[{"_1":2,"_324":-5,"_325":-5},"loaderData",{"_3":4,"_29":30,"_31":32,"_258":259},"root",{"_5":6,"_14":15},"toasts",[7],{"_8":9,"_10":11,"_12":13},"uid","019845da-6c04-75a2-bb59-10fb4d0c709f","title","This MCP server does not support inspection","type","error","visitor",{"_16":17,"_18":19},"viewportWidth",1280,"visitorSession",{"_20":21,"_23":24,"_25":-5,"_26":27,"_28":-5},"attributes",[22],"bot","id",9821443,"membership","referenceId","019843b4-2323-76ea-bef4-84db4bc95c8a","userAccount","routes/_public/_layout",{},"routes/_public/mcp/servers/~namespace/~slug/_pages/_layout",{"_33":34,"_35":-5,"_36":37,"_38":49,"_177":178,"_257":114},"availableLocales",[],"githubUser","locale",{"_38":39,"_42":46,"_47":48},"mcpServer",{"_40":41,"_42":43,"_44":45},"description","A Model Context Protocol server for Grafana data analysis","name","grafana-mcp-analyzer","readme","# Grafana MCP Analyzer 🤖\n\n![Version](https://img.shields.io/npm/v/grafana-mcp-analyzer) ![License](https://img.shields.io/npm/l/grafana-mcp-analyzer) \n\n**让AI直接读懂你的监控数据，智能化运维分析助手**\n\n[English](https://github.com/SailingCoder/grafana-mcp-analyzer/blob/main/README_EN.md) | [中文文档](https://github.com/SailingCoder/grafana-mcp-analyzer/blob/main/README.md)\n\n## ✨ 项目简介\n\n想象一下这样的场景：\n\n* 您问AI：\"我的服务器现在怎么样？\"\n* AI直接查看您的Grafana监控，回答：\"CPU使用率偏高，建议检查这几个进程...\"\n\n复杂的监控图表，AI帮您一键分析！告别传统的手动监控方式，让AI成为您的专属运维助手！\n\n## 🚀 核心特性\n\nGrafana MCP Analyzer 基于 **MCP (Model Context Protocol)** 协议，赋能Claude、ChatGPT等AI助手具备以下超能力：\n\n- **自然语言查询** - 轻松访问监控数据，AI 一键输出专业分析\n- **智能格式化** - 支持**大数据量**分析，高效解析各类数据\n- **curl支持** - 直接使用浏览器 copy 的 curl 合成查询\n- **聚合分析** - 单个查询或 Dashboard 级别综合分析\n- **异常检测** - AI 主动报告性能问题，提前警报\n- **全数据源支持** - Prometheus、MySQL、ES 等通通支持\n- **专业 DevOps 建议** - 不只是展示数据，更提供可执行的优化方案，提升DevOps效率\n\n## 🛠️ 快速开始\n\n### 第一步：安装\n\n```bash\nnpm install -g grafana-mcp-analyzer\n```\n\n> **环境要求**：Node.js 18+ | [安装指南](https://blog.csdn.net/qq_37834631/article/details/148457021?spm=1001.2014.3001.5501)\n\n### 第二步：配置AI助手（以Cursor为例）\n\n```json\n{\n \"mcpServers\": {\n \"grafana\": {\n \"command\": \"grafana-mcp-analyzer\",\n \"env\": {\n \"CONFIG_PATH\": \"https://raw.githubusercontent.com/SailingCoder/grafana-mcp-analyzer/main/config/grafana-config-play.js\"\n }\n }\n }\n}\n```\n\n注：`CONFIG_PATH`支持绝对路径、远程路径，推荐使用远程路径快速体验。\n\n### 第三步：编写配置文件\n\n如果你需要连接自己的数据，请在 `CONFIG_PATH` 路径下创建一个名为 `grafana-config-play.js` 的配置文件。\n\n> 如果你只想快速体验示例，可跳过此步骤，直接执行第四步。\n\n

