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BC datadog-mcp

Datadog observability via the official MCP Server — query logs, traces, metrics, monitors, incidents, dashboards, hosts, synthetics, and workflows through Datadog's remote MCP endpoint. Use when investigating production issues, checking monitor status, searching logs/traces, querying metrics timeseries, managing incidents, or listing dashboards and synthetic tests. Supports both remote (Streamable HTTP) and local (stdio) MCP transports. Requires DD_API_KEY and DD_APP_KEY.

ClawHub Agent Skills author: Brandon Wilson v1.1.1 MIT-0 10 files · 1 script body ≈ 907 tokens Open the sourceclawhub.ai↗ analyzed 13 d ago

Datadog observability via the official MCP Server — query logs, traces, metrics, monitors, incidents, dashboards, hosts, synthetics, and workflows through…

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, failures and branches

ProcedureAI and agentsData and analyticstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
B
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
84
Run on models
none yet
Process rating
C
53/100
Has gaps
Result and completion w 14
0
Failures and branches w 10
0
When it triggers w 12
20
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 0

    ✓ No critical or high findings

    Files scanned: 10. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "homepage"

    Process rating: all ten parameters 53/100

    • 0Result and completion. Does not say what the result is
    • 0Failures and branches. Linear process with no failure handling
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 2 mutating operations with no state check
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 13 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 907 tokens
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • -31 of 1 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +3Description length 476: enough signal without eating the budget
    • +4Structure: 11 headings
    • +3Step-by-step instructions: 13 items
    • +4Has examples (3 code blocks)
    • +4Reference files are cited in the instructions (3 of 5)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.

    External checks

    ClawHub: suspicious
    This Datadog MCP skill is coherent, but users should review it because its setup can expose Datadog credentials through an unvalidated site value and an unpinned local npm package.
    LLM: suspicious (high) · VirusTotal: · 12 Sept 2026