A Model Context Protocol (MCP) server that allows AI assistants to read text files stored in AWS S3 buckets
Two tools with basic schemas and descriptions, but significant gaps in parameter documentation and output schema clarity. Tool names follow verb_noun convention (getS3Object, listS3Objects), which is good. However, parameter descriptions lack detail on constraints, formats, and error recovery. Output schemas are implicit (text content only) with no documented structure for downstream tool chaining. Error handling returns plain text errors rather than structured guidance. No pagination support for listS3Objects despite potential for large result sets. Missing parameter descriptions for optional 'prefix' parameter.
Fetch text file content from AWS S3
List files (keys) in an AWS S3 bucket
listS3Objects lacks pagination support. No limit, offset, or next_cursor parameters despite potential for large S3 buckets. Returns all matching keys without truncation, risking context window exhaustion.
Output schemas not documented. Both tools return implicit text content with no structured field definitions. Downstream tools cannot reliably extract bucket names, keys, or metadata for chaining.
Parameter 'prefix' in listS3Objects is marked optional but has no description explaining its purpose, format, or behavior when omitted.
Error messages are plain text without recovery guidance. 'Error fetching S3 object: <message>' gives no hint whether to retry, check credentials, or verify bucket/key existence.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 48 | 2026-07-28+ | v2 |
No input validation or constraint documentation. Parameters accept any string; no guidance on bucket naming rules, key path format, or maximum lengths.