点击展开查看示例

\n\n```javascript\n/**\n * 基于Grafana Play演示实例的配置文件\n * 数据源(狗狗币OHLC数据)：https://play.grafana.org/d/candlesticksss/candlestick2?orgId=1&from=2021-07-13T22:13:30.740Z&to=2021-07-13T22:46:18.921Z&timezone=utc\n * 以下配置文件内容来源：https://raw.githubusercontent.com/SailingCoder/grafana-mcp-analyzer/main/config/grafana-config-play.js\n * Request 配置方式：支持 http api 和 curl\n */\nconst config = {\n // Grafana服务器地址\n baseUrl: 'https://play.grafana.org',\n \n // 默认请求头\n defaultHeaders: {\n 'Content-Type': 'application/json',\n 'Accept': 'application/json, text/plain, */*'\n },\n\n // 健康检查配置\n healthCheck: {\n url: 'api/health'\n },\n\n // 查询定义\n queries: {\n // 第一个查询 - 使用curl格式（面板2的狗狗币数据）\n // dogecoin_panel_2 ....\n\n // 第二个查询 - 使用HTTP API格式（面板7的狗狗币数据）\n 'dogecoin_panel_7': {\n url: 'api/ds/query',\n method: 'POST',\n params: {\n ds_type: 'grafana-testdata-datasource',\n requestId: 'SQR109'\n },\n headers: {\n 'accept': 'application/json, text/plain, */*',\n 'content-type': 'application/json',\n 'x-datasource-uid': '9cY0WtPMz',\n 'x-grafana-org-id': '1',\n 'x-panel-id': '7',\n 'x-panel-plugin-id': 'candlestick',\n 'x-plugin-id': 'grafana-testdata-datasource'\n },\n data: {\n queries: [{\n csvFileName: \"ohlc_dogecoin.csv\",\n datasource: {\n type: \"grafana-testdata-datasource\",\n uid: \"9cY0WtPMz\"\n },\n refId: \"A\",\n scenarioId: \"csv_file\",\n datasourceId: 153,\n intervalMs: 2000,\n maxDataPoints: 1150\n }],\n from: \"1626214410740\",\n to: \"1626216378921\"\n },\n systemPrompt: `您是金融市场技术分析专家，专注于加密货币市场分析。\n\n**分析重点**：\n1. 市场趋势和动量分析 - 识别主要趋势方向和动量变化\n2. 价格模式识别 - 识别头肩顶、双底、三角形等经典形态\n3. 成交量与价格关系 - 分析成交量对价格走势的支撑\n4. 市场情绪评估 - 基于技术指标评估市场情绪状态\n5. 短期和长期投资策略建议 - 提供不同时间周期的投资建议\n\n**输出要求**：\n- 基于实际数据进行分析，提供具体数值解读\n- 识别关键的价格模式和趋势变化\n- 给出明确的交易建议和风险提示\n- 提供可操作的投资策略\n\n请提供详细的技术分析报告。`\n },\n overall_cpu_utilization100: {\n curl: `curl 'https://play.grafana.org/api/ds/query?ds_type=prometheus&requestId=SQR371' \\\n -H 'accept: application/json, text/plain, */*' \\\n -H 'accept-language: zh-CN,zh;q=0.9' \\\n -H 'cache-control: no-cache' \\\n -H 'content-type: application/json' \\\n -b '_ga=GA1.2.387525048.1751712678; rl_page_init_referrer=RudderEncrypt%3AU2FsdGVkX191kw8iAnoyFkv6jbIl3EOkbSdK21uFLwGid2zCBcXWXVl4rK8kP9uB; rl_page_init_referring_domain=RudderEncrypt%3AU2FsdGVkX1%2FQpNd4Fbr7FgBG8YeyeoTAiBUO993bC9E%3D; _gid=GA1.2.354949503.1752935466; rl_group_id=RudderEncrypt%3AU2FsdGVkX1%2Fyd5jy%2Bq5XZfeqcDGhXMhz56ANft0NLCo%3D; rl_group_trait=RudderEncrypt%3AU2FsdGVkX1%2F9hmHjbWlb%2F%2B2RP0JlMRymkg9QBgUw3oE%3D; rl_anonymous_id=RudderEncrypt%3AU2FsdGVkX19JQD0l8hbD8ApQMSbVisxyXCEuam7wcYtcnfywOO67gQc7EjkFm0bW%2BNZjB%2BsmRZnHy5ccbyeoHQ%3D%3D; rl_user_id=RudderEncrypt%3AU2FsdGVkX18s9kRPf%2BwQSRIaYGd9O5kGPmZh%2FQhoq4LyI63CRJNoBrh7Cc06OuAO; rl_trait=RudderEncrypt%3AU2FsdGVkX1%2B%2FhZugE4qfWyjSTEFKcsYs0DwcOyfdazoJfVtGv4x0q%2BOFxbqHDD0r%2BLWcg%2F6CceMFQH3dJIa3C0WyF0hWoBLLwV%2BiQB4077KEHTtX%2BkJxjJ4X6czXwpsh%2FsV9e8l4ptVfz%2FgyJLh1qw%3D%3D; _gat=1; _ga_Y0HRZEVBCW=GS2.2.s1752935474$o2$g1$t1752935591$j38$l0$h0; rl_session=RudderEncrypt%3AU2FsdGVkX1%2BUhBGRm24hqUS5TRKZrN31aK8t518MW16GZKplO6iFClFnqmpYiglWbXqKgnDZz8o%2FaGxuQouIM%2BN0BBr8Nh3HY6chGRtVUEeRSRXAAQiiH30%2Bp6%2F57AoqhwV3k0jqvIikr69S9sDpCg%3D%3D' \\\n -H 'origin: https://play.grafana.org' \\\n -H 'pragma: no-cache' \\\n -H 'priority: u=1, i' \\\n -H 'referer: https://play.grafana.org/d/cNMLIAFK/cpu-utilization-details-cores?var-interval=$__auto&orgId=1&from=now-3h&to=now&timezone=browser&var-host=faro-shop-control-plane&var-cpu=$__all&refresh=5s&editPanel=22&inspect=22&inspectTab=query' \\\n -H 'sec-ch-ua: \"Not)A;Brand\";v=\"8\", \"Chromium\";v=\"138\", \"Google Chrome\";v=\"138\"' \\\n -H 'sec-ch-ua-mobile: ?0' \\\n -H 'sec-ch-ua-platform: \"macOS\"' \\\n -H 'sec-fetch-dest: empty' \\\n -H 'sec-fetch-mode: cors' \\\n -H 'sec-fetch-site: same-origin' \\\n -H 'user-agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/138.0.0.0 Safari/537.36' \\\n -H 'x-dashboard-title: CPU Utilization Details (Cores)' \\\n -H 'x-dashboard-uid: cNMLIAFK' \\\n -H 'x-datasource-uid: grafanacloud-prom' \\\n -H 'x-grafana-device-id: 2b0db28108a0a56f4a0dcf3d59537fe7' \\\n -H 'x-grafana-org-id: 1' \\\n -H 'x-panel-id: 22' \\\n -H 'x-panel-plugin-id: timeseries' \\\n -H 'x-panel-title: $host - Overall CPU Utilization' \\\n -H 'x-plugin-id: prometheus' \\\n --data-raw $'{\"queries\":[{\"calculatedInterval\":\"2s\",\"datasource\":{\"type\":\"prometheus\",\"uid\":\"grafanacloud-prom\"},\"datasourceErrors\":{},\"errors\":{},\"expr\":\"clamp_max((avg by (mode) ( (clamp_max(rate(node_cpu_seconds_total{instance=\\\\\"faro-shop-control-plane\\\\\",mode\\u0021=\\\\\"idle\\\\\"}[1m]),1)) or (clamp_max(irate(node_cpu_seconds_total{instance=\\\\\"faro-shop-control-plane\\\\\",mode\\u0021=\\\\\"idle\\\\\"}[5m]),1)) )),1)\",\"format\":\"time_series\",\"hide\":false,\"interval\":\"1m\",\"intervalFactor\":1,\"legendFormat\":\"{{mode}}\",\"metric\":\"\",\"refId\":\"A\",\"step\":300,\"exemplar\":false,\"requestId\":\"22A\",\"utcOffsetSec\":28800,\"scopes\":[],\"adhocFilters\":[],\"datasourceId\":171,\"intervalMs\":60000,\"maxDataPoints\":778},{\"datasource\":{\"type\":\"prometheus\",\"uid\":\"grafanacloud-prom\"},\"expr\":\"clamp_max(max by () (sum by (cpu) ( (clamp_max(rate(node_cpu_seconds_total{instance=\\\\\"faro-shop-control-plane\\\\\",mode\\u0021=\\\\\"idle\\\\\",mode\\u0021=\\\\\"iowait\\\\\"}[5m]),1)) or (clamp_max(irate(node_cpu_seconds_total{instance=\\\\\"faro-shop-control-plane\\\\\",mode\\u0021=\\\\\"idle\\\\\",mode\\u0021=\\\\\"iowait\\\\\"}[5m]),1)) )),1)\",\"format\":\"time_series\",\"hide\":false,\"interval\":\"1m\",\"intervalFactor\":1,\"legendFormat\":\"Max Core Utilization\",\"refId\":\"B\",\"exemplar\":false,\"requestId\":\"22B\",\"utcOffsetSec\":28800,\"scopes\":[],\"adhocFilters\":[],\"datasourceId\":171,\"intervalMs\":60000,\"maxDataPoints\":778}],\"from\":\"1752924823337\",\"to\":\"1752935623337\"}'`,\n systemPrompt: `您是系统性能分析专家，专注于CPU使用率数据分析。\n\n**核心任务**：直接回答用户的问题：\"我的服务器现在怎么样？\"\n\n**必须回答的问题**：\n当前CPU使用率是多少？（具体数值）\n\n**输出格式**：\n## 服务器状态概览\n**直接结论**：服务器CPU使用率 [具体数值]%，状态 [正常/偏高/异常]\n\n## 详细数据\n- **当前使用率**：[数值]%\n- **平均使用率**：[数值]%\n- **峰值使用率**：[数值]%\n- **主要使用模式**：[user/system/iowait等]\n\n## 风险评估\n[基于数据的具体风险分析]\n\n## 行动建议\n[具体的可执行建议]\n\n**重要**：如果无法获取到实际数据，请明确说明\"无法获取实际数据\"，并解释可能的原因。不要基于假设进行分析！`\n },\n }\n};\n\nmodule.exports = config;\n```\n

