INDIVIDUAL MCP TOOL

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.

xportl.ionone authenticationAvailability not checked

LIVE ENDPOINT

https://xportl.io/mcp

No auth detected

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

{}

Inferred read-only · medium confidence · heuristic, not a guarantee.

No write-capable action terms were found; this is not proof that invocation has no side effects.

CONNECT WITH APPROVAL

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.

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.
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).