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Fair Value Calculator

Stock valuation snapshot

get_stock_preview
Read-onlyIdempotent

Quick Fair Value Calculator snapshot for one stock: valuation status, upside band, quality score and evidence level, without the exact fair value. Use it to check many stocks cheaply; use get_stock_verdict when the user wants the actual fair value. For a signed-in user the stock also has on_watchlist (true or false) and email_alerts (on = Fair Value Calculator emails the user when one of their watchlist stocks changes from on track to review, off = these emails are off for this account (set_watchlist_alerts turns them on), pro_only = these emails are part of Pro). Fields: valuation_status (undervalued = price at least 10% below fair value, overvalued = price at least 10% above fair value, else near_fair_value; no_valuation = covered, but no fair value is shown right now, note says why), upside_band (distance from price to fair value in 10-point bands, e.g. "10_to_20", "above_30", "below_-30"), quality_score (0-100 business quality), evidence_level (how much audited data backs the value), price_as_of (date of the share price the valuation status refers to), calculated_at (date the valuation models last ran for this stock), requested_symbol with listing = other_listing_same_company (present when the answer is about another listing of the same company than the symbol asked for, the listing the website shows for it, e.g. 939.HK answered with the Shanghai listing 601939.SHG; fair value and currency refer to the listing in symbol), deep_link (full analysis page on fairvalue-calculator.com with the fair value history chart and the result of each valuation model with its bear, base and bull case), trend12 (uptrend: true when the price one month ago was higher than twelve months ago, false when not, null when not known), trend12_pct (that change in percent), trend_arrow (arrow on the site: up = rising, sideways = sideways down to -10 percent, down = falling), buy_rules (the three buy rules of the Fair Value Calculator strategy, each true, false or null: fair_value = fair value upside of at least fair_value_min_pct percent, quality = quality score of at least quality_min, measured as in the backtest without the AI Quality points, trend = uptrend, all = all three met; quality_rule = the quality score the rule decided on, present only when it decides differently from quality_score; upside_rule = the fair value upside in percent the rule decided on (fair value before the cycle and dilution adjustment), present only when it decides differently from the shown upside; the strategy only buys stocks with all three), fair_value_adjustment (present together with upside_rule: the shown fair value was adjusted after the model blend, the buy rule decides on the value before; reason = cyclical_peak_year (a record profit year of a cyclical business is not carried forward in full), cyclical_trough_year (a weak year of a cyclical business is not carried forward in full), dilution_new_shares (new shares lower the value per share) or adjusted; before and now = the fair value before the adjustment and the shown fair value, only where the fair value itself is shown), fair_value_last_change (only in the verdict, present when the shown fair value changed by 15 percent or more within the last 30 days: date, before and after (the shown fair value before and right after that change; the current value is fair_value) and reason = new_fiscal_year_figures (the figures of fiscal_year arrived and the value was recalculated from them), cyclical_peak_year, cyclical_trough_year or dilution_new_shares (that adjustment started to apply on this date), calculation_update (an updated calculation went live for all stocks on this date, nothing happened at the company itself) or not_recorded (no cause is on record)). The values are calculated by Fair Value Calculator from its own models and cannot be derived from the other fields; deep_link opens the full analysis page with the fair value history chart and every valuation model. This is a valuation, not financial advice.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "symbol": "<symbol>"
    },
    "name": "get_stock_preview"
  }
}

Input Schema

NameRequiredDescriptionDefault
symbolYesStock symbol in the form TICKER.EXCHANGE as returned by resolve_stock, e.g. AAPL.US, SAP.XETRA, NESN.SW. A bare ticker (AAPL) also works and is resolved like the website search.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior5/5

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

Annotations already declare read-only, idempotent, non-destructive and closed-world behavior, and the description adds substantial context beyond them. It discloses that exact fair value is omitted, that values come from Fair Value Calculator's own models and cannot be derived from other fields, how signed-in status affects the response, and that this is valuation data rather than financial advice.

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

Conciseness2/5

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

The purpose and alternative are front-loaded, but the description then becomes an extremely dense run-on catalog of return fields. It duplicates the deep_link explanation and includes fair_value_last_change, which is explicitly marked as only in the verdict, so not every sentence earns its place for this preview tool.

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?

With no output schema, the description carries the full burden of explaining return values and does so extensively, including field meanings, listing substitution, adjustment reasons and signed-in-only fields. For a tool with many complex return fields, this is complete enough for an agent to interpret the response correctly.

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

Parameters3/5

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

There is only one parameter, symbol, and the schema description already covers its expected format, examples, resolve_stock relationship and bare-ticker fallback at 100% coverage. The prose adds no further symbol-specific semantics, so the high schema coverage baseline of 3 is appropriate.

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 states a specific verb and resource: a quick valuation snapshot for one stock, returning valuation status, upside band, quality score and evidence level without exact fair value. It explicitly distinguishes itself from get_stock_verdict, so an agent can select it without opening the sibling schema.

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?

It gives explicit usage guidance: use this tool to check many stocks cheaply, and use get_stock_verdict when the user wants the actual fair value. It also explains signed-in-user fields and points to set_watchlist_alerts for enabling alert emails.

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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