ValueScope
Server Details
Standardized DCF valuation engine for stocks (A-shares, Hong Kong, US, Japan). Two-phase analyst workflow via one run_dcf tool: baseline from 5-year historicals, then a final valuation with reasoned assumptions — value bridge, sensitivity matrix, reverse DCF. A-shares & HK need no API key.
- Status
- Healthy
- Uptime
- 100.0% over the last 24 hours
- Last Tested
- Transport
- Streamable HTTP · MCP 2025-11-25
- URL
- ChatGPT
- –
- Claude
- –
TDQS
Scored across 3 tools
Each tool addresses a distinct analytical question: relative historical valuation (get_relative_valuation), composite quality/growth/momentum score (get_score), and intrinsic DCF value (run_dcf). The descriptions explicitly cross-reference one another and clarify when to use each, leaving no meaningful ambiguity.
Two tools follow a clean get_<noun> pattern, while run_dcf breaks the pattern by using run_ to signal a multi-step computation workflow. Naming is still readable and consistently snake_case, with only a minor verb-style deviation.
Three tools form a tightly scoped valuation and analysis suite. Each tool earns its place—relative valuation, quick health score, and full DCF—without redundancy or padding, fitting well within the ideal 3-15 range.
The suite covers the core equity-analysis workflow: relative positioning, composite scoring, and intrinsic value. Minor gaps exist—such as no direct peer-comparable valuation tool—but the descriptions acknowledge this limitation and the overall surface feels complete for its stated purpose.
Available Tools
3 toolsget_relative_valuationAInspect
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。
Inputs: ticker (required), years, fmp_api_key.
get_scoreAInspect
四维综合评分(估值/质量/成长/动量,各 25%)。秒回,不需要假设参数。
用于快速体检一家公司:每个维度 0-100 独立打分后等权合成总分。
- 估值 —— 相对自身历史是否便宜(PE 历史分位、Forward PE、经营 PE)
- 质量 —— 是否经营得好(ROE 水平与趋势、收入与利润率趋势、杠杆、
Piotroski F 分、利润质量)
- 成长 —— 是否在增长(预期 EPS 增速、3 年收入 CAGR、最新收入增速)
- 动量 —— 市场是否在奖励它(12-1 月、6-1 月收益,剔除最近 1 个月)
这是**相对指标的体检,不是内在价值判断**:估值维度用的是 PE 分位而非
DCF。要回答"值多少钱"请用 run_dcf;要回答"这公司整体怎么样"用本工具。
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。
Inputs: ticker (required), fmp_api_key.
run_dcfAInspect
一站式 DCF 估值(10 年两阶段 FCFF 折现),分两步使用:
第一步——不带任何假设参数调用:返回分析材料,含历史财务数据、每个参数的历史
区间、引擎算出的 WACC 与历史平均税率,以及 parameter_analysis_guide(资深分析师
参数分析指南)。**此步不返回估值**——先给一个数字只会锚定你接下来的判断。收到后
请按指南对每个参数做独立分析(若有联网搜索能力,务必先按指南搜索业绩指引与
分析师预期),然后进入第二步。
第二步——带上你分析得出的假设参数再次调用:返回最终估值,含每股内在价值、
与市价差异、价值桥、逐年预测表、敏感性矩阵、反向 DCF(市价隐含假设)。
参数单位:增长率/利润率/税率/WACC 为百分数(10 表示 10%);
revenue_invested_capital_ratio 为倍数(如 2.0);convergence 为收敛年数。
省略 tax_rate/wacc 时由引擎按财报与市场数据自动计算。
include_history_chart=true 时额外返回一张历史趋势图(PNG,2×2:营收与增速、
EBIT 利润率、Rev/IC、再投资额)——用户想看关键假设的历史数据可视化时使用。
ticker 格式:A股 600519.SS / 000333.SZ;港股 0700.HK;美股 AAPL;日股 7203.T。
A股/港股无需 key。美股/日股需要 FMP key:可通过 fmp_api_key 参数传入,或在
MCP 连接配置中设置 X-FMP-Key 请求头;未提供时可使用每日限量的免费体验额度。
FMP 注册(valuescope 优惠码有折扣):
https://site.financialmodelingprep.com/pricing-plans?couponCode=valuescope
Inputs: ticker (required), wacc, tax_rate, convergence, +9 more.
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