grafana-mcp-analyzer
Version:
让AI助手直接分析你的Grafana监控数据 - A Model Context Protocol server for Grafana data analysis
198 lines (176 loc) • 10.9 kB
JavaScript
/**
* 基于Grafana Play演示实例的配置文件
* 数据源：https://play.grafana.org (狗狗币OHLC数据)
*/
const config = {
// Grafana服务器地址
baseUrl: 'https://play.grafana.org',
// 默认请求头
defaultHeaders: {
'Content-Type': 'application/json',
'Accept': 'application/json, text/plain, */*'
},
// 健康检查配置
healthCheck: {
url: 'api/health'
},
// 查询定义
queries: {
// 第一个查询 - 使用curl格式（面板2的狗狗币数据）
'dogecoin_panel_2': {
curl: `curl 'https://play.grafana.org/api/ds/query?ds_type=grafana-testdata-datasource&requestId=SQR108' \\
-X POST \\
-H 'accept: application/json, text/plain, */*' \\
-H 'content-type: application/json' \\
-H 'x-datasource-uid: 9cY0WtPMz' \\
-H 'x-grafana-org-id: 1' \\
-H 'x-panel-id: 2' \\
-H 'x-panel-plugin-id: candlestick' \\
-H 'x-plugin-id: grafana-testdata-datasource' \\
--data-raw '{"queries":[{"csvFileName":"ohlc_dogecoin.csv","datasource":{"type":"grafana-testdata-datasource","uid":"9cY0WtPMz"},"refId":"A","scenarioId":"csv_file","datasourceId":153,"intervalMs":2000,"maxDataPoints":1150}],"from":"1626214410740","to":"1626216378921"}'`,
systemPrompt: `您是狗狗币数据分析专家，专注于OHLC（开盘价、最高价、最低价、收盘价）数据分析。
**分析重点**：
1. 价格趋势和波动模式 - 识别主要趋势方向和变化周期
2. 支撑位和阻力位识别 - 找出关键价格水平
3. 交易机会分析 - 基于技术指标识别入场和出场时机
4. 风险评估和建议 - 评估当前市场风险和投资建议
5. 技术指标分析 - 结合多个技术指标进行综合分析
**输出要求**：
- 基于实际数据进行分析，提供具体数值解读
- 识别关键的价格水平和趋势变化
- 给出明确的交易建议和风险提示
- 提供可操作的投资策略
请提供专业的投资分析和建议。`
},
// 第二个查询 - 使用HTTP API格式（面板7的狗狗币数据）
'dogecoin_panel_7': {
url: 'api/ds/query',
method: 'POST',
params: {
ds_type: 'grafana-testdata-datasource',
requestId: 'SQR109'
},
headers: {
'accept': 'application/json, text/plain, */*',
'content-type': 'application/json',
'x-datasource-uid': '9cY0WtPMz',
'x-grafana-org-id': '1',
'x-panel-id': '7',
'x-panel-plugin-id': 'candlestick',
'x-plugin-id': 'grafana-testdata-datasource'
},
data: {
queries: [{
csvFileName: "ohlc_dogecoin.csv",
datasource: {
type: "grafana-testdata-datasource",
uid: "9cY0WtPMz"
},
refId: "A",
scenarioId: "csv_file",
datasourceId: 153,
intervalMs: 2000,
maxDataPoints: 1150
}],
from: "1626214410740",
to: "1626216378921"
},
systemPrompt: `您是金融市场技术分析专家，专注于加密货币市场分析。
**分析重点**：
1. 市场趋势和动量分析 - 识别主要趋势方向和动量变化
2. 价格模式识别 - 识别头肩顶、双底、三角形等经典形态
3. 成交量与价格关系 - 分析成交量对价格走势的支撑
4. 市场情绪评估 - 基于技术指标评估市场情绪状态
5. 短期和长期投资策略建议 - 提供不同时间周期的投资建议
**输出要求**：
- 基于实际数据进行分析，提供具体数值解读
- 识别关键的价格模式和趋势变化
- 给出明确的交易建议和风险提示
- 提供可操作的投资策略
请提供详细的技术分析报告。`
},
overall_cpu_utilization100: {
curl: `curl 'https://play.grafana.org/api/ds/query?ds_type=prometheus&requestId=SQR371' \
-H 'accept: application/json, text/plain, */*' \
-H 'accept-language: zh-CN,zh;q=0.9' \
-H 'cache-control: no-cache' \
-H 'content-type: application/json' \
-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' \
-H 'origin: https://play.grafana.org' \
-H 'pragma: no-cache' \
