**Interrogate a quantitative equity model — do not just read its output.** VecViz (https://vecviz.com) publishes daily…
**Interrogate a quantitative equity model — do not just read its output.** VecViz (https://vecviz.com) publishes daily model output for > 700 US tickers: VNA target prices, probability-weighted price bands, a six-horizon V-Score, VecEvents, channel geometry, correlation, risk history, model portfolios and option fair value estimates. **Start with `vecviz_read`** — one ticker, self-sufficient: target, channels, VecEvents, V-Score and which price-probability bands exist. `vecviz_catalog` (free) lists what is covered. ### Two inputs, reconciled The model runs on two independent legs, and the VNA target price is where they're reconciled: * **Price channels** — long-term price history distilled into anchored by major tops and bottoms distilled from long-term price history. Our Vector Set channels mark out support and resistance that we can quantify, and provide a basis for several chart shape metrics. Our ML is trained to predict price volatiltiy scaled in terms of support and resistance traversed. * **Narrative** — VecEvents, an LLM-generated read on the qualitative story behind a name. `vecevent_matrix` returns each VecEvent's contribution to the VNA target, per Vector Set. A price move needs an event that explains it; an event needs a channel to be priced against. Neither leg alone is the model. ### What makes it different Most finance MCP servers wrap quotes and fundamentals. This one exposes the model itself, so an agent can audit a number instead of quoting it: * **Why a target is what it is** — `vecevent_matrix`, per Vector Set. * **What it's built on** — `vector_set_anchors`. * **What it becomes if you disagree** — `vna_scenario` re-prices the target under a changed or added VecEvent. `vecevent_matrix_reviewed` composes several changes at once and reports whether the blend ties out to the sum of the parts. * **Whether a price is reachable** — `price_likelihood` answers "could NVDA reach 250" from five angles: the forecast bands and when each reaches it, which Vector Sets already imply it, how many new VecEvents it would take, what the implied option is worth, and whether the V-Score agrees. ### The narrative layer is LLM-generated — treat it accordingly VecEvents are produced by an LLM, not pulled live from a feed. That means they inherit that model's hallucination risk, and they're bounded by its training window — the narrative can miss anything more recent than the source model's cutoff, or characterize an event wrong. The VNA target reflects VecEvent characterizations **as published at the model date**. If you know something the model date doesn't — an earnings print, guidance, a filing, a changed narrative — don't assume the baseline carries it, and don't lean on your own training data either, which may be just as stale. Apply it explicitly: `vna_scenario` for one change, `vecevent_matrix_reviewed` to compose several. You supply the judgement; every number comes back computed, so render it rather than recompute it. ### Correlation, risk and portfolios * `correlate` — pairwise correlation across 2-40 tickers by three methods: Pearson (trailing 252-day returns), VecEvent (narrative similarity) and Fingerprint (long-term chart-shape metric correspondence). Two tickers with no method named returns all three, which is the honest way to see them disagree. * `diversifiers` — what would most diversify an exposure, ranked on expected upside per unit of downside rather than low correlation alone, which regularly selects the worst name in the universe. * `portfolio` — VecViz model portfolios by strategy, max weight and target volatility, or analysis of weights you supply. Omit the strategy and four variants come back deliberately: they're different readings of the same universe, and one alone reads as "the answer." * `risk_history` — how a ticker's upside and downside bounds have moved, and where today sits in its own range, so you can say "90th percentile of its own history" instead of quoting a bare number. * `model_review` (free) — the published track record: realised bound breaks by ticker, breakage rates, return by cohort, per-variant portfolio performance. The only place "has VecViz been right about this name lately" can be answered. ### Also here Screen 788 tickers or 98 industries on one metric (`vecviz_universe`); chart how any metric has moved and get a PNG URL back (`metric_history`); price an option off the model's own band structure (`option_fair_value`, which cannot say cheap or rich — there is no market IV source here); and read the method behind any of it (`methodology`). Calls are priced per request, so cost is knowable before you spend it. `vecviz_catalog`, `methodology` and `model_review` are free. A subscription includes 50,000 units. One unit per ticker query, 10 per sector query, 25 per portfolio query. Outputs are quantitative research, not forecasts or investment advice. ### Auth An API key starting with `vv_`. * **Free** — ten tickers (AAPL, GLD, JPM, MRK, MSFT, SPY, TLT, TSLA, WMT, XOM), one click at https://agents-vecviz.fly.dev/try, no card. * **Full access** — the whole 788-ticker universe, $39/month, cancel anytime: https://vecviz.com/signup/ ### How to Connect to Claude, Gemini, etc. from Smithery 1) Click "Add to toolbox" and pick the namespace you want the connection to live under. 2) Click "Set up" on the VecViz entry that appears in your Toolbox, and paste in your vv_ key. The connection stays inactive until you do this. 3) Click "Install" and choose your client from the list. Smithery gives you a URL of the form https://mcp.smithery.ai/your-namespace. In Claude.ai (desktop or mobile), go to Settings > Connectors, click Add custom connector, and paste it in. For Claude Code, Smithery will likely give you something like "claude mcp add --transport http your-namespace "https://mcp.smithery.ai/your-namespace" For Gemini CLI, Smithery will likely give you directions to use the following at the command prompt: gemini mcp add --transport http your-namespace "https://mcp.smithery.ai/your-namespace"
vecviz
mcp
free
Others in the same category, ranked by how often they are opened.
