Skip to main content
Glama

Convex MCP server

Official
by get-convex
TableMetrics.tsx2 kB
import React from "react"; import { useDeploymentAuthHeader, useDeploymentUrl, } from "@common/lib/deploymentApi"; import { ChartModal } from "@common/elements/ChartModal"; import { calcBuckets } from "@common/lib/charts/buckets"; import { TableMetric, tableRate } from "@common/lib/appMetrics"; import { ChartData } from "@common/lib/charts/types"; const useChartData = ( deploymentUrl: string, tableName: string, metric: TableMetric, authHeader: string, name: string, color = "var(--chart-line-1)", ) => async (start: Date, end: Date): Promise<ChartData> => { const { startTime, endTime, numBuckets, timeMultiplier, formatTime } = calcBuckets(start, end); const buckets = await tableRate( deploymentUrl, tableName, metric, startTime, endTime, numBuckets, authHeader, ); const data = buckets.map((value) => value.metric ? { time: formatTime(value.time), metric: value.metric * timeMultiplier, } : { time: formatTime(value.time), metric: 0, }, ); const xAxisKey = "time"; const lineKeys = [ { key: "metric", name, color, }, ]; return { data, xAxisKey, lineKeys }; }; export function TableMetrics({ tableName, onClose, }: { tableName: string; onClose: () => void; }) { const deploymentUrl = useDeploymentUrl(); const authHeader = useDeploymentAuthHeader(); const readsSource = useChartData( deploymentUrl, tableName, "rowsRead", authHeader, " reads", ); const writesSource = useChartData( deploymentUrl, tableName, "rowsWritten", authHeader, " writes", "var(--chart-line-2)", ); return ( <ChartModal onClose={onClose} chartTitle="Metrics" entityName={tableName} dataSources={[readsSource, writesSource]} labels={["Reads", "Writes"]} /> ); }

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/get-convex/convex-backend'

If you have feedback or need assistance with the MCP directory API, please join our Discord server