-H 'priority: u=1, i' \
-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' \
-H 'sec-ch-ua: "Not)A;Brand";v="8", "Chromium";v="138", "Google Chrome";v="138"' \
-H 'sec-ch-ua-mobile: ?0' \
-H 'sec-ch-ua-platform: "macOS"' \
-H 'sec-fetch-dest: empty' \
-H 'sec-fetch-mode: cors' \
-H 'sec-fetch-site: same-origin' \
-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' \
-H 'x-dashboard-title: CPU Utilization Details (Cores)' \
-H 'x-dashboard-uid: cNMLIAFK' \
-H 'x-datasource-uid: grafanacloud-prom' \
-H 'x-grafana-device-id: 2b0db28108a0a56f4a0dcf3d59537fe7' \
-H 'x-grafana-org-id: 1' \
-H 'x-panel-id: 22' \
-H 'x-panel-plugin-id: timeseries' \
-H 'x-panel-title: $host - Overall CPU Utilization' \
-H 'x-plugin-id: prometheus' \
--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"}'`,
systemPrompt: `您是系统性能分析专家，专注于CPU使用率数据分析。
**核心任务**：直接回答用户的问题："我的服务器现在怎么样？"
**必须回答的问题**：
当前CPU使用率是多少？（具体数值）
**输出格式**：
## 服务器状态概览
**直接结论**：服务器CPU使用率 [具体数值]%，状态 [正常/偏高/异常]
## 详细数据
- **当前使用率**：[数值]%
- **平均使用率**：[数值]%
- **峰值使用率**：[数值]%
- **主要使用模式**：[user/system/iowait等]
## 风险评估
[基于数据的具体风险分析]
## 行动建议
[具体的可执行建议]
**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！`
},
lcp_analysis: {
curl: `curl 'https://monitor-grafana.tal.com/api/datasources/proxy/674/_msearch' \
-H 'accept: application/json, text/plain, */*' \
-H 'accept-language: zh-CN,zh;q=0.9' \
-H 'cache-control: no-cache' \
-H 'content-type: application/x-ndjson' \
-H 'origin: https://cloud.tal.com' \
-H 'pragma: no-cache' \
-H 'priority: u=1, i' \
-H 'referer: https://cloud.tal.com/' \
-H 'sec-ch-ua: "Not)A;Brand";v="8", "Chromium";v="138", "Google Chrome";v="138"' \
-H 'sec-ch-ua-mobile: ?0' \
-H 'sec-ch-ua-platform: "macOS"' \
-H 'sec-fetch-dest: empty' \
-H 'sec-fetch-mode: cors' \
-H 'sec-fetch-site: same-site' \
-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' \
-H 'x-grafana-org-id: 1' \
--data-raw $'{"search_type":"query_then_fetch","ignore_unavailable":true,"index":"basiclog-interactive_1005774-*","max_concurrent_shard_requests":256}\n{"size":0,"query":{"bool":{"filter":[{"range":{"@timestamp":{"gte":1753446722303,"lte":1753450322303,"format":"epoch_millis"}}},{"query_string":{"analyze_wildcard":true,"query":"projectId:\\"cloud-prod\\" AND type:\\"perfResource\\" AND metricData.metric:\\"pageLoad\\" AND metricData.lcp:{* TO 30000}"}}]}},"aggs":{"2":{"terms":{"field":"metricData.url.keyword","size":500,"order":{"1[75.0]":"desc"},"min_doc_count":1},"aggs":{"1":{"percentiles":{"field":"metricData.lcp","percents":["50","75","85","95"]}}}}}}\n'`,
systemPrompt: `
您是一位专业的数据分析专家，请根据提供的页面静态资源加载数据，生成一份结构化的性能分析报告。
**请输出以下内容：**
**一、静态资源分析**
分析页面静态资源加载情况，并判断其意义。
1. 网络具体ttfb值
2. 网络具体fcp值
3. 网络具体lcp值
4. 网络具体duration值
5. 网络具体资源数量
6. 网络具体资源大小
7. 网络具体资源类型
**二、静态资源优化建议**
给出简洁明确的静态资源优化建议（如：优化图片、减少请求、优化代码等），并说明主要依据（如性能指标、用户体验等）。
和我用中文对话，输出请保持专业、简洁、逻辑清晰，避免冗余语言。
`
}
}
};
module.exports = config;