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.
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Average 4.8/5 across 1 of 1 tools scored.
With only one tool, there is no possibility of confusion or overlap between tools, so disambiguation is perfect.
The single tool name 'run_dcf' follows a clear verb_noun pattern, consistent with typical naming conventions.
The server has only one tool, which is somewhat minimal for a valuation service. However, the tool is comprehensive and handles a two-step DCF process, so the count is borderline acceptable but still feels thin.
The single tool covers the full DCF valuation process, including baseline calculations, parameter analysis, final valuation, and charts. However, the server lacks tools for other valuation methods or data retrieval, which might be expected given the name 'ValueScope'.
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 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description carries full burden. It describes complete behavior: two-step output (baseline with guide first, final valuation second), parameter units, auto-calculation for omitted tax_rate/wacc, optional chart output, and authentication handling. No contradictions between description and annotations (none exist).
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?
Description is moderately long but well-structured: overview, two-step usage, parameter units, optional chart, ticker format, key handling. Front-loads essential information. Every sentence adds value, though some detail (e.g., FMP registration link) could be considered auxiliary. Appropriate for the tool's complexity.
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?
Given no output schema and 0% schema coverage, the description is exceptionally complete. It covers purpose, step-by-step usage, parameter semantics, output description (including optional chart), authentication for different markets, and even includes a guide for analysis. An agent can fully understand and invoke the tool correctly.
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?
Schema coverage is 0%, so description compensates well. Explains units for all parameters (percentages, multiples, years) and behavior when omitted (auto-calculation). Describes fmp_api_key and include_history_chart parameters. However, not every parameter is individually named in description; the meaning of revenue_growth_1/2, etc., is implied by units and context, but could be more explicit.
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 the tool performs DCF valuation (10-year two-stage FCFF) in a two-step process. It specifies the resource (DCF valuation) and the verb (run), and distinguishes the tool's workflow from potential alternatives by outlining the two-step approach. No sibling tools mentioned, but the purpose is specific and unambiguous.
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?
Explicitly instructs to use in two steps: first call without assumptions to get baseline and guide, then second call with analyzed assumptions. Provides guidance on when to use the history chart (include_history_chart=true). Also explains when to use FMP key and ticker format. Clear usage context without need for exclusion phrasing since no sibling tools exist.
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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