A collection of Python recipes demonstrating agentic AI workflows with XNS cloud storage, including document parsing, RAG, checkpointing, and multi-agent coordination
Three tools with basic schemas and descriptions, but significant gaps in parameter documentation and output schema clarity. write_artifact and read_artifact have minimal descriptions (under 100 chars). No parameter type constraints (enums, ranges). Output schemas not documented. No error handling guidance. Naming is verb-noun compliant but descriptions lack WHEN/WHY context. No security considerations documented despite write_artifact being a WRITE operation.
List object keys in the shared workspace under a prefix.
Fetch the text stored at an object key in the shared workspace.
Store text in the shared workspace under an object key. Use this to hand work to another agent instead of pasting it into your answer.
Descriptions lack WHEN/WHY context. 'Store text in the shared workspace' does not explain when to use write_artifact vs alternatives, prerequisites, or consequences. LLMs cannot determine tool selection without this context.
Output schemas not documented. Callers cannot infer what write_artifact returns (success boolean? object key? metadata?). read_artifact return type is unstated. list_artifacts result structure (array of strings? objects with metadata?) is unclear.
No error handling guidance. If write_artifact fails due to permission denied, quota exceeded, or invalid key format, the tool provides no recovery path. LLMs cannot self-correct without actionable error messages.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 44 | 2026-07-28+ | v2 |
write_artifact is marked WRITE but has no confirmation/dry-run pattern. Agents could accidentally overwrite critical shared state. No idempotency guarantee stated.
Parameter 'prefix' in list_artifacts has a default ('') but no constraint on length, character set, or max results. Unbounded prefix could return thousands of keys, exhausting context window.