mackorn_market_intel

How attractive is this market, and how do we compare with competitors?

What it answers

返回五维(行业/技术/时效/经济/可操作)评估框架与打分口径、破碎效率核心杠杆链、竞品对标矩阵、优势与差距、客户痛点应对话术。

Input parameters

ParameterTypeDescription
focusstring分析焦点(区域市场/客户类型/竞品品牌等)。
scoresobject

Worked example (real output, not a mock-up)

Run via: local plugin

Input:

{}

Output (first fields):

FieldValue
focusnull
framework[5 项]
scoringnull
leverage_chain[6 项]
competitors.brands[4 项]
mackorn.advantages[6 项]
mackorn.gaps[4 项]
pain_point_playbook[5 项]
price_factors[7 项] ["机型规格","腔型配置(EF/EFX 等特殊腔型价格略高)","电压等级(6600V 高压 vs 380V 低压)","控制要求(AORS 配置等级)"]
price_reference.nh[9 项]
price_reference.ns[4 项]
price_reference.hydraulic_station[3 项]
evidence_discipline[3 项] ["每个维度的结论必须能指到一份可核查的证据(报告页码 / 报价单 / 实测记录)","同一指标出现多来源冲突时,保留两个口径并标注统计边界,不要合并成一个数","禁止把研究样本数字当作标定值:标注时点、样本量、地域口径"]
assumptions[2 项]
warnings[2 项] ["以下为 MACKORN 内部历史参考价格区间(来源 S4),实际报价以最新商务确认为准;汇率、钢材价、电压等级、液压站配置、交货条件均会显著影响最终价格。","未提供 scores,仅返回五维评估框架;请按每维 evidence 取证后再给 1-5 分"]
verdict.decidedfalse
verdict.verdictneed_scoring
verdict.focusnull

How to call it

POST a JSON-RPC 2.0 request to the public MCP endpoint — no installation, no API key:

curl -s https://mackorn.cn/ai/api/mcp.php \
  -H 'Content-Type: application/json' \
  -d '{"jsonrpc":"2.0","id":1,"method":"tools/call",
       "params":{"name":"mackorn_market_intel","arguments":{}}}'

Endpoint: https://mackorn.cn/ai/api/mcp.php · Registry: cn.mackorn/mackorn-cone-crusher · npm: mackorn-cone-crusher

Data sources and limits

Particle-size and flow models are independently implemented from published literature (Whiten 1972; Bond 1952). Vendor parameters come from MACKORN NH/NS tables. Every output carries assumptions[] and warnings[]; unknown values are returned as null, never estimated. Literature parameters must be calibrated against on-site sieve data before use in a formal proposal.

A hydraulic cone crusher is a secondary/tertiary machine: feed above roughly 350 mm is refused.