Score breakdown
Popularity is tracked separately. Support, ads, sponsorships, and tips never affect these signals.
Why it matters
Useful for teams that need to know which actor, tenant, session, or tool caused a data read when AI agents start touching real pipelines and warehouses.
Who should use it
Who should skip it
Move on from data-context-hq/datacontext if the licensing terms, language support, or platform requirements do not fit your project.
About this signal
data-context-hq/datacontext is tracked by RepoRadar as a python package in the AI Infrastructure section. It was first seen on 2026-06-28 and last updated on 2026-06-28. The current verdict is 'try now' with a Silver tier and moderate setup difficulty. The standout signals for data-context-hq/datacontext are workflow potential (9.0) and open-source/build quality (8.4), while momentum (5.0) trails — that balance shapes where it fits best. This page summarizes the evidence RepoRadar has captured from captured source metadata. The score, tier, risk label, and verdict on this page are never influenced by sponsorship, ads, or tips — they reflect only the usefulness, popularity, novelty, momentum, maturity, and evidence signals described in the RepoRadar methodology.
How this item is evaluated
RepoRadar assigned data-context-hq/datacontext a composite score of 7.9 out of 10, placing it in the Silver tier. This score combines weighted sub-signals: usefulness (35%), novelty (18%), momentum (14%), maturity (10%), open-source/build quality (7%), evidence quality (6%), workflow potential (6%), and setup ease (4%). Popularity is tracked separately at 1.0 and never affects the composite score or tier. The risk label of 'conditional' reflects inherent user-impacting hazards, not generic novelty. Items with no risk flag may still require normal code review before production use.
Putting this into practice? Read How to evaluate an AI tool before you adopt it for the checklist behind this score.
Risk explanation
Instrumentation can capture query context, actor identifiers, and session metadata, so scrub sensitive tenant or customer details before exporting events beyond a trusted environment; The package is still early and spans many optional integrations, so validate overhead and event shape in staging before relying on it for production audit flows.