Score breakdown
Popularity is tracked separately. Support, ads, sponsorships, and tips never affect these signals.
Why it matters
Useful for structural-biology teams who already use PyMOL and want an agent to drive real session analysis without replacing the visualization tool they trust.
Who should use it
Who should skip it
Skip Arcadia-Science/agentic-pymol if you cannot isolate its execution environment or audit what data it touches before connecting anything sensitive.
About this signal
Arcadia-Science/agentic-pymol is tracked by RepoRadar as a mcp server in the Science Tools section. It was first seen on 2026-06-26 and last updated on 2026-06-26. The current verdict is 'try now' with a Silver tier and moderate setup difficulty. Across RepoRadar's eight signals, Arcadia-Science/agentic-pymol is strongest on novelty (9.0) and workflow potential (8.9) and weakest on momentum (6.0) — a profile worth weighing against your own priorities. 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 Arcadia-Science/agentic-pymol a composite score of 7.8 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 77.0 and never affects the composite score or tier. The risk label of 'medium' 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 vet an AI agent or MCP server before you wire it in for the checklist behind this score.
Risk explanation
A connected agent can manipulate the live PyMOL session and generate exports, so it should run only in a trusted local research environment; Rendered views, alignments, and contact reports still need domain review because a fluent agent can present a bad molecular interpretation confidently; The setup depends on a working PyMOL installation plus MCP client configuration, so teams should confirm the environment before evaluating the analysis quality.