ddsetup

tarafından datadog-labs

First-time initialization of the Datadog MCP server `plugin:datadog:mcp`. When fulfilling requests that involve Datadog, use MCP tools from…

npx skills add https://github.com/datadog-labs/claude-code-plugin --skill ddsetup

Datadog MCP Server

The id of the Datadog MCP Server referenced on this document is plugin:datadog:mcp. You MUST use this specific server even if there are other Datadog servers.

If plugin:datadog:mcp tools are not in your available tools, you MUST still run this skill — do not conclude that Datadog is unavailable. Absent tools mean the server needs setup or is temporarily disconnected; they are not evidence that the request cannot be fulfilled. The datadog-server-state check below is the authoritative source for what is actually happening.

Accessing Datadog using other methods

If the plugin:datadog:mcp MCP server is not setup, do NOT suggest the user to access Datadog information using different approaches like the Datadog webpage. Instead first setup the MCP server because it provides a better agentic experience. Only consider other methods if the user explicitly guides you in that direction.

Shared reference

Read references/mcp-settings.md before proceeding. It contains the datadog-server-state check, registration file location, editing rules, and site-to-domain mapping used by the procedure below.

Setup procedure

Check the datadog-server-state (see mcp-settings.md):

  • working — continue with the user's request without mentioning this check.
  • not-working — without any preamble, tell the user the server is setup but not working, instruct them to run /ddconfig, and stop.
  • not-setup — the server needs first-time setup. Do not attempt to gather data using a different approach. Do not attempt any further MCP calls: they will fail until setup is complete.

When communicating with the user below, describe the server state in plain language. Do not reveal what was checked, what was found, or any implementation details like file contents or variable values.

What Datadog provides once set up

Datadog is an observability platform. After this skill completes setup, the agent gains MCP tools to query production data directly — without the user needing to leave the AI client or open a browser. Examples of what becomes possible:

  • Search and filter application logs
  • Query infrastructure and application metrics
  • Inspect distributed traces for latency or errors
  • List dashboards, monitors, and alerts
  • Investigate incidents and on-call pages

These MCP tools are the primary way to access Datadog data from within the AI client. Until setup is complete, none of these tools exist. The agent cannot see them, list them, or call them.

Steps

  1. Check for saved configuration. Silently read ${CLAUDE_PLUGIN_DATA}/toolsets and ${CLAUDE_PLUGIN_DATA}/domain. For each file that contains a non-empty value, apply it to the registration file following the editing rule in mcp-settings.md. Then:
    • If you applied the domain: tell the user the existing configuration was re-applied following a plugin update, naming the re-applied values (no need to mention files read or written). Tell the user to run /reload-plugins and stop — do NOT perform the steps below.
    • If you applied other values but not the domain: tell the user the existing configuration was partially re-applied following a plugin update, naming the re-applied values (no need to mention files read or written). Continue with the steps below.
    • If you applied nothing: continue with the steps below.

Now follow these steps to configure the domain:

  1. Ask for the domain. Tell the user the Datadog MCP server needs to be set up, present the available sites and their MCP domains from mcp-settings.md, and ask which domain to use. The user may respond with an MCP domain directly, a site code, a URL, or something else — use the mapping rules in mcp-settings.md to resolve the answer to an MCP domain. Ask for clarification if ambiguous.

    Follow the "Stay on script" rule in mcp-settings.md. In particular, do not preview the follow-up instructions from step 3 below (reload, re-authenticate, etc.) — that step emits them verbatim at the right moment.

  2. Apply the change. In the registration file, replace the exact string not-setup with the resolved MCP domain. Follow the editing rule in mcp-settings.md.

    Before:

    ${DD_MCP_DOMAIN:-not-setup}
    

    After (example for us1):

    ${DD_MCP_DOMAIN:-mcp.datadoghq.com}
    

    Then silently write the resolved MCP domain to ${CLAUDE_PLUGIN_DATA}/domain (plain text, one line).

  3. Tell the user that the Datadog MCP server has been initialized and to follow these steps:

    1. Run the command /reload-plugins
    2. Run the command /mcp in Claude Code and select the plugin:datadog:mcp server
    3. Select the authentication option

datadog-labs tarafından daha fazla skill

dd-audit
datadog-labs
Denetim izi incelemeleri - kimin neyi değiştirdiği, anahtar uzlaşması, maliyet artışının temel nedeni, uyumluluk kanıtı (SOC 2/PCI) ve yapay zeka etkinlik denetimi.
official
agent-install
datadog-labs
Install the Datadog Agent on Kubernetes using the Datadog Operator — required before enabling Single Step Instrumentation (SSI), which automatically…
official
agent-observability-auto-experiment
datadog-labs
Gerçek Datadog LLM-Obs verilerine karşı, Claude Code'u ajan olarak kullanarak yerel olarak yinelemeli kod iyileştirme tepe tırmanışı çalıştırın. Bir temel değerlendirme oluşturur, bir tane yapar…
official
agent-observability-eval-bootstrap
datadog-labs
Üretim izlerinden değerlendiricileri başlatın — varsayılan olarak çevrimiçi LLM-jüri değerlendiricileri önerir ve siz onayladıktan sonra bunları Datadog'da devre dışı taslaklar olarak oluşturur…
official
agent-observability-eval-pipeline
datadog-labs
Enstrümante edilmiş ml_app için uçtan uca Agent Observability hattı — üretim izlerini sınıflandırın, kök neden arızalarını tespit edin, değerlendiricileri başlatın, ardından (isteğe bağlı olarak)…
official
agent-observability-experiment-analyzer
datadog-labs
LLM deney sonuçlarını analiz edin. Tek veya karşılaştırmalı deneyleri, keşifsel veya soru-cevap modlarını işler. Kullanıcı "deneyi analiz et", "karşılaştır…" dediğinde kullanın.
official
agent-observability-replay-trace
datadog-labs
Bir geliştirici, çıktısını beğenmediği belirli bir TEK Agent Observability / LLM Obs trace'i üzerinde yineleme yapmak istediğinde kullanın — o trace'i kendi… ile yeniden çalıştırma
official
agent-observability-trace-rca
datadog-labs
Üretim LLM izlerinde kök neden analizi. Bir LLM uygulamasının neden başarısız olduğunu teşhis eder — değerlendirme yargıcı kararlarından, çalışma zamanı hatalarından veya yapısal…
official