LinkedIn CLI exhibits significant definition quality gaps. Tool names lack consistent verb prefixes (e.g., 'linkedin profile' vs 'get_profile'); descriptions are present but generic and often under 100 chars, providing minimal LLM guidance. Input schemas exist for some tools but are sparse, many parameters lack type constraints (e.g., 'section', 'period', 'vertical' are strings with no enums despite accepting only specific values). Output schemas are entirely undocumented. No error handling guidance, no pagination metadata, no chaining IDs in responses. Tools 10, 14, 17, 18, 19 are inferred from validation script rather than explicit registration, capping their scores at 50. Critical pattern violations: no verb-noun naming, missing parameter enums, no output documentation.

• Rename tools to verb_noun pattern: 'get_profile', 'list_connections', 'search_jobs', 'view_feed'. This enables LLMs to parse intent from names alone.
• Add enum constraints to all string parameters: section=['employees','posts'], vertical=['people','companies','jobs','posts'], sort=['recent','relevance','name']. Document in parameter descriptions.
• Document output schemas for all tools. Specify which fields are returned, their types, and which IDs enable chaining (e.g., profile_id for downstream calls).
• Add pagination support: accept limit (1-100, default 20) and offset/cursor parameters. Return total_count or next_cursor in responses to guide multi-page retrieval.
• Explicitly register all tools in source code (not via validation script). Move content, network, search, jobs, