How do I call this tool?

POST a JSON-RPC 2.29 tools/call request to https://mackorn.cn/ai/api/mcp.php with name "mackorn_cone_selection". No installation is required.

Is the model based on published literature?

Yes. The particle-size and flow models are independently implemented from Whiten (1972) and Bond (1952); the implementation is MIT-licensed and auditable. Vendor data comes from MACKORN parameter tables. Every number carries its source; unknown values return null rather than an estimate.

mackorn_cone_selection — Which cone crusher model, cavity and CSS should I choose for a target throughput?

What it answers

按目标处理量、给料最大粒度、目标产品粒度与工序段，从 NH / NS 中选出机型 + 腔型 + CSS + 台数，给出候选排序、P80 估计与达标判定。

Input parameters

Parameter	Type	Description
`target_tph`	number	目标处理量 t/h（必填，正数）。
`max_feed_mm`	number	进入本段破碎机的最大给料粒度 mm。
`target_product_mm`	number	目标最终产品粒度 mm（用于推断工序段与细度可行性）。
`stage`	string	工序段；auto 由给料/产品粒度自动推断。
`ore`	string	矿石名称与硬度备注（如"花岗岩 f=12-14"）。
`units`	integer	指定并联台数；不填则自动按目标产量推算。
`closed_circuit`	boolean	是否与筛分构成闭路（默认 true）。闭路会按循环负荷放大系数校核。

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

Run via: local plugin

Input:

{
  "target_tph": 500,
  "max_feed_mm": 300,
  "target_product_mm": 20,
  "ore": "granite"
}

Output (first fields):

Field	Value
`request.targetTph`	500
`request.maxFeedMm`	300
`request.targetProductMm`	20
`request.stage`	中碎
`request.ore`	granite
`request.units`	3
`request.closedCircuit`	true
`recommended_units`	3
`per_unit_tph`	166.7
`recommended_cavity_pool`	[2 项] ["EC","C"]
`cavity_pool_basis.by_stage`	[3 项] ["EC","C","MC"]
`cavity_pool_basis.by_feed`	[2 项] ["C","EC"]
`cavity_pool_basis.effective`	[2 项] ["EC","C"]
`cavity_pool_basis.rule`	按给料粒度选腔（MACKORN《NH_NS系列技术参数大全》§4.2）：<50 EF/EFX/EEF；50-100 F/MF；100-200 M/MC；200-350 C/EC；>
`candidates`	[2 项]
`top_pick.model`	NH700
`top_pick.series`	NH 标准型单缸液压圆锥破
`top_pick.cavity`	EC

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_cone_selection","arguments":{"target_tph":500,"max_feed_mm":300,"target_product_mm":20,"ore":"granite"}}}'

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.