# MACKORN Hydraulic Cone Crusher Selection [Health: Active]

**Category:** 💻 Developer Tools  
**Repository:** https://github.com/LeifDai/MACKORN-hydraulic-cone-crusher  
**GitHub Stars:** 2  
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**Directory Page:** https://allmcps.com/mcp/mackorn-hydraulic-cone-crusher-selection

## Description
MACKORN hydraulic cone crusher selection and crushing-plant design. 21 tools, callable over HTTP.

## Tools
Capabilities this server exposes over MCP:

- **mackorn_requirement_intake** — extracted_fields[]` (value + source + confidence), `missing[]` (level + question + why), `normalized`, `plant_design`, `cone_selection`, `annual_output`, `derived_recommendations[]`, `assumptions[]`, `warnings[]
- **mackorn_proposal** — 14-section Markdown proposal: project overview · design basis · process flow · equipment selection + bill of materials · technical parameters · electrical & control · environment · civil & layout · supply scope · investment estimate · schedule · **assumptions & sources** · **risks &…
- **mackorn_cone_selection** — candidates[]` ranked: `model`, `series`, `cavity`, `css_mm`, `capacity_tph[]`, `capacity_after_circulating_load_tph`, `headroom_ratio`, `power_kw`, `p80_estimate_mm[]`, `match_score`, `basis` (`S1 detailed table` or `series-interval approximation`), `warnings[]
- **mackorn_plant_design** — total_reduction_ratio`, `stage_count`, per-stage feed/product/reduction ratio, medium & fine cone selection, `screen_area_m2`, `belt_width_mm`, auxiliaries, `assumptions[]
- **mackorn_capacity_check** — capacity range t/h, CSS compliance, max feed limit, dimensions, weight, `basis`, `warnings[]
- **mackorn_mcfm_analysis** — MCFM value, optimum-window verdict (4.0–4.5), deviation, lever-chain adjustment advice
- **mackorn_cost_estimate** — installed power, annual kWh, energy cost per tonne, liner cost per tonne, financing monthly payment, exclusions list
- **mackorn_wear_design** — wear uniformity index, multi-gradient zone hardness H_i, improvement over uniform design
- **mackorn_grading_porosity** — bed porosity φ, optimal-blend comparison, stability verdict
- **mackorn_equipment_catalog** — all NH (9) / NS (4) models with max feed, CSS range, power, weight, capacity; 9 cavity codes and their applicability
- **mackorn_crusher_curve** — product_p80_mm`, `percentiles` (P20/P50/P80), `reduction_ratio`, interlock zone `K1_mm`/`K2_mm`, `sample_curve[]`, `power` (kWh/t), **`parameter_source`**, `calibration_basis`, `mass_balance`, `assumptions[]`, `warnings[]
- **mackorn_simulate_flowsheet** — screen, css_mm / aperture_mm, throw_mm, recirculate_to, screen_efficiency}`), `feed_n`, `bond_wi`, `ore`, `max_iter
- **mackorn_calibrate** — calibrated` (φ/γ/β + interlock factor), `fit` (RMSE in percentage points, max deviation, evaluations, quality verdict), `residuals[]` per point, `library_defaults` for comparison, `how_to_persist`, `assumptions[]`, `warnings[]` (incl. **boundary-detection warning**)
- **mackorn_market_intel** — five-dimension framework & scoring rubric, competitive-benchmark matrix, advantage/gap analysis, customer-pain talking points, quotation factors
- **mackorn_selection_report** — consolidated Markdown report: plant config + cone selection + MCFM + cost + wear, with aggregated assumptions & risks
- **mackorn_intel_watch** — 6 fixed watch items (Sandvik / Metso / China patents / international patents / standards / market), each with *why watch · which sources · search terms · cadence · ingestion format · credibility rubric*
- **mackorn_knowledge_update** — ingestion result, credibility score, numeric-conflict ledger, version-evolution verdict, changelog. **Entries without a source URL are rejected.
- **mackorn_pdca_status** — model version, knowledge revision, entry count & credibility distribution, conflict ledger, four evolution metrics, due watch items, optimization suggestions
- **mackorn_contact** — company name, address (CN/EN), service times, sales contacts, WeChat QR asset, worldwide distributor/agent recruitment programme; optional trigger-coverage report