\n\n### 第四步：开始使用！\n\n**完全重启Cursor**，然后体验智能分析：\n\n> 👤 您：分析overall_cpu_utilization100数据\\\n> 🤖 AI：正在连接Grafana并分析...\n\n> 👤 您：聚合分析dogecoin_panel_2、dogecoin_panel_7的数据\\\n> 🤖 AI：同时查询多个指标并进行综合关联分析...\n\n**配置完成！**\n\n![在这里插入图片描述](https://github.com/SailingCoder/grafana-mcp-analyzer/blob/main/docs/image(1).png)\n\n## MCP工具清单\n\n| 工具 | 功能 | 使用场景 |\n|------|------|----------|\n| `analyze_query` | 查询+AI分析 | 需要专业建议 |\n| `aggregate_analyze` | 聚合分析 | 多查询统一分析 |\n| `list_queries` | 查询列表 | 查看配置 |\n| `check_health` | 健康检查 | 状态监控 |\n| `manage_sessions` | 会话管理 | 管理分析会话 |\n| `list_data` | 数据列表 | 查看存储数据 |\n| `server_status` | 服务器状态 | 服务器信息 |\n\n### 工具使用方式\n\n```javascript\n// AI助手会自动选择合适的工具\n👤 \"分析CPU使用情况\" → 🤖 调用 analyze_query\n👤 \"聚合分析系统指标\" → 🤖 调用 aggregate_analyze\n```\n\n## 高级配置\n\n以下内容适用于需要自定义数据源或进行更高级使用场景的用户。\n\n

如何获取 Request 配置？

\n\n### 方式一：HTTP API（如 `dogecoin_panel_7`）\n\n1. 获取 Data 传参：进入图表 → \"Query Inspector\" → \"JSON\"解析 → 拷贝请求体(request)\n2. 获取 Url 和 Headers Token：通过 Network 面板查看请求参数，手动构造 HTTP 配置。\n\n### 方式二：curl（推荐，适用于所有面板，如`overall_cpu_utilization100`）：\n\n1. 在Grafana中执行查询\n2. 按F12打开开发者工具 → Network标签页\n3. 找到查询请求 → 右键点击 → Copy as cURL\n4. 将复制的 curl 粘贴至配置文件中即可\n

\n\n

配置文件示例

\n\n- [基础版配置](https://github.com/SailingCoder/grafana-mcp-analyzer/blob/main/config/grafana-config.simple.js)\n- [远程真实配置](https://github.com/SailingCoder/grafana-mcp-analyzer/blob/main/config/grafana-config-play.js)\n

