emdb-analytics

name	emdb-analytics
description	View EmergentDB analytics and usage stats. Use when the user wants to check API usage, latency, errors, growth, or per-key stats.
allowed-tools	Bash, Read, Write, Edit

EmergentDB Analytics

Help the user retrieve analytics and usage data from their EmergentDB account.

TypeScript SDK

import { EmergentDB } from "emergentdb";

const db = new EmergentDB("emdb_your_api_key");

// Request stats by endpoint (last 30 days)
const endpoints = await db.analyticsEndpoints();
// [{ endpoint, requestCount, totalBytes, avgLatencyMs, p95LatencyMs, errorCount }]

// Usage by namespace (last 30 days)
const namespaces = await db.analyticsNamespaces();
// [{ namespace, requestCount, totalVectors, avgLatencyMs }]

// Latency percentiles by day (last 30 days)
const latency = await db.analyticsLatency();
// [{ date, p50, p95, p99, requestCount }]

// Error rates by day (last 30 days)
const errors = await db.analyticsErrors();
// [{ date, totalRequests, errorCount, error4xx, error5xx }]

// Per-API-key usage (last 30 days)
const keys = await db.analyticsKeys();
// [{ apiKeyId, keyName, keyPrefix, requestCount, totalBytes, avgLatencyMs, lastUsed }]

// Vector count growth (daily snapshots, last 90 days)
const growth = await db.analyticsGrowth();
// [{ date, vectorCount }]

Python SDK

from emergentdb import EmergentDB

db = EmergentDB("emdb_your_api_key")

endpoints  = db.analytics_endpoints()   # request stats per endpoint
namespaces = db.analytics_namespaces()  # usage per namespace
latency    = db.analytics_latency()     # p50/p95/p99 by day
errors     = db.analytics_errors()      # 4xx/5xx counts by day
keys       = db.analytics_keys()        # per-API-key stats
growth     = db.analytics_growth()      # vector count by day (90 days)

Available Analytics

Method	Data	Window
analyticsEndpoints / analytics_endpoints	Requests, bytes, latency per endpoint	30 days
analyticsNamespaces / analytics_namespaces	Requests, vectors, latency per namespace	30 days
analyticsLatency / analytics_latency	p50, p95, p99 latency by day	30 days
analyticsErrors / analytics_errors	Error counts (4xx, 5xx) by day	30 days
analyticsKeys / analytics_keys	Usage stats per API key	30 days
analyticsGrowth / analytics_growth	Total vector count by day	90 days

Error Codes

Code	Meaning
401	Missing or invalid API key
429	Rate limit exceeded
500	Server error — retry with backoff

Plans & Limits

Plan	Vectors	Price
Free	10,000	$0/mo
Launch	500,000	$29/mo
Scale	2,500,000	$99/mo

When helping the user, suggest the right analytics method based on what they want to understand (performance, errors, growth, etc.).