## Claude Desktop Quick Installation
Heuristic fallback — verify the package name and runner against the repository README before running it. Uses `npx` (confidence: low):

```json
"mcpServers": {
  "mackorn-hydraulic-cone-crusher-selection": {
    "command": "npx",
    "args": ["-y","mackorn-hydraulic-cone-crusher-selection"]
  }
}
```

## Documentation

## What MACKORN Hydraulic Cone Crusher Selection MCP server does

The MACKORN Hydraulic Cone Crusher Selection MCP server provides domain-specific tools for selecting MACKORN NH and NS single-cylinder hydraulic cone crushers and designing crushing and screening plants. It targets metal-mining and hard-rock aggregate scenarios, including applications involving iron, copper, lead-zinc, molybdenum, granite, basalt, and similar materials.

The tool set can turn a customer requirement into normalized inputs, identify missing information, rank crusher candidates, configure plant stages, and produce a consolidated selection report. Outputs can include capacity, CSS, feed limits, reduction ratios, product-size estimates, power, circulating-load effects, assumptions, and warnings. It can also generate a 14-section Markdown proposal covering design basis, process flow, equipment, controls, environment, civil and layout considerations, commercial scope, investment, schedule, sources, assumptions, and risks.

## How it works

The server exposes MCP tools through a stdio interface and is also described as a Node ES module and DeepSeek Harness plugin. An AI client can call individual calculations or combine them into a design workflow. Requirement intake extracts supplied fields with their source and confidence, identifies missing questions, and derives preliminary plant and cone-selection results.

Selection tools use equipment catalog information for NH and NS models, cavity codes, CSS ranges, feed limits, power, weight, and capacity. Plant-design tools calculate stage reductions, medium and fine cone choices, screen area, belt width, and auxiliaries. Simulation supports screen settings, CSS or aperture, throw, recirculation, feed data, Bond work index, ore data, and iteration limits. Calibration compares measured sieve results with model output and reports fit quality, residuals, defaults, and boundary-detection warnings.

The MACKORN Hydraulic Cone Crusher Selection MCP server also includes tools for MCFM analysis, cost estimation, wear design, grading porosity, market intelligence, competitor and standards watch items, sourced knowledge ingestion, and PDCA status tracking.

## Setup and configuration

The repository identifies zero npm runtime dependencies and an MIT license. Use an MCP client that supports a stdio server, configure it with the repository's server entry point according to the project instructions, and provide structured tool inputs from the client or calling application. No required environment variables are specified in the supplied material.

The interfaces and metadata list Chinese, English, Spanish, Brazilian Portuguese, Russian, Arabic, French, German, Japanese, and Indonesian support. Results can include source information, assumptions, confidence values, warnings, and credibility data, so callers should preserve these fields when using outputs in engineering or quotation workflows.

## Tools and capabilities

- Intake and normalize customer requirements, including missing-data questions and derived recommendations.
- Rank crusher models and cavities, check capacity and CSS compliance, and inspect catalog parameters.
- Design multistage crushing plants with screens, belts, auxiliaries, and reduction ratios.
- Simulate product-size distributions and closed-circuit flowsheets using model parameters and mass balance.
- Calibrate model coefficients against field sieve data and report fit statistics and residuals.
- Estimate installed power, energy and liner cost per tonne, financing payments, wear uniformity, and porosity.
- Generate proposals, consolidated selection reports, market analyses, watch plans, and contact information.

## Limitations and notes

The MACKORN Hydraulic Cone Crusher Selection MCP server reports engineering estimates and model assumptions rather than replacing site validation or vendor review. Results may be based on detailed tables or series-interval approximations, and outputs explicitly expose their basis where available. Calibration requires measured data to improve model parameters, while knowledge-update entries without a source URL are rejected.

Users should review warnings, assumptions, exclusions, credibility scores, and numeric-conflict ledgers before relying on a result. Cost outputs include an exclusions list, and the simulator and crusher-curve tools identify parameter sources or calibration bases when available. The supplied material does not define a client-specific authentication method, hosted endpoint, or required environment-variable configuration.

_Full upstream README: https://allmcps.com/mcp/mackorn-hydraulic-cone-crusher-selection/readme_