MCP server

XportL Official

io.xportl/xportl

Checked 28 Oct 2026, 09:03 UTC · Connected to it and listed its tools

GPU fabric tools: NCCL benchmark analysis, degradation cost, cluster compatibility, XportL facts.

Website

At a glance

Type

MCP server

Publisher

xportl.io (verified by the MCP Registry)

Status

Working at last check

Tools

6

Token cost

About 3,292 tokens per request

Sets up in

Claude Code, Claude Desktop, Codex, Gemini CLI, Cursor, VS Code, Antigravity, ChatGPT, OpenAI API, Grok

Last checked

28 Oct 2026, 09:03 UTC

Connect it to your agent

Add to CursorAdd to VS CodeVS Code Insiders

Opens the app, which shows the server and asks you to confirm. For other apps, copy the setup below.

Claude Code

claude mcp add --transport http xportl https://xportl.io/mcp

Claude Desktop

Settings → Connectors → Add custom connector, and paste the URL.

Codex (config.toml)

[mcp_servers.xportl]
url = "https://xportl.io/mcp"

Gemini CLI

{"mcpServers":{"xportl":{"httpUrl":"https://xportl.io/mcp"}}}

Cursor

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

VS Code

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

Antigravity

agy mcp add xportl https://xportl.io/mcp

ChatGPT

Settings → Apps & Connectors → Advanced settings: turn on Developer mode, then Create a connector and paste the URL. Which plans can do this varies.

OpenAI API (Responses tools)

{"type":"mcp","server_label":"xportl","server_url":"https://xportl.io/mcp","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"xportl","server_url":"https://xportl.io/mcp"}

Tools

• analyze_nccl_benchmark ~704 tokens
  Analyzes nccl-tests / rccl-tests results (paste raw all_reduce_perf output, or give size/busbw points). Detects node and GPU counts from the output, compares large-message bus bandwidth with the fabric's NIC line rate (InfiniBand HDR/NDR/XDR, RoCE 100G to 800G), and flags data errors, degraded bandw
• calculate_fabric_degradation_cost ~706 tokens
  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
• check_cluster_compatibility ~618 tokens
  Checks a GPU cluster against XportL's published support matrix (Linux 5.8+ for eBPF, InfiniBand / RoCE v2 / NVLink, NVIDIA and AMD GPUs, NCCL / RCCL, on-prem / cloud / hybrid, host access for the eBPF agent). Returns a verdict (good fit / needs review / not ready), each check, and next steps. Anythi
• get_xportl_overview ~196 tokens
  Returns cite-ready facts about XportL from xportl.io: what it does, the problem it solves, the seven failure classes it detects, how it collects telemetry and acts, supported fabrics/GPUs/collectives/kernels, use cases, FAQ, company details (parent company, stage, NVIDIA Inception, contacts) and lin
• recommend_checkpoint_interval ~482 tokens
  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). Returns the recommended interval, expected failures per day, GPU time
• request_enterprise_demo ~586 tokens
  Sends the user's contact details and cluster summary to the XportL enterprise team to arrange a technical assessment or demo deployment. Before calling: collect name, work email, company and a summary (include benchmark, cost or compatibility results from this conversation), show the user exactly wh

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