Measured AI-inference-storage benchmarks with citations, article search, KV-cache ROI estimation.
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Scored across 3 tools
Each tool maps to a completely distinct task: estimating ROI, querying benchmark results, and searching documentation. There is no functional overlap or ambiguity between them.
All tool names follow a consistent verb_noun snake_case pattern: estimate_roi, query_benchmark, search_mingxin_docs. The naming style is uniform and predictable across the entire set.
With 3 focused tools, the server is compact but well-scoped for its niche purpose of evaluating Mingxin's storage solutions. Each tool serves an essential role and none feel redundant or missing.
The server covers the full evaluation workflow: discover relevant content via docs, validate performance through benchmarks, and quantify value via ROI estimation. No obvious dead ends or critical missing operations exist within the stated domain.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
estimate_roiquery_benchmarksearch_mingxin_docsGPU and LLM inference benchmarks, hardware evidence, deployment recommendations, and launch configs.
Research intelligence for AI coding agents. 2M+ CS papers with evidence and tradeoffs.
Attestable memory for AI agents: curated writes, verifiable tenant isolation, bi-temporal facts.
Engineering log of self-hosted AI on NVIDIA DGX Spark (GB10/SM121A). 60+ articles indexed.
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