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lolifamily

ashare-mcp

get_valuation_metrics

Fetch valuation metrics history and current snapshot for any A-share stock, including PE, PB, PS, and PCF with percentile rankings.

Instructions

Fetch valuation metrics (PE/PB/PS/PCF) history and current snapshot.

Each metric in metrics independently reports current and as_of (the date of its last valid observation), since close and PE/PB/PS can be missing on different days. period.last_trading_date reports the last K-line bar in the window, separate from any metric's as_of.

Each non-close metric also reports positive_mean/positive_median/ positive_min/positive_max plus percentile_pct (0-100 rank of current: a fresh low ~0.4, an all-time high ~100), computed over POSITIVE values only. sample_size is the positive-observation count; percentile_pct is null when current is non-positive.

Args: code: Stock code. start_date: Optional, defaults to 1 year ago. end_date: Optional, defaults to today.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeYes
start_dateNo
end_dateNo
Behavior5/5

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

With no annotations, the description fully discloses behavioral traits: independent metric reporting, as_of dates, percentile computation over positive values, null handling for non-positive current, and period.last_trading_date separate from metric as_of. This is highly transparent.

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 front-loaded with the core purpose and then details behavioral nuances. While somewhat verbose, each sentence adds necessary context, making it appropriately sized.

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?

Despite no output schema, the description thoroughly explains return values (current, as_of, percentile_pct, sample_size, period.last_trading_date) and parameter defaults. It covers all edge cases and behavioral details, making it fully complete for an agent to invoke correctly.

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 an 'Args' section explaining each parameter (code as stock code, start_date defaulting to 1 year ago, end_date defaulting to today). This adds meaningful context beyond the schema titles.

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

Purpose4/5

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

The description clearly states it fetches valuation metrics history and current snapshot (PE/PB/PS/PCF). It uses a specific verb and resource, but does not explicitly differentiate from sibling tools, which are mostly different calculation or data retrieval functions.

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 does not explicitly state when to use this tool versus alternatives. It implies usage by describing the data, but lacks direct guidance on context or exclusions.

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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