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github/arize-link

github

arize-link

Generates deep links to the Arize UI for traces, spans, sessions, datasets, labeling queues, evaluators, and annotation configs. Produces clickable URLs for sharing Arize resources with team members. Use when the user wants to link to or open a trace, span, session, dataset, evaluator, or annotation config in the Arize UI.

global
author:arize
version:1.0
New~1.1k
v1.0Saved Jun 26, 2026

Arize Link

Generate deep links to the Arize UI for traces, spans, sessions, datasets, labeling queues, evaluators, and annotation configs.

When to Use

  • User wants a link to a trace, span, session, dataset, labeling queue, evaluator, or annotation config
  • You have IDs from exported data or logs and need to link back to the UI
  • User asks to "open" or "view" any of the above in Arize

Required Inputs

Collect from the user or context (exported trace data, parsed URLs):

Always required Resource-specific
org_id (base64) project_id + trace_id [+ span_id] — trace/span
space_id (base64) project_id + session_id — session
dataset_id — dataset
queue_id — specific queue (omit for list)
evaluator_id [+ version] — evaluator

All path IDs must be base64-encoded (characters: A-Za-z0-9+/=). A raw numeric ID produces a valid-looking URL that 404s. If the user provides a number, ask them to copy the ID directly from their Arize browser URL (https://app.arize.com/organizations/{org_id}/spaces/{space_id}/…). If you have a raw internal ID (e.g. Organization:1:abC1), base64-encode it before inserting into the URL.

URL Templates

Base URL: https://app.arize.com (override for on-prem)

Trace (add &selectedSpanId={span_id} to highlight a specific span):

{base_url}/organizations/{org_id}/spaces/{space_id}/projects/{project_id}?selectedTraceId={trace_id}&queryFilterA=&selectedTab=llmTracing&timeZoneA=America%2FLos_Angeles&startA={start_ms}&endA={end_ms}&envA=tracing&modelType=generative_llm

Session:

{base_url}/organizations/{org_id}/spaces/{space_id}/projects/{project_id}?selectedSessionId={session_id}&queryFilterA=&selectedTab=llmTracing&timeZoneA=America%2FLos_Angeles&startA={start_ms}&endA={end_ms}&envA=tracing&modelType=generative_llm

Dataset (selectedTab: examples or experiments):

{base_url}/organizations/{org_id}/spaces/{space_id}/datasets/{dataset_id}?selectedTab=examples

Queue list / specific queue:

{base_url}/organizations/{org_id}/spaces/{space_id}/queues
{base_url}/organizations/{org_id}/spaces/{space_id}/queues/{queue_id}

Evaluator (omit ?version=… for latest):

{base_url}/organizations/{org_id}/spaces/{space_id}/evaluators/{evaluator_id}
{base_url}/organizations/{org_id}/spaces/{space_id}/evaluators/{evaluator_id}?version={version_url_encoded}

The version value must be URL-encoded (e.g., trailing =%3D).

Annotation configs:

{base_url}/organizations/{org_id}/spaces/{space_id}/annotation-configs

Time Range

CRITICAL: startA and endA (epoch milliseconds) are required for trace/span/session links — omitting them defaults to the last 7 days and will show "no recent data" if the trace falls outside that window.

Priority order:

  1. User-provided URL — extract and reuse startA/endA directly.
  2. Span start_time — pad ±1 day (or ±1 hour for a tighter window).
  3. Fallback — last 90 days (now - 90d to now).

Prefer tight windows; 90-day windows load slowly.

Instructions

  1. Gather IDs from user, exported data, or URL context.
  2. Verify all path IDs are base64-encoded.
  3. Determine startA/endA using the priority order above.
  4. Substitute into the appropriate template and present as a clickable markdown link.

Troubleshooting

Problem Solution
"No data" / empty view Trace outside time window — widen startA/endA (±1h → ±1d → 90d).
404 ID wrong or not base64. Re-check org_id, space_id, project_id from the browser URL.
Span not highlighted span_id may belong to a different trace. Verify against exported span data.
org_id unknown ax CLI doesn't expose it. Ask user to copy from https://app.arize.com/organizations/{org_id}/spaces/{space_id}/….
  • arize-trace: Export spans to get trace_id, span_id, and start_time.

Examples

See references/EXAMPLES.md for a complete set of concrete URLs for every link type.

Files2
2 files · 3.0 KB

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Overall Score

88/100

Grade

A

Excellent

Safety

95

Quality

87

Clarity

88

Completeness

82

Summary

This skill generates deep links to the Arize UI for traces, spans, sessions, datasets, queues, evaluators, and annotation configs. It provides URL templates with required ID parameters (base64-encoded), guidance on time range selection, and troubleshooting steps. The skill is read-only and does not perform file operations or shell execution.

Detected Capabilities

URL generationID validation (base64 encoding)Time range calculationMarkdown link formatting

Trigger Keywords

Phrases that MCP clients use to match this skill to user intent.

share trace linkarize trace urldeep link to arizeannotate datasetevaluator version linklabeling queue url

Risk Signals

INFO

External domain reference (app.arize.com)

SKILL.md (base URL and examples)

Referenced Domains

External domains referenced in skill content, detected by static analysis.

app.arize.com

Use Cases

  • Share a trace with team members via a direct link
  • Generate a deep link to a specific span within a trace
  • Create clickable URLs for dataset examples or experiments
  • Link to a labeling queue for review and annotation
  • Generate URLs for evaluators at specific versions
  • Troubleshoot missing data by adjusting time window parameters

Quality Notes

  • Well-organized URL templates for all supported resource types
  • Clear priority order for time range selection reduces common misconfigurations
  • Troubleshooting table directly addresses user pain points
  • Examples file provides concrete reference URLs for each link type
  • Base64 encoding requirement is clearly documented with guidance on how to identify base64-encoded vs. raw IDs
  • On-premises override guidance shows awareness of deployment variations
  • Related skills section connects to arize-trace for data export workflow
  • Time window logic is thorough and includes fallback strategy
Model: claude-haiku-4-5-20251001Analyzed: Jun 26, 2026

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