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Fair Value Calculator

Show my watchlist

get_watchlist
Read-onlyIdempotent

Read the watchlist saved in the user's Fair Value Calculator account: each stock with its current valuation status, upside band and quality score, plus whether email alerts are on. Works for Free and Pro accounts. For Pro accounts each stock also has review_status (on_track, or review when the price has reached the optimistic fair value, the quality score has fallen below 50 (the minimum of the buy rule), or the stock has been held for five years; there is no moving-average rule) and flagged_for_review lists those symbols. Use it for questions like "how is my watchlist valued" or "which of my stocks should I review". 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 60 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

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_watchlist"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already establish read-only, idempotent, non-destructive, closed-world behavior, and the description adds meaningful context beyond that: Free versus Pro behavior, Pro-only review_status and flagged_for_review, email-alert status, and the explicit note that there is no moving-average rule. It does not cover edge cases such as an empty watchlist or rate limits, so it is not fully comprehensive.

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 opening sentence front-loads purpose, but the body is an extremely long single paragraph with deeply nested clauses and repeated material, including deep_link and the fair value history page appearing twice. Much field detail is needed because there is no output schema, but the lack of structured formatting and the duplication make it substantially less concise than it could be.

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 and a rich return payload, the description carries the full burden and largely meets it by defining valuation_status, upside_band, quality_score, review_status, buy_rules, fair_value_adjustment, fair_value_last_change, and deep_link semantics. The exact top-level response shape is implied rather than formally specified, but the substantive field-level completeness is strong.

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?

The tool has zero input parameters, so there is no parameter schema semantics for the description to supplement. The baseline score of 4 applies because the description does not need to explain input parameters and correctly focuses on returned fields instead.

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: it reads the watchlist saved in the user's Fair Value Calculator account and enumerates the per-stock valuation fields returned. It also frames the read-only nature clearly enough to distinguish it from mutation siblings such as add_to_watchlist, remove_from_watchlist, and set_watchlist_alerts.

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

Usage Guidelines4/5

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

It gives explicit usage examples ('how is my watchlist valued', 'which of my stocks should I review') and notes that it works for Free and Pro accounts, with Pro-only review fields. It does not name alternatives for single-stock valuation or alert changes, so it stops short of full 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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