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Mingxin MCP Server

Developer ToolsLow Risk10.0MCP RegistryLocalRemote
Free

Server data from the Official MCP Registry

Measured AI-inference-storage benchmarks with citations, article search, KV-cache ROI estimation.

About

Measured AI-inference-storage benchmarks with citations, article search, KV-cache ROI estimation.

Remote endpoints: streamable-http: https://www.mingxinstorage.xyz/api/mcp

Security Report

10.0
Low Risk10.0Low Risk

Valid MCP server (1 strong, 1 medium validity signals). No known CVEs in dependencies. Package registry verified. Imported from the Official MCP Registry. 1 finding(s) downgraded by scanner intelligence.

3 tools verified · Open access · 2 issues found

Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.

Permissions Required

This plugin requests these system permissions. Most are normal for its category.

HTTP Network Access

Connects to external APIs or services over the internet.

env_vars

Check that this permission is expected for this type of plugin.

How to Install & Connect

Available as Local & Remote

This plugin can run on your machine or connect to a hosted endpoint. during install.

Documentation

View on GitHub

From the project's GitHub README.

Mingxin MCP Server

Official MCP server for Mingxin (Tianjin) Semiconductor Equipment Co., Ltd. — signed AI-inference-storage benchmark data, technical article search, and KV-cache tiering ROI estimation.

Every figure this server returns carries a signed test-report citation (R1–R9). The underlying benchmark is open source and reproducible: mingxin-kvcache-bench.

Tools

ToolWhat it does
query_benchmarkSigned FX-series results: throughput +29–40%, TTFT −26–32% (Qwen3-480B class on 8× AMD MI308X), model loading 6.2–9.3× vs NFS; full R1–R9 report list with hosted PDF URLs
search_mingxin_docsSearch Mingxin's published articles on KV cache tiering, NVMe-oF all-flash storage and LLM serving (Chinese + English)
estimate_roiKV-cache tiering ROI estimate for a GPU cluster (faithful port of the reproducible Python model; mid-scenario estimates, not commitments)

Install

Remote (streamable HTTP, no install)

Endpoint: https://mingxinstorage.xyz/api/mcp

Cursor (~/.cursor/mcp.json):

{
  "mcpServers": {
    "mingxin": { "url": "https://mingxinstorage.xyz/api/mcp" }
  }
}

Claude Desktop / VS Code and other stdio-only clients (via this npm package):

{
  "mcpServers": {
    "mingxin": { "command": "npx", "args": ["-y", "mingxin-mcp-server"] }
  }
}

Domain-level discovery

https://mingxinstorage.xyz/.well-known/mcp.json

Honesty & provenance

  • Measured numbers come only from signed/official test reports; vendor specs and estimates are labeled as such.
  • ROI outputs are mid-scenario estimates with stated bands (uplift 29–40% measured; cold-recovery share 10–50% estimated, to be backfilled by pilot measurement).
  • This server exposes only already-public website content; no customer data.

License

MIT

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