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
- Last Tested
- Transport
- Streamable HTTP
- URL
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Managed credentials
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Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 4.9/5 across 1 of 1 tools scored.
With only one tool, there is no possibility of confusion between tools. The single tool has a clear, distinct purpose for DCF valuation.
The single tool name 'run_dcf' uses a verb_noun pattern that is consistent. There are no other tools to create inconsistency.
The server has only one tool, which is on the borderline of being too few. However, the tool is comprehensive and covers the full DCF valuation process, so it earns its place despite the low count.
The single tool covers the entire DCF valuation lifecycle: baseline calculation, user input of assumptions, final valuation with sensitivity matrices, reverse DCF, and optional historical charts. There are no obvious gaps for its stated purpose.
Available Tools
1 toolrun_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：返回按 5 年历史均值计算的基线估值、每个参数的
历史区间，以及 parameter_analysis_guide（资深分析师参数分析指南）。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
| Name | Required | Description | Default |
|---|---|---|---|
| wacc | No | ||
| ticker | Yes | ||
| tax_rate | No | ||
| convergence | No | ||
| ebit_margin | No | ||
| fmp_api_key | No | ||
| revenue_growth_1 | No | ||
| revenue_growth_2 | No | ||
| ronic_match_wacc | No | ||
| include_history_chart | No | ||
| revenue_invested_capital_ratio_1 | No | ||
| revenue_invested_capital_ratio_2 | No | ||
| revenue_invested_capital_ratio_3 | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Thorough disclosure without annotations. Describes two-step behavior, automatic calculation when tax_rate/wacc omitted, output components (per-share value, value bridge, sensitivity matrix, etc.), and API key handling for different markets.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Well-structured with clear sections: overall purpose, two-step usage, parameter units, ticker format, and API key info. Every sentence adds value; no filler. Front-loaded with core workflow.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite 13 parameters, no output schema, and no annotations, the description covers workflow, parameter semantics, ticker formats, and API key handling comprehensively. Only minor gap is not listing all output fields explicitly, but the narrative covers them.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description explains units for most parameters (percentages, multiples, convergence years) and conditions for omitting certain fields. However, not all 13 parameters are individually detailed; some like revenue_growth_1/2 are only named in schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it's a DCF valuation tool for 10-year two-stage FCFF discounting. It specifies the two-step usage and what outputs to expect at each step, leaving no ambiguity about the tool's purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit step-by-step guidance: first call without parameters to get baseline and analysis guide, then second call with assumptions. Also explains when to use include_history_chart for historical visualization. No sibling tools exist, but usage context is clear.
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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