INDIVIDUAL MCP TOOL

recommend_checkpoint_interval

Recommends how often to checkpoint a training job using the Young and Daly formulas, from GPU count, blocking checkpoint write time, restart time and job MTTF (estimated from Meta's published 2024 reliability data when not given).

xportl.ionone authenticationAvailability not checked

LIVE ENDPOINT

https://xportl.io/mcp

No auth detected

Connect to this endpoint to inspect the live schema for recommend_checkpoint_interval and invoke it with your own arguments.

Indexed input schema

{}

Risk classification

Potential side effects detected · medium confidence · heuristic, not a guarantee.

A tool name or description contains a write-action term.

Parent server

xportl.io

CONNECT 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.xportl]
url = "https://xportl.io/mcp"
enabled = true

Claude Code

.mcp.json

{
  "mcpServers": {
    "xportl": {
      "type": "http",
      "url": "https://xportl.io/mcp"
    }
  }
}

Claude Desktop

Settings → Connectors → Add custom connector

Name: xportl
Remote MCP URL: https://xportl.io/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": {
    "xportl": {
      "url": "https://xportl.io/mcp"
    }
  }
}

Visual Studio Code

.vscode/mcp.json

Add to Visual Studio Code

{
  "servers": {
    "xportl": {
      "type": "http",
      "url": "https://xportl.io/mcp"
    }
  }
}

Generic MCP

Client-specific MCP configuration

{
  "name": "xportl",
  "transport": "streamable-http",
  "url": "https://xportl.io/mcp"
}

MCP Inspector

Run the official MCP Inspector locally and enter the indexed Streamable HTTP endpoint.

Related tools

get_xportl_overview — Returns cite-ready facts about XportL from xportl.
analyze_nccl_benchmark — Analyzes nccl-tests / rccl-tests results (paste raw all_reduce_perf output, or give size/busbw points).
calculate_fabric_degradation_cost — Estimates what silent fabric degradation (slow NCCL collectives, flapping links, congestion) costs a GPU cluster: throughput lost, GPU-hours and dollars per year, idle-GPU equivalent, cost per degraded hour, extra days on a training run, and the value of a recovery scenario with a conservative case and a sensitivity table.
check_cluster_compatibility — Checks a GPU cluster against XportL's published support matrix (Linux 5.
request_enterprise_demo — Sends the user's contact details and cluster summary to the XportL enterprise team to arrange a technical assessment or demo deployment.