Four tools with basic verb-noun naming (get_*, search_) and minimal descriptions (10-40 chars). All tools have input schemas with type definitions and parameter descriptions, meeting baseline requirements. However, descriptions are too brief to guide LLM selection effectively, they lack context on WHEN to use each tool, dependencies, or return value structure. No output schemas documented. Error handling returns generic error dicts without recovery guidance. Tools lack idempotency hints, destructive/readonly annotations, and pagination support for search results.

• Expand tool descriptions to 50-200 chars. Example: 'Get detailed package metadata including author, license, dependencies, and release history. Use this to understand a package before installing. Returns info dict with keys: name, version, author, license, requires_python, classifiers.'
• Document output schemas for each tool. Example for get_package_info: 'Returns {name: str, version: str, author: str, license: str, summary: str, requires_python: str, classifiers: [str]}'
• Add pagination to search_packages: accept limit (1-100, default 20) and offset parameters; return {results: [...], total: int, has_more: bool}.
• Enhance error messages with recovery hints. Example: 'Package "typo-pkg" not found. Did you mean: typo, typo-lib? Try search_packages("typo") to find alternatives.'
• Add tool annotations: mark all four tools with readOnlyHint=true since they only query PyPI and never modify state.

Inferred effective spec: <=2025-11-25.

• Relies on Logging (deprecated) - log to stderr or use OpenTelemetry