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lolifamily

ashare-mcp

calculate_ddm_valuation

Calculate intrinsic value using the Dividend Discount Model (DDM). Uses historical dividends, current price, and user-provided discount and terminal growth rates to project future dividends and value.

Instructions

DDM (Dividend Discount Model) valuation.

Uses dividCashPsBeforeTax from baostock. Auto-sums semicolon-separated multi-payouts. Current price from latest K-line close. Excludes the current calendar year, whose dividend bucket is usually incomplete mid-year. Buckets are by announcement year, so a prior fiscal year's final payout and the next year's interim can land in the same bucket, distorting dividend_cagr.

forecast_growth caps dividend_cagr at growth_clamp_bounds.max (20%); negative CAGR passes through unchanged. growth_clamped is "exceeded_max" if the cap fired, else null.

Args: code: Stock code. discount_rate: Required rate of return, e.g. 0.10. terminal_growth_rate: Perpetual growth rate, e.g. 0.025. years_back: Years of dividend history to use. forecast_years: Projection period (must be in [1, 20]).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeYes
discount_rateYes
terminal_growth_rateYes
years_backNo
forecast_yearsNo
Behavior5/5

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

With no annotations, the description fully discloses behavioral details: data source, multi-payout handling, year exclusion, bucket distortions, growth cap logic, and growth_clamped field. This provides comprehensive transparency.

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

Conciseness4/5

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

The description is well-structured with clear sections and bullet points, front-loading key information. It is somewhat lengthy but every sentence adds value, balancing detail and clarity.

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?

Given the complexity of DDM, no output schema, and 0% schema coverage, the description is remarkably complete. It explains assumptions, calculation details, and parameter constraints, leaving little ambiguity for the agent.

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 0%, but the description adds meaning for four of five parameters: provides examples for discount_rate and terminal_growth_rate, and a range for forecast_years. It lacks detail for code and years_back, but overall compensates well.

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 clearly states it performs DDM valuation using specific data sources and details the methodology. The tool name itself distinguishes it from siblings like calculate_dcf_valuation.

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

Usage Guidelines3/5

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

The description explains how the tool works (e.g., auto-summing multi-payouts, excluding current year) but does not explicitly state when to use it over alternatives or when not to use it. It implies suitability for dividend-paying stocks but lacks direct 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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