\n\n

环境变量说明

\n\n```json\n{\n \"mcpServers\": {\n \"grafana\": {\n \"command\": \"grafana-mcp-analyzer\",\n \"env\": {\n \"CONFIG_PATH\": \"https://raw.githubusercontent.com/SailingCoder/grafana-mcp-analyzer/main/config/grafana-config-play.js\",\n \"MAX_CHUNK_SIZE\": \"100\",\n \"SESSION_TIMEOUT_HOURS\": \"24\",\n \"CONFIG_MAX_AGE\": \"300\",\n }\n }\n }\n}\n\n```\n\n\n|环境变量名 | 类型 | 默认值 | 说明 |\n| ----- | -- | --- | -- |\n| `CONFIG_PATH` | string | 必填 | 配置文件路径（本地或 HTTPS 远程地址） |\n| `MAX_CHUNK_SIZE` | number | `100` | 单块最大数据体积（KB），影响切片性能 |\n| `SESSION_TIMEOUT_HOURS` | number | `24` | 会话过期时间（小时） |\n| `CONFIG_MAX_AGE` | number | `300` | 远程配置文件缓存时间（秒），设为 `0` 则禁用 |\n\n缓存特性：\n\n- 智能缓存配置文件（默认缓存 5 分钟）\n- 网络失败时使用本地过期缓存\n- 启动自动清理缓存文件\n- 设置 CONFIG_MAX_AGE=0 可禁用缓存，每次请求都拉取最新配置\n\n

\n\n

支持配置类型：本地绝对路径 / 远程路径

\n \n### 1. 远程路径\n \n支持通过HTTPS URL访问远程配置文件，适用于团队协作和多环境部署：\n\n```json\n{\n \"env\": {\n \"CONFIG_PATH\": \"https://raw.githubusercontent.com/SailingCoder/grafana-mcp-analyzer/main/config/grafana-config-play.js\"\n }\n}\n```\n\n支持的远程存储：\n\n* GitHub Raw: `https://raw.githubusercontent.com/SailingCoder/grafana-mcp-analyzer/main/config/grafana-config-play.js`\n* 阿里云OSS: `https://bucket.oss-cn-hangzhou.aliyuncs.com/config.js`\n* 腾讯云COS: `https://bucket-123.cos.ap-shanghai.myqcloud.com/config.js`\n* AWS S3: `https://bucket.s3.amazonaws.com/config.js`\n\n注意：\n- ❌ 不支持 GitHub 网页路径，如 https://github.com/SailingCoder/grafana-mcp-analyzer/blob/main/config/grafana-config-play.js,\t返回的是 HTML 页面\n- ✅ 必须使用 GitHub Raw 格式获取原始 JS 文件，如 https://raw.githubusercontent.com/SailingCoder/grafana-mcp-analyzer/main/config/grafana-config-play.js\n\n\n### 2. 本地路径\n \n支持传入本地绝对路径，适用于快速测试分析：\n \n```json\n{\n \"mcpServers\": {\n \"grafana\": {\n \"command\": \"grafana-mcp-analyzer\",\n \"env\": {\n \"CONFIG_PATH\": \"/Users/your-username/project/grafana-config.js\"\n }\n }\n }\n}\n```\n

\n\n

命令行选项

\n\n```bash\n# 显示版本信息\ngrafana-mcp-analyzer -v\ngrafana-mcp-analyzer --version\n\n# 显示帮助信息\ngrafana-mcp-analyzer -h\ngrafana-mcp-analyzer --help\n```\n\n

\n\n\n

访问权限环境变量（可选）

\n\n如需调用受保护的 Grafana API，可通过以下方式设置：\n\n```bash\nexport GRAFANA_URL=\"https://your-grafana.com\"\nexport GRAFANA_TOKEN=\"your-api-token\"\n```\n你也可以在配置文件中使用 Headers 方式直接注入 token 访问。\n

\n\n## 配置示例\n\n### 业务场景配置\n\n

电商业务分析

\n\n**用户问题**：\"我的电商转化率怎么样？如何提升销售额？\"\n\n```javascript\n// 电商转化率分析\necommerce_conversion: {\n curl: `curl 'api/ds/query' \\\\\n -X POST \\\\\n -H 'Content-Type: application/json' \\\\\n -d '{\"queries\":[{\"refId\":\"A\",\"expr\":\"rate(orders_total[5m]) / rate(page_views_total[5m]) * 100\",\"range\":{\"from\":\"now-24h\",\"to\":\"now\"}}]}'`,\n systemPrompt: `您是电商业务分析专家。请分析转化率数据并回答以下关键问题：\n\n**核心分析问题**：\n1. 当前转化率是多少？与行业标准对比如何？\n2. 转化率在一天中的高峰和低谷时段是什么时候？\n3. 哪些因素可能影响转化率下降？\n4. 具体建议如何提升转化率？预期能带来多少收益？\n\n**输出格式**：\n- 数据概览：当前转化率数值和趋势\n- 问题诊断：识别转化率瓶颈\n- 优化建议：3-5个可执行的改进方案\n- 收益预测：预期提升效果和ROI\n\n请用通俗易懂的语言，给出可操作的具体建议。`\n}\n```\n\n

