Three tools with basic descriptions and input schemas, but significant gaps in parameter documentation and output schema clarity. All tools are READ_ONLY and well-intentioned, but descriptions lack actionable context for LLM selection. Parameter descriptions are minimal (e.g., 'The name of the package to query' is generic). No output schemas documented. Error handling is implicit (httpx.raise_for_status) with no recovery guidance. Tool names follow verb_noun convention (get_*, check_*) which is good, but descriptions don't explain WHEN to use each tool vs. alternatives or what downstream tools expect.

• Document output schemas for all three tools. E.g., get_package_health returns {name: string, version: string, summary: string, last_release: ISO8601 date, license: string, project_url: string}. This enables LLMs to chain tools and extract required fields.
• Enhance parameter descriptions with constraints and examples. E.g., 'package_name (string, case-insensitive): The PyPI package name, e.g., 'requests' or 'numpy'. If not found, the tool returns an error with suggestions for similar names.'
• Add explicit error handling with recovery guidance. Catch httpx.HTTPError and return structured errors like {error: 'package_not_found', message: 'Package "xyz" not found on PyPI.', suggestions: ['Did you mean: xyz-utils, xyz-core?']}.
• Expand tool descriptions to clarify selection criteria. E.g., 'Use get_package_health to assess maintenance status and licensing. Use check_vulnerabilities to find known CVEs. Use get_dependency_graph to understand supply-chain risk.'
• Replace fragile string parsing in get_dependency_graph with a proper requirements parser (e.g., packaging.requirements.Requirement) to handle edge cases and return clear errors if parsing fails.