经 AI Skill Hub 精选评估，资产运维基准 获评「强烈推荐」。已获得 1.6k 颗 GitHub Star，这款MCP工具在功能完整性、社区活跃度和易用性方面表现出色，AI 评分 8.0 分，适合有一定技术背景的用户使用。
统一基准和框架，用于 Industry 4.0 资产运维和维护
资产运维基准 是一款遵循 MCP（Model Context Protocol）标准协议的 AI 工具扩展。通过 MCP 协议，它可以让 Claude、Cursor 等主流 AI 客户端直接访问和操作外部工具、数据源和服务，实现 AI 能力的无缝扩展。无论是文件操作、数据库查询还是 API 调用，都可以通过自然语言在 AI 对话中直接触发，极大提升生产效率。
统一基准和框架，用于 Industry 4.0 资产运维和维护
资产运维基准 是一款遵循 MCP（Model Context Protocol）标准协议的 AI 工具扩展。通过 MCP 协议，它可以让 Claude、Cursor 等主流 AI 客户端直接访问和操作外部工具、数据源和服务，实现 AI 能力的无缝扩展。无论是文件操作、数据库查询还是 API 调用，都可以通过自然语言在 AI 对话中直接触发，极大提升生产效率。
# 方式一：通过 Claude Code CLI 一键安装
claude skill install https://github.com/IBM/AssetOpsBench
# 方式二：手动配置 claude_desktop_config.json
{
"mcpServers": {
"------": {
"command": "npx",
"args": ["-y", "assetopsbench"]
}
}
}
# 配置文件位置
# macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
# Windows: %APPDATA%/Claude/claude_desktop_config.json
# 安装后在 Claude 对话中直接使用 # 示例： 用户: 请帮我用 资产运维基准 执行以下任务... Claude: [自动调用 资产运维基准 MCP 工具处理请求] # 查看可用工具列表 # 在 Claude 中输入："列出所有可用的 MCP 工具"
// claude_desktop_config.json 配置示例
{
"mcpServers": {
"______": {
"command": "npx",
"args": ["-y", "assetopsbench"],
"env": {
// "API_KEY": "your-api-key-here"
}
}
}
}
// 保存后重启 Claude Desktop 生效
git clone https://github.com/IBM/AssetOpsBench.git cd AssetOpsBench pip install -e .
```bash
| Domain | Example Task |
|---|---|
| **IoT** | "List all sensors of Chiller 6 in MAIN site" |
| **FMSR** | "Identify failure modes detected by Chiller 6 Supply Temperature" |
| **TSFM** | "Forecast Chiller 9 Condenser Water Flow for the week of 2020-04-27" |
| **WO** | "Generate a work order for Chiller 6 anomaly detection" |
Some tasks focus on a single domain, others are multi-step end-to-end workflows. Explore all scenarios on Hugging Face.
---
📘 Hands-on guides from our team:
---
The src/ directory contains MCP servers and a plan-execute runner built on the Model Context Protocol. See INSTRUCTIONS.md for setup.
---
AssetOpsBench is being extended by university research groups exploring new asset classes, evaluation paradigms, and agentic architectures. To list your project, open a PR.
