AI & Machine Learning
llmlatency.dev
Provides latency and uptime metrics for AI inference APIs across regions.
ENDPOINT 1
https://llmlatency.dev/mcp
MCP server metadata
- Name
- llmlatency-mcp
- Version
- 1.0.0
Known tools 2
get_ai_api_latencyMeasured latency (TTFB p50/p95) and uptime rankings of AI inference API providers by region, from llmlatency.
Inferred read-onlyget_model_deprecationsAI model deprecation calendar: announced and shutdown dates, replacement models, and how many days of migration notice each provider actually gives (median/min/max).
Inferred read-onlyCONNECT WITH APPROVAL
Client installation
Review this server and its permissions before adding it. Secret placeholders must be set locally.
Codex
~/.codex/config.toml
[mcp_servers.llmlatency-mcp]
url = "https://llmlatency.dev/mcp"
enabled = true
Claude Code
.mcp.json
{
"mcpServers": {
"llmlatency-mcp": {
"type": "http",
"url": "https://llmlatency.dev/mcp"
}
}
}
Claude Desktop
Settings → Connectors → Add custom connector
Name: llmlatency-mcp
Remote MCP URL: https://llmlatency.dev/mcp
Add this remote URL as a custom connector in Claude Desktop. Availability depends on the user plan and workspace policy.
Cursor
.cursor/mcp.json
{
"mcpServers": {
"llmlatency-mcp": {
"url": "https://llmlatency.dev/mcp"
}
}
}
Visual Studio Code
.vscode/mcp.json
Add to Visual Studio Code{
"servers": {
"llmlatency-mcp": {
"type": "http",
"url": "https://llmlatency.dev/mcp"
}
}
}
Generic MCP
Client-specific MCP configuration
{
"name": "llmlatency-mcp",
"transport": "streamable-http",
"url": "https://llmlatency.dev/mcp"
}
MCP Inspector
Run the official MCP Inspector locally and enter the indexed Streamable HTTP endpoint.
TRUST AND VERIFICATION EVIDENCE
Trust Data Available
BuiltWith Trust API v2 evidence for llmlatency.dev was fetched 2026-08-04T02:55:05.943Z and is being refreshed.
llmlatency.dev is assessed as Neutral: No suspicious signals found, but no strong positive signal either
Evidence is source-attributed and does not guarantee that a third-party server is safe. Risk labels are conservative metadata heuristics.