Zyberno stock valuation

Brina Gap valuation, screener and market score for US-listed stocks.

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What it can do

    What data it sees

    Do you need an account

    No: the server works without sign-in

    Brina Gap valuation, screener and market score for US-listed stocks. SEC data, free, no key.

    How to connect

    How to connect Zyberno stock valuation to Claude

    The server address to paste into Claude settings:

    https://api.zyberno.com/mcp

    1. Open Claude (claude.ai or the desktop app).
    2. Go to Settings → Connectors.
    3. Click Add custom connector.
    4. Paste the server address copied below into Remote MCP server URL and click Add.
    5. In a chat, click + → Connectors and switch the new connector on.

    Custom connectors are available on Free, Pro, Max, Team and Enterprise plans (Free is limited to one). On Team and Enterprise an organization Owner adds the connector first under Organization settings → Connectors.

    Authorization

    After you click Add, a sign-in window for the service opens. Sign in with your own account and approve access. Claude never sees your password.

    How to connect Zyberno stock valuation to ChatGPT

    The server address to paste into ChatGPT settings:

    https://api.zyberno.com/mcp

    1. Open ChatGPT in a browser (chatgpt.com). A Plus, Pro, Business, Enterprise or Edu plan is required.
    2. Turn on developer mode once: Settings → Apps → Advanced settings → Developer mode.
    3. Open Settings → Connectors and click Create.
    4. Fill in the form: Name (anything), Description (one line about what the service does), MCP server URL (copy it below).
    5. Under Authentication choose OAuth if the service requires sign-in, otherwise None. Click Create.
    6. In a new chat open + → Apps/Connectors and enable the connector.

    OpenAI has renamed this section before (Connectors → Apps/Plugins). If the label differs, search settings for "developer mode". On Business/Enterprise workspaces an admin must allow custom connectors first.

    Authorization

    On first use ChatGPT opens the service's sign-in window. Sign in and approve access.

    How to connect Zyberno stock valuation to Cursor

    The server address to paste into Cursor settings:

    https://api.zyberno.com/mcp

    Fastest: click Open in Cursor below and confirm the prompt.

    Manually:

    1. In Cursor open Settings → Cursor Settings → MCP and click Add new global MCP server.
    2. Paste the JSON copied below into ~/.cursor/mcp.json (per project: .cursor/mcp.json in the repo root).
    3. Save the file. The server appears in the MCP list; authorize it there if asked.

    Authorization

    If the service needs sign-in, an authorize button appears next to the server in the MCP list.

    Server tool list (6)

    Raw names from tools/list. Only developers need these.

    calculate_dcf_valueRuns a two-stage discounted cash flow on numbers you provide and returns the fair value per share, the margin of safety against the price you gave, and how much of the value sits in the terminal stage. Cash flow grows at your growth rate for the stage-one years, then forever at your terminal rate, with the terminal value from the Gordon Growth Model discounted back over the stage-one years. Use when the user wants to value a company under their own assumptions, test how sensitive a valuation is to the discount or growth rate, or check the arithmetic of a DCF they are building. Do not use it to look up what a company is worth on Zyberno's own assumptions, which is get_stock_valuation and uses a different, fade-based model; the two will not agree and are not meant to. This computes your assumptions, it does not endorse them. The output is arithmetic, and a two-stage DCF is highly sensitive to the discount and terminal rates, so treat a single result as one point rather than an answer.
    get_stock_valuationReturns Zyberno's stored valuation record for a single US-listed company: Margin of Safety against a discounted cash flow estimate, the Brina Gap (the growth rate the business can fund from its own returns minus the growth rate its current price implies), the Brina Matrix quadrant those two produce together, the Zyberno quality score, and trailing twelve month fundamentals from SEC filings. Use when the user names a company or ticker and wants to know how it is valued. This is also the tool for questions phrased without the framework's vocabulary: what growth rate is priced into a stock, what the market expects from a company, what a reverse DCF on it implies, whether its growth expectations look too high or too low, or whether a cheap-looking stock is actually a value trap. Do not use it to find or rank companies (use screen_stocks), to retrieve the published monthly lists (use list_flagged_stocks), or for price quotes, charts, news, analyst ratings, earnings dates, or non-US listings, none of which this server holds. Records refresh on a rolling cycle of roughly two weeks per company, so figures can lag a company's newest filing for part of that cycle. Every response states the date its data was computed.
    get_us_market_valuationReturns the Zyberno Market Valuation Score, a single 0 to 100 reading of how expensive the US equity market is relative to its own history, combining the Buffett Indicator, the Shiller PE, the 10 year minus 2 year Treasury curve and the Baa credit spread. Use for questions about the market as a whole, overall valuation levels, or whether US stocks broadly look expensive or cheap right now. Do not use it for any individual company, sector, or index price level, none of which it measures. The score measures present valuation against history. It is not a forecast and not a market timing signal.
    list_flagged_stocksReturns the two lists Zyberno publishes each month from one fixed screen. The first holds companies where the Margin of Safety and the Brina Gap both read as undervalued, labelled Double Discount. The second holds companies that look cheap on trailing earnings power while the Brina Gap reads negative, labelled value trap. Takes no parameters: the thresholds, sector exclusions and ranking are fixed and match the published page. Use when the user asks what the framework currently flags, or asks about the Double Discount or value trap lists by name. Do not use it for custom criteria (use screen_stocks) or for one named company (use get_stock_valuation). Both labels describe where a company sits on two valuation measures at one moment. They are screen output, not recommendations, and neither label forecasts a return.
    screen_stocksSearches Zyberno's database of roughly 10,000 US-listed companies and returns those matching the numeric criteria you supply, ranked by a field you choose. All metrics are computed from SEC filings. Use when the user wants to find, filter, rank, or count companies by their characteristics rather than asking about one company by name. Omit any filter you do not want applied; omitting all of them ranks the whole covered universe. Do not use it for a single known ticker (use get_stock_valuation), and do not use it to reconstruct the published monthly lists, which apply their own fixed thresholds, sector exclusions and curation (use list_flagged_stocks). Returns at most 25 companies plus the total number that matched, so a query that is too broad or too narrow reports its own breadth.
    simulate_portfolio_outcomesRuns a Monte Carlo simulation over a portfolio balance and returns the distribution of possible end values: percentiles, mean, median, and if you give a goal amount, the share of simulated paths that reached it. Returns are drawn from a normal distribution using the expected return and volatility you supply. Use when the user asks about the range or probability of outcomes rather than a single projected number, for example the odds of reaching a target, or how much volatility widens the spread. Do not use it to value a company (use calculate_dcf_value or get_stock_valuation), and do not use it for retirement withdrawal, tax, or drawdown-sequencing questions, which it does not model. The simulation propagates the assumptions you give it. Normally distributed returns understate real market tail risk, and the output is a property of the inputs, not a prediction about any real portfolio.