Server has 3 tools with basic schemas and descriptions, but significant quality gaps. All tools have input schemas with types and descriptions, meeting minimum requirements. However, descriptions are generic and lack LLM-optimization guidance. Output schemas are completely undocumented, tools return unstructured strings (JSON dumps or concatenated text) with no field documentation. Error handling is minimal: exceptions are caught but responses are bare error strings without recovery guidance. No parameter validation, no enum constraints, no pagination metadata (total count, next_cursor). The get_feed_posts tool has a critical bug: it references undefined variable 'api' instead of 'client'. Tool names follow verb_noun convention (good), but descriptions lack context about when to use each tool vs alternatives.

• Document output schemas for all tools. Define structured return objects with typed fields (e.g., {profile: {name, headline, location, ...}, metadata: {retrieved_at, source}}). Return JSON objects, not concatenated strings.
• Fix get_feed_posts: change 'api.get_feed_posts()' to 'client.get_feed_posts()' to resolve undefined variable bug.
• Add pagination metadata: return {results: [...], total_count: N, next_offset: M} so LLMs know when more results exist and can plan multi-page fetches.
• Enhance error messages with recovery guidance: 'User not found. Try search_users() with a partial name.' or 'Rate limited. Retry after 60 seconds.' Categorize errors as retryable vs fatal.
• Expand tool descriptions to 100-150 chars. Include WHEN to use (e.g., 'Use to retrieve a specific user's profile when you have their LinkedIn public ID'), WHAT it returns (field names), and any prerequisites (e.g., 'Requires valid LinkedIn credentials in LINKEDIN_EMAIL and LINKEDIN_PASSWORD environment variables').

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

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