- Internalizing MCP Tool Knowledge in Small LLMs via QLoRA Fine-Tuning — HPML project using AssetOpsBench to fine-tune ~4B models to internalize MCP tool knowledge and reduce prompt schema overhead. Ayal Yakobe, Columbia University · repo - SPIN — Structural LLM Planning via Iterative Navigation for Industrial Tasks. Yusuke Ozaki, University at Albany · paper · repo - Synthetic Scenario Generation for Evaluation of Industry 4.0 Agents — Automated scenario generation, transformer asset integration, and scenario quality evaluation. Rohith Kanathur, Sagar Chethan Kumar, Columbia University · repo - AgentOpsBench — High-throughput battery analytics MCP server with DNN prognostics (RUL prediction) and 3.3× latency optimization. Siddharth Gowda, Rushin Bhatt, Aryaman Agrawal, Winston Li, Columbia University · repo - Skill-Knowledge-Augmented Agents on AssetOpsBench — Confidence-gated skill execution with scoped knowledge plugins for industrial fault diagnosis. Vera Mazeeva, Sanskruti Shejwal, Shrey Arora, Mana Abbaszadeh, Columbia University · repo - Evaluating Temporal Semantic Caching and Workflow Optimization in Agentic Plan-Execute Pipelines. Krish Veera, Alimurtaza Mustafa Merchant, Sajal Kumar Goyla, Shambhawi Bhure, Columbia University · paper · repo - Towards Multi-Turn Dialog Systems for Industrial Asset Operations and Maintenance - Improved response quality and reduced redundant tool calls and multi-turn latency. Chengrui Li, Rujing Li, Yitong Bai, Rui Li, Columbia University ·paper· repo - Skills and Knowledge Plugin MCP Servers for Optimized Industrial O&M Agents - reducing planning overhead and improving retrieval grounding in industrial asset maintenance agents through an MCP Skills Server that exposes reusable multi-step operational workflows and a Knowledge Plugin Server that enables injection of context-specific documentation. Andrew Li, Kirthana Natarajan, Thai On, Trisha Maturi, Yeshitha Bhuvanesh, Columbia University · repo - Profiling and Optimizing the TSFM MCP Server - Developed a reproducible benchmarking harness, stage-level profiling system, and interchangeable model interface that identified preprocessing and inference bottlenecks, achieving up to 12.8× faster forecasting and 12.2% lower fine-tuning latency while supporting forecasting, fine-tuning, and anomaly detection workflows. Tomas Pasiecznik, Sam Colman, Byeolah Kwon, Sally Go, Columbia University · repo - Profiling and Optimizing the AssetOpsBench Plan-Execute Pipeline - Provides the first systematic performance characterization of the AssetOpsBench plan-execute pipeline to quantify the latency-accuracy tradeoff of thinking mode on Gemma 4 26B for industrial asset operations tasks. Implemented and evaluated scenario-based routing optimizations to balance the tradeoff. Shen Li, Charles Xu, Ann Li, Caroline Cahill, Columbia University · repo - Performance Optimzation of the TSFM Agent in an Industrial Agentic Benchmark - Developed an optimization framework for IBM's TinyTimeMixer(TTM) model by implementing model pre-loading, torch.compile graph fusion, and replacing Huggingface abstractions with direct batched model calls. We achieved 3.3X reduction in workflow latency and 68% decrease in total execution time while maintaining zero-shot forecast quality on industrial sensor data. Alisha Vinod, Jonathan Ang, Sanjaii Vijayakumar, Thomas Ajai, Columbia University . repo - Visual Inspection Agent for AssetOpsBench - Adds a vision modality to AssetOpsBench via an MCP-connected Visual Inspection Agent and 22 hand-authored visual inspection scenarios across pumps, induction motors, power transformers, and wind turbine blades. Benchmarks AWQ W4A16 quantization and vLLM serving optimizations on Qwen2.5-VL-7B and Llama-3-LLaVA-NeXT-8B, with an LLM-as-a-judge scoring pipeline for accuracy evaluation. Amaan Sheikh, Aman Upganlawar, Madhav Rajkondawar, Yang-Jung (Eric) Chen, Columbia University · repo - Agentic AI Workflows for Naval Operations and Maintenance — Exploring AssetOpsBench for evaluating agentic AI workflows, with future extensions using digital-twin-generated synthetic data. Priyam Dalmia, Chin-Teng Lin, Fred Chang, University of Technology Sydney ---
高质量的开源项目，具有较强的实用价值
AI Skill Hub 为第三方内容聚合平台，本页面信息基于公开数据整理，不对工具功能和质量作任何法律背书。
建议在沙箱或测试环境中充分验证后，再部署至生产环境，并做好必要的安全评估。
✅ Apache 2.0 — 宽松开源协议，可商用，需保留版权声明和 NOTICE 文件，含专利授权条款。
AI Skill Hub 点评：资产运维基准 的核心功能完整，质量优秀。对于Claude Desktop / Claude Code 用户来说，这是一个值得纳入个人工具库的选择。建议先在非生产环境试用，再逐步推广。
| 原始名称 | AssetOpsBench |
| 原始描述 | 开源MCP工具：AssetOpsBench - Industry 4.0: A unified benchmark and framework for building, or。⭐1.6k · Python |
| Topics | mcpiotllm-agentsmodel-context-protocol |
| GitHub | https://github.com/IBM/AssetOpsBench |
| License | Apache-2.0 |
| 语言 | Python |
收录时间：2026-05-25 · 更新时间：2026-05-26 · License：Apache-2.0 · AI Skill Hub 不对第三方内容的准确性作法律背书。
选择 Agent 类型，复制安装指令后粘贴到对应客户端