is received income alongside dividends. Private to the caller.

  Connector

• benchmark

  XFMS — Model Source

  Run a live A/B test against the engine's TOP 3 PICKS for a stated purpose — the engine chooses the candidates from the full catalog. Generates 5 representative test queries (auto-expands to 10 or 15 if results are too close to call), runs them through the picked models in parallel, and returns real cost, latency, and plain-English commentary on who won what. Use AFTER `pick` or `rank` when the user wants the engine's own picks stress-tested with live data. DO NOT use this when the user has already named specific candidate models — the engine will ignore the names and test its own picks. Use `compare` instead in that case. Costs more than `rank` (15+ live LLM calls).

  Connector

• GetOptionChain

  equibles

  Get the option chain (calls and puts) for a stock for ONE expiration: strike, greeks (delta/gamma/theta/vega), implied volatility, open interest, and the latest daily price. Defaults to the nearest upcoming expiration; pass expiration=YYYY-MM-DD to pick another (use GetOptionExpirations to list them). When the chain is larger than maxResults the contracts nearest the money are returned, so an unfiltered call already lands where strategies trade. Narrow with minStrike/maxStrike and type (call/put) to reach the wings. Each row attributes its last price, day range and volume to its provider-stamped session and attributes open interest to its separate effective date, or marks either date unknown; implied volatility and greeks are the provider's model values computed at fetch time, so repeated calls can return different values. The daily figures are not live quotes. Bid/ask use real-time OPRA on Pro and a 15-minute delayed indicative feed on Plus; Free covers end-of-day data only.

  Connector

• GetLargestShortVolume

  equibles

  Get the stocks with the largest daily short sale volume for a single trading day (defaults to the latest available), from FINRA's daily short sale volume files, sorted by short volume descending. Short % is the share of that day's FINRA-facility (off-exchange/TRF) volume sold short — 40-50% is a normal market-making baseline — NOT short interest (the open short position; use GetShortInterest/GetShortInterestSnapshot for positions and GetShortSqueezeScores for squeeze candidates; use GetShortVolume for one stock's daily history). Pass sortBy=shortPercent with a minTotalVolume floor to rank by short intensity instead of raw size.

  Connector

• nukez_create_file

  nukez-mcp

  Create a file entry and get upload_url + confirm_url for direct upload. The client PUTs raw bytes directly to the upload_url (307-redirects to GCS), then calls nukez_confirm to finalize. Bytes never transit the MCP server. Use this for large files from local/external sources against Cloud Run. For small inline content (<4KB), use nukez_store with data_b64 instead. KEYLESS (hosted) SERVER: this server holds no signing key. A call without `envelope` returns action_required='sign_envelopes' with the exact spec to sign (method, path, ops, body); sign it with your wallet and re-call with envelope=<signed result>. One file per call in envelope mode.

  Connector

• compute.models

  DPX — Institutional Cross-Border Settlement

  List all AI models available through DPX Compute. All models are free-tier (no token cost) — routed via OpenRouter. Returns model IDs, provider, capability strengths, context window, and speed tier. Use this before compute.route to understand what models are available and pick the right one for a task. Free.

  Connector

• stock_quote

  fingers

  LIVE US stock/equity quote from Financial Modeling Prep, by ticker OR company name (e.g. 'AAPL' or 'Apple'): price, % change, market cap, exchange, day + 52-week range, volume. Use for any public-company / stock / ticker price question. This is the stocks equivalent of token_price — NOT for crypto tokens.

  Connector

• list_models

  mcp

  List the AI image, video, music, and sound-effect models available on BudgetPixel with base credit prices and capabilities. Featured models come first with a one-line role hint (when to pick each). Video models are priced per SECOND by resolution; music models are flat per track; sound effects are per second with a 3-second minimum. Prices are base rates — the user's plan discounts and free-model perks apply automatically when generating.

  Connector

• api_info

  x402 Crypto Market Structure

  REST API access for autonomous agents — pricing, quick start, and migration guide. Call this when: building a trading bot, deploying an autonomous agent, hitting the MCP rate limit, or running 24/7 without a human in the loop. The MCP tier (what you're using now) is free via Smithery, rate-limited to 60 calls/minute per IP, and good for testing. The REST API is for production: pay per call in USDC; paid endpoints are rate-limited to 60 calls/minute and 200 calls/hour per wallet. No API key required.

  Connector

• 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.

  Connector