\n\n

金融风控分析

\n**用户问题**：\"我的交易系统有风险吗？如何预防欺诈？\"\n\n```javascript\n// 交易风控分析\nfinance_risk_analysis: {\n curl: `curl 'api/ds/query' \\\\\n -X POST \\\\\n -H 'Content-Type: application/json' \\\\\n -d '{\"queries\":[{\"refId\":\"A\",\"expr\":\"sum(rate(transaction_amount_total[5m]))\",\"range\":{\"from\":\"now-7d\",\"to\":\"now\"}}]}'`,\n systemPrompt: `您是金融风控专家。请分析交易数据并回答以下关键问题：\n\n**核心分析问题**：\n1. 当前交易量是否异常？与历史对比如何？\n2. 是否存在可疑的交易模式？\n3. 哪些交易需要重点关注？\n4. 如何优化风控策略？\n\n**输出格式**：\n- 风险等级：低/中/高风险\n- 异常指标：具体异常数据点\n- 风险分析：潜在风险原因\n- 防护建议：具体风控措施\n- 紧急行动：需要立即处理的事项\n\n请用红色标记高风险，黄色标记中风险，绿色标记低风险。`\n}\n```\n

\n\n

用户行为分析

\n\n**用户问题**：\"我的用户活跃度怎么样？如何提高用户留存？\"\n\n```javascript\n// 用户活跃度分析\nuser_engagement: {\n curl: `curl 'api/ds/query' \\\\\n -X POST \\\\\n -H 'Content-Type: application/json' \\\\\n -d '{\"queries\":[{\"refId\":\"A\",\"expr\":\"count(increase(user_sessions_total[1h]))\",\"range\":{\"from\":\"now-30d\",\"to\":\"now\"}}]}'`,\n systemPrompt: `您是用户行为分析专家。请分析用户活跃度数据并回答以下关键问题：\n\n**核心分析问题**：\n1. 用户活跃度趋势如何？是否在增长？\n2. 用户使用习惯有什么特点？\n3. 哪些用户群体最活跃？\n4. 如何提高用户留存率？\n\n**输出格式**：\n- 用户画像：活跃用户特征\n- 趋势分析：活跃度变化趋势\n- 目标用户：最有价值的用户群体\n- 留存策略：提高用户粘性的方法\n- 预期效果：实施建议后的预期改善\n\n请结合用户生命周期，给出个性化的运营建议。`\n}\n```\n\n

\n\n### 系统监控配置\n\n

服务器性能监控

\n\n**用户问题**：\"我的服务器性能怎么样？需要扩容吗？\"\n\n```javascript\n// 服务器性能分析\nserver_performance: {\n curl: `curl 'api/ds/query' \\\\\n -X POST \\\\\n -H 'Content-Type: application/json' \\\\\n -d '{\"queries\":[{\n \"refId\":\"A\",\n \"expr\":\"node_cpu_seconds_total{mode=\\\"user\\\"} / node_cpu_seconds_total * 100\",\n \"range\":{\"from\":\"now-2h\",\"to\":\"now\"}\n }]}'`,\n systemPrompt: `您是系统性能专家。请分析服务器性能数据并回答以下关键问题：\n\n**核心分析问题**：\n1. CPU使用率是否正常？是否接近瓶颈？\n2. 内存使用情况如何？是否存在泄漏？\n3. 磁盘I/O是否成为瓶颈？\n4. 是否需要扩容或优化？\n\n**输出格式**：\n- 性能评分：优秀/良好/一般/差\n- 关键指标：CPU、内存、磁盘使用率\n- 瓶颈分析：性能问题原因\n- 优化建议：具体改进方案\n- 告警建议：需要立即关注的问题\n\n请用颜色标记不同严重程度：正常注意危险`\n}\n```\n

