Install payload
Install payload is mixed and needs spot-checking.
67% (2/3)
Select entries to compare install and trust signals side by side.
3 results in this view
1 trust signal differs in this sample: Submitter
Signals differ on Submitter — add entries to compare before you install.
Rollout signal scan
Biggest gaps: metadata review, package integrity. 1 entries have 2+ required gaps.
Install payload
Install payload is mixed and needs spot-checking.
67% (2/3)
Most at-risk entries in this view
Adoption queue
1/3 visible results are in hold tier and need mitigation before adoption.
1 blockers: Metadata review
50/100
Request metadata review from maintainers or internal owners.
Collect package checksum or signed artifact information.
mcp/arize-phoenix-mcp-server · trust review · confidence 67%
1 blockers: Metadata review
50/100
Request metadata review from maintainers or internal owners.
Collect package checksum or signed artifact information.
mcp/mcp-nixos · trust review · confidence 67%
3 blockers: Metadata review, Safety notes
10/100
Request metadata review from maintainers or internal owners.
Capture safety notes with misuse/guardrail guidance.
Add install/config payload for reproducible team rollout.
tools/arize-phoenix · trust review · confidence 17%
Decision confidence
1/3 results are low-confidence and need review before adoption.
Address Metadata review, Package integrity before broader rollout.
54/100
mcp/arize-phoenix-mcp-server · trust review
Address Metadata review, Package integrity before broader rollout.
54/100
mcp/mcp-nixos · trust review
Hold adoption until Metadata review, Safety notes are resolved.
10/100
tools/arize-phoenix · trust review
Freshness distribution
Median age 45 days; all 3 scanned entries are within 90 days.
Theme distribution
67% of this view shares the top theme. Leading themes: evaluation, ai-observability, arize-phoenix.
13 distinct themes across 3 scanned
MCP server for live NixOS, nixpkgs, Home Manager, nix-darwin, Nixvim, FlakeHub, Noogle, NixOS Wiki, nix.dev, NixHub, binary cache, and local flake input lookups.
Inspect LLM traces and spans, manage prompts, explore datasets, and review evaluation experiments from Claude — with the official Arize Phoenix MCP server, built into the open-source Phoenix AI observability platform.
Open-source observability and evaluation tooling for LLM applications, traces, datasets, and experiments.