Zyberno stock valuation

Monte Carlo distribution of portfolio outcomes

simulate_portfolio_outcomes

Read-onlyIdempotent

Runs 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.

Input Schema

TableJSON Schema

Name	Required	Description
`years`	Yes	How many years to simulate forward.
`goal_amount`	No	Optional target end value. When set, the result reports the share of simulated paths that finished at or above it.
`simulations`	No	Number of simulated paths. Defaults to 10000. More paths stabilise the tails and cost more time.
`fee_rate_pct`	No	Annual fee drag in percent, for example a fund expense ratio. Defaults to 0.
`current_balance`	Yes	Starting portfolio value in currency units.
`inflation_rate_pct`	No	Annual inflation in percent. When set above 0, results are expressed in today's purchasing power. Defaults to 0.
`monthly_contribution`	No	Amount added every month. Defaults to 0.
`annual_volatility_pct`	Yes	Annual standard deviation of returns in percent. Pass 15 for 15 percent. Broad equity indices have historically run in the mid teens.
`expected_annual_return_pct`	Yes	Expected average annual return in percent. Pass 7 for 7 percent.

TDQS

A4.9/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Despite annotations declaring readOnlyHint and idempotentHint, the description adds material behavioral context beyond them: the assumption-propagation caveat, the normal-distribution understating tail risk, and the explicit statement that output is a property of inputs rather than a prediction. This is rich transparency that complements, not contradicts, the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three paragraphs in logical order: what it returns, when to use vs not use, and a behavioral caveat. No wasted words, and the most decision-relevant information (purpose and outputs) is front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 9-parameter tool with 100% schema coverage and no output schema, the description fully specifies behavior, usage constraints, and limitations. The distributional assumptions and their implications are disclosed. Nothing material is left unexplained.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and the schema already documents all 9 parameters well. The description adds slight value by clarifying the return-distribution assumption (normal, using expected return and volatility), which reinforces parameter intent. However, it does not add per-parameter detail beyond the schema, so it stays near the baseline.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific verb plus resource ('Runs a Monte Carlo simulation over a portfolio balance') and enumerates the outputs (percentiles, mean, median, goal-reach share), clearly distinguishing it from the sibling valuation tools. The 'odds of reaching a target' and 'volatility widens the spread' examples make scope explicit.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides an explicit 'Use when' clause (range/probability questions) and an explicit 'Do not use' list naming alternatives (calculate_dcf_value, get_stock_valuation) and excluded domains (retirement withdrawal, tax, drawdown-sequencing). This is exemplary when-to-use/when-not guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4.6/5.0

Disambiguation5/5

Each tool targets a clearly distinct purpose: single-company valuation (get_stock_valuation), market-level indicator (get_us_market_valuation), custom screening (screen_stocks), published lists (list_flagged_stocks), user-driven DCF arithmetic (calculate_dcf_value), and portfolio simulation (simulate_portfolio_outcomes). Descriptions even cross-reference to prevent misselection, making boundaries crisp.

Naming Consistency4/5

Most tools follow a verb_object snake_case pattern (calculate_dcf_value, get_stock_valuation, get_us_market_valuation, list_flagged_stocks, screen_stocks, simulate_portfolio_outcomes). All are consistent in style, though verbs vary somewhat between 'get', 'list', 'screen', 'calculate', and 'simulate', which is acceptable given the domain diversity.

Tool Count5/5

Six tools appropriately cover single-company analysis, market-level analysis, screening, published lists, DCF calculation, and portfolio simulation. Each tool holds clear territory without redundancy, and six is well within the ideal 3-15 range for a valuation-focused server.

Completeness4/5

The surface covers individual valuation, market valuation, screening, published lists, DCF modeling, and portfolio outcomes—covering the core analytics workflows. Minor gaps include no dedicated sector/index analysis, no price quotes or charts, and no explanation-of-framework tool, but these are explicitly noted as out of scope rather than dead ends, and the documented coverage is coherent for the stated purpose.

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