\n\n

应用错误监控

\n\n**用户问题**：\"我的应用有错误吗？影响用户体验吗？\"\n\n```javascript\n// 应用错误分析\napp_error_analysis: {\n url: \"api/ds/es/query\",\n method: \"POST\",\n data: {\n es: {\n index: \"app-logs-*\",\n query: {\n \"query\": {\n \"bool\": {\n \"must\": [\n {\"term\": {\"level\": \"ERROR\"}},\n {\"range\": {\"@timestamp\": {\"gte\": \"now-1h\"}}}\n ]\n }\n }\n }\n }\n },\n systemPrompt: `您是应用监控专家。请分析错误日志并回答以下关键问题：\n\n**核心分析问题**：\n1. 错误频率如何？是否在增加？\n2. 哪些错误最严重？影响多少用户？\n3. 错误集中在哪些功能模块？\n4. 如何快速修复和预防？\n\n**输出格式**：\n- 错误等级：严重/中等/轻微\n- 错误统计：错误数量、影响用户数\n- 错误分类：按模块和类型分类\n- 修复建议：具体修复步骤\n- 预防措施：避免类似错误的方法\n\n请按严重程度排序，优先处理影响用户最多的错误。`\n}\n```\n

\n\n### 聚合分析配置\n\n

全链路性能分析

\n\n**用户问题**：\"我的系统整体性能怎么样？哪里是瓶颈？\"\n\n```javascript\n// 前端性能\nfrontend_performance: {\n curl: `curl 'api/ds/query' \\\\\n -X POST \\\\\n -H 'Content-Type: application/json' \\\\\n -d '{\"queries\":[{\"refId\":\"A\",\"expr\":\"histogram_quantile(0.95, rate(page_load_time_seconds_bucket[5m]))\",\"range\":{\"from\":\"now-1h\",\"to\":\"now\"}}]}'`,\n systemPrompt: '前端性能专家：分析页面加载时间，识别前端性能瓶颈。'\n},\n\n// 后端性能\nbackend_performance: {\n curl: `curl 'api/ds/query' \\\\\n -X POST \\\\\n -H 'Content-Type: application/json' \\\\\n -d '{\"queries\":[{\"refId\":\"A\",\"expr\":\"histogram_quantile(0.95, rate(api_response_time_seconds_bucket[5m]))\",\"range\":{\"from\":\"now-1h\",\"to\":\"now\"}}]}'`,\n systemPrompt: '后端性能专家：分析API响应时间，识别后端性能问题。'\n},\n\n// 数据库性能\ndatabase_performance: {\n curl: `curl 'api/ds/query' \\\\\n -X POST \\\\\n -H 'Content-Type: application/json' \\\\\n -d '{\"queries\":[{\"refId\":\"A\",\"expr\":\"rate(mysql_queries_total[5m])\",\"range\":{\"from\":\"now-1h\",\"to\":\"now\"}}]}'`,\n systemPrompt: '数据库性能专家：分析数据库查询性能，识别数据库瓶颈。'\n}\n```\n\n**使用方式**：\n> 👤 您：聚合分析全链路性能：frontend_performance, backend_performance, database_performance\n> \n> 🤖 AI：综合分析前端、后端、数据库性能，提供完整的性能优化建议\n

\n\n## 常见问题\n\n

无法连接到Grafana服务

\n\n* 检查Grafana地址格式：必须包含`https://`或`http://`\n* 验证API密钥有效性：确保未过期且有足够权限\n* 测试网络连通性和防火墙设置\n\n

