Install payload
Install payload is mixed and needs spot-checking.
50% (2/4)
Source-backed filter active — add entries to compare trust side by side.
4 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. 0 entries have 2+ required gaps.
Install payload
Install payload is mixed and needs spot-checking.
50% (2/4)
Adoption queue
2/4 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/opik-mcp-server · trust review · confidence 67%
2 blockers: Metadata review, Install payload
36/100
Request metadata review from maintainers or internal owners.
Add install/config payload for reproducible team rollout.
Collect package checksum or signed artifact information.
tools/agenta · trust review · confidence 50%
2 blockers: Metadata review, Install payload
36/100
Request metadata review from maintainers or internal owners.
Add install/config payload for reproducible team rollout.
Collect package checksum or signed artifact information.
tools/mlflow · trust review · confidence 50%
Decision confidence
2/4 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/opik-mcp-server · trust review
Hold adoption until Metadata review, Package integrity are resolved.
36/100
tools/agenta · trust review
Hold adoption until Metadata review, Package integrity are resolved.
36/100
tools/mlflow · trust review
Freshness distribution
Median age 48 days; all 4 scanned entries are within 90 days.
Theme distribution
100% of this view shares the top theme. Leading themes: prompt-management, evaluation, evals.
12 distinct themes across 4 scanned
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.
Debug, evaluate, and monitor LLM applications from Claude — read traces and spans, score outputs, save prompts, run evaluation experiments, and query project metrics — with the official Opik MCP server by Comet.
Open-source LLMOps platform for prompt management, prompt versioning, evaluation, and observability across LLM applications.
Open-source AI engineering platform for tracing, evaluating, prompt-managing, and deploying agents, LLM applications, and ML models.