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get_relative_valuation

相对估值:当前倍数 + 自身历史分位。秒回,不需要任何假设参数。

回答"这只票相对自己的历史贵不贵"——与 run_dcf 互补而非替代:
run_dcf 算的是内在价值(这家公司值多少),本工具算的是相对位置
(市场现在给它的倍数处于历史什么水平)。两者应互相验证:若 DCF 显示
低估而倍数处于历史高位,必须解释这个矛盾。

返回:
- current —— 当前 P/E、Forward P/E、P/B、P/S、EV/EBITDA
- historical —— P/E 与 P/B 的历史区间和当前分位(百分位越低越便宜)

注意分位只与公司自身历史比较,不含可比公司横向对比;判断行业是否
整体重估时,仍需结合同业倍数。years 可取 1-10,默认 5 年。

ticker 格式同 run_dcf。A股/港股无需 key;美股/日股需要 FMP key 或
使用每日限量的免费体验额度。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yearsNo
tickerYes
fmp_api_keyNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.8/5.0
Behavior5/5

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

The description discloses the fast '秒回' behavior, notes that no assumption parameters are needed, describes the exact output fields, states the historical percentile limitation, and specifies the years range and API key requirements for different markets. Since no annotations are provided, this description carries the full behavioral burden and does so thoroughly.

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 longer than minimal but well structured with a bolded headline, a clear complementary-vs-alternative explanation, bullet-like return sections, and concise usage notes. Every section earns its place, though a sentence or two could be trimmed without losing value.

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 no output schema and no annotations, the description is remarkably complete: it lists the returned current and historical metrics, explains the percentile meaning, covers the cross-company comparison caveat, specifies the years range, and documents API key requirements by market. An agent has enough information to select the tool and invoke it 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 description coverage is 0%, so the description must compensate. It explains that years can be 1–10 with a default of 5, indicates that ticker format follows run_dcf, and clarifies when fmp_api_key is needed (US/Japan) versus not needed (A-shares/HK). It could be slightly more explicit about the exact ticker format and the meaning of an empty fmp_api_key, but it provides substantial meaning beyond the raw schema.

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 identifies the tool as computing relative valuation (current multiples plus the company's own historical percentile) and explicitly answers the question 'is this stock expensive relative to its own history'. It also distinguishes itself from run_dcf by contrasting intrinsic value with relative position, so an agent can tell them apart immediately.

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?

The description explicitly says this tool is complementary to rather than a replacement for run_dcf, and explains when to prefer each: run_dcf for intrinsic value and this tool for historical positioning. It even instructs that the two should be cross-validated, and warns that industry-wide re-rating still requires comparable-company multiples.

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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