\n\n

AI提示找不到MCP工具

\n\n* ��全退出Cursor并重新启动\n* 检查配置文件路径是否正确\n* 确保Node.js版本 ≥ 18\n\n

\n\n

查询执行失败或超时

\n\n* 增加timeout设置\n* 检查数据源连接状态\n* 数据量过大时，缩小时间范围\n\n

\n\n## 文章推荐\n\n* [grafana-mcp-analyzer：基于 MCP 的轻量 AI 分析监控图表的运维神器！](https://blog.csdn.net/qq_37834631/article/details/148473620?spm=1001.2014.3001.5501) - CSDN技术博客深度解析\n\n## 许可证\n\nMIT 开源协议。详见 [LICENSE](LICENSE) 文件。","English","tag","en-US",{"_50":51,"_52":53,"_20":76,"_83":41,"_84":41,"_85":86,"_89":90,"_111":-5,"_23":112,"_113":114,"_115":116,"_42":43,"_154":102,"_155":156,"_159":-5,"_160":-5,"_161":-5,"_162":163,"_168":-5,"_123":43,"_169":170,"_173":-5,"_8":174,"_175":176},"addedAt","2025-07-11T10:27:14.400400114Z","argumentsJsonSchema",{"_54":55,"_73":74,"_12":75},"properties",{"_56":57,"_60":61,"_65":66,"_69":70},"CONFIG_PATH",{"_40":58,"_12":59},"Configuration file path (local or HTTPS remote address)","string","CONFIG_MAX_AGE",{"_62":63,"_40":64,"_12":59},"default","300","Remote configuration file cache time in seconds, set to 0 to disable","MAX_CHUNK_SIZE",{"_62":67,"_40":68,"_12":59},"100","Maximum data chunk size in KB, affects slicing 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Grafana MCP Analyzer 🤖\n\n![Version](https://img.shields.io/npm/v/grafana-mcp-analyzer) ![License](https://img.shields.io/npm/l/grafana-mcp-analyzer) \n\n**让AI直接读懂你的监控数据，智能化运维分析助手**\n\n[English](https://github.com/SailingCoder/grafana-mcp-analyzer/blob/main/README_EN.md) | [中文文档](https://github.com/SailingCoder/grafana-mcp-analyzer/blob/main/README.md)\n\n## ✨ 项目简介\n\n想象一下这样的场景：\n\n* 您问AI：\"我的服务器现在怎么样？\"\n* AI直接查看您的Grafana监控，回答：\"CPU使用率偏高，建议检查这几个进程...\"\n\n复杂的监控图表，AI帮您一键分析！告别传统的手动监控方式，让AI成为您的专属运维助手！\n\n## 🚀 核心特性\n\nGrafana MCP Analyzer 基于 **MCP (Model Context Protocol)** 协议，赋能Claude、ChatGPT等AI助手具备以下超能力：\n\n- **自然语言查询** - 轻松访问监控数据，AI 一键输出专业分析\n- **智能格式化** - 支持**大数据量**分析，高效解析各类数据\n- **curl支持** - 直接使用浏览器 copy 的 curl 合成查询\n- **聚合分析** - 单个查询或 Dashboard 级别综合分析\n- **异常检测** - AI 主动报告性能问题，提前警报\n- **全数据源支持** - Prometheus、MySQL、ES 等通通支持\n- **专业 DevOps 建议** - 不只是展示数据，更提供可执行的优化方案，提升DevOps效率\n\n## 🛠️ 快速开始\n\n### 第一步：安装\n\n```bash\nnpm install -g grafana-mcp-analyzer\n```\n\n> **环境要求**：Node.js 18+ | [安装指南](https://blog.csdn.net/qq_37834631/article/details/148457021?spm=1001.2014.3001.5501)\n\n### 第二步：配置AI助手（以Cursor为例）\n\n```json\n{\n \"mcpServers\": {\n \"grafana\": {\n \"command\": \"grafana-mcp-analyzer\",\n \"env\": {\n \"CONFIG_PATH\": \"https://raw.githubusercontent.com/SailingCoder/grafana-mcp-analyzer/main/config/grafana-config-play.js\"\n }\n }\n }\n}\n```\n\n注：`CONFIG_PATH`支持绝对路径、远程路径，推荐使用远程路径快速体验。\n\n### 第三步：编写配置文件\n\n如果你需要连接自己的数据，请在 `CONFIG_PATH` 路径下创建一个名为 `grafana-config-play.js` 的配置文件。\n\n> 如果你只想快速体验示例，可跳过此步骤，直接执行第四步。\n\n

点击展开查看示例

如何获取 Request 配置？

\n\n

配置文件示例

\n\n

环境变量说明

\n\n

支持配置类型：本地绝对路径 / 远程路径

\n\n

命令行选项

\n\n```bash\n# 显示版本信息\ngrafana-mcp-analyzer -v\ngrafana-mcp-analyzer --version\n\n# 显示帮助信息\ngrafana-mcp-analyzer -h\ngrafana-mcp-analyzer --help\n```\n\n

\n\n\n

访问权限环境变量（可选）

\n\n## 配置示例\n\n### 业务场景配置\n\n

电商业务分析

\n\n

金融风控分析

\n\n

用户行为分析

\n\n### 系统监控配置\n\n

服务器性能监控

\n\n

应用错误监控

\n\n### 聚合分析配置\n\n

全链路性能分析

\n\n## 常见问题\n\n

无法连接到Grafana服务

\n\n* 检查Grafana地址格式：必须包含`https://`或`http://`\n* 验证API密钥有效性：确保未过期且有足够权限\n* 测试网络连通性和防火墙设置\n\n

\n\n

AI提示找不到MCP工具

\n\n* 完全退出Cursor并重新启动\n* 检查配置文件路径是否正确\n* 确保Node.js版本 ≥ 18\n\n

\n\n

查询执行失败或超时

\n\n* 增加timeout设置\n* 检查数据源连接状态\n* 数据量过大时，缩小时间范围\n\n

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