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  3. MACKORN Hydraulic Cone Crusher Selection
M
Health: ActiveRecent health check succeeded.Last checked 10/13/2026, 11:47:49 PM

MACKORN Hydraulic Cone Crusher Selection

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View Repository2 GitHub StarsTotal stargazers on GitHub for the source repository (2 stars).Visit Website
miningcone-crusherplant-designprocess-simulationmcp

MCP tools for MACKORN cone selection, crushing-plant design, simulation, costing, calibration, and technical reporting.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β€” or use 1-click editor setup below.

One-click editor setup isn’t available for this listing yet β€” we don’t have a confirmed install command, and we’d rather show nothing than point your editor at the wrong package or host. Follow the project’s own setup instructions, linked above.

Manual Client & Custom JSON ConfigExpand JSON β–Ύ
No confirmed setup config for this listing yet. We only publish a config block when the install details come from the project itself β€” its README, its docs, or a verified owner. We haven’t found those for MACKORN Hydraulic Cone Crusher Selection, and we’d rather show nothing than a guess you’d paste into your client. Follow the project’s own setup instructions for the current steps.
Install Tool Schemas (19) Directory Badge Claim listing AlternativesπŸ’» More in Developer Tools

Overview

MACKORN Hydraulic Cone Crusher Selection MCP server gives AI clients tools for hydraulic cone crusher sizing and crushing-plant design. It accepts customer requirements, ore and capacity data, then returns equipment candidates, plant configurations, calculations, warnings, and Markdown reports. Additional tools cover flowsheet simulation, field-data calibration, cost and wear analysis, market intelligence, and knowledge updates. Reach for it when evaluating NH or NS cone crushers, designing crushing and screening circuits, or preparing a technical proposal.

Use cases

β€’Select a hydraulic cone crusher model, cavity, and CSS from customer requirements
β€’Design a multistage crushing and screening plant for a target capacity
β€’Simulate product-size distributions and circulating-load behavior
β€’Calibrate crusher models against field sieve-analysis data
β€’Generate a technical proposal and consolidated selection report

Key features

β€’NH and NS crusher catalog lookup and ranked selection
β€’Crushing-plant flowsheet design and capacity checks
β€’Product-size simulation with closed-circuit mass balance
β€’Field-data calibration with fit and residual reporting
β€’Cost, wear, porosity, and MCFM analysis
β€’Markdown proposal and selection-report generation

Capabilities & Tool Schemas (19) ~861 tokensApproximate context cost of this server’s tool schemas (~4 chars/token), before any tool is called. Actual usage depends on your client and model.Self-reported Self-reportedParsed from the repository README, not verified against a live server β€” may be incomplete or out of date.

Inspect callable tools, capabilities, and parameters exposed to AI agents by MACKORN Hydraulic Cone Crusher Selection.

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 Β· design basis Β· process flow Β· equipment selection + bill of materials Β· technical parameters Β· electrical & control Β· environment Β· civil & layout Β· supply scope Β· investment estimate Β· schedule Β· **assumptions & sources** Β· **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

How MACKORN Hydraulic Cone Crusher Selection works

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.

Read the full README β†’View source on GitHub β†’

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Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks β€” not a rating.

GitHub stars
2
Stargazers on the source repository.
Last commit
5d ago
Most recent push to the default branch.
Tools exposed
19
Callable tools this server registers over MCP.

Reviews

No reviews yet β€” be the first to share how this listing worked for you.

Frequently Asked Questions about MACKORN Hydraulic Cone Crusher Selection

MACKORN Hydraulic Cone Crusher Selection is an MCP server that connects AI clients to tools for MACKORN hydraulic cone crusher selection and crushing-plant design. Its main tools intake requirements, rank cone candidates, design plant stages, check capacity, simulate flowsheets, calibrate models with field data, estimate costs, and generate technical reports.

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Technical Specs & Signals

CategoryπŸ’»Developer Tools
More technical detailsExpand β–Ύ
LicenseMIT
ClientsClaude Desktop, Cursor, Cline / VS Code, Windsurf, Claude Code, VS Code (GitHub Copilot), Zed, OpenAI Codex CLI, Gemini CLI, JetBrains AI Assistant, Roo Code, Continue, LM Studio
Last updatedOct 14, 2026
Views0
Unique ViewsTotal visits recorded for this listing page on AllMCPs.
Installs0
Installs & Copy ActionsTotal times users copied install commands or configuration snippets for this server.
GitHub stars2
GitHub Star CountTotal stargazers on GitHub representing community popularity (2 stars).
Last commit5d ago
Last Repository CommitThe most recent commit or push recorded for this server's GitHub repository.Last commit on Oct 8, 2026
47Quality signal: Fair Β· 47/100How this signal is calculated β–Ύ
Server availabilityNot measured

Not scored for repo-hosted servers β€” we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20
Documentation & tools20/30
Adoption & activity5/15
Community engagement0/10

A guidance signal from public completeness & health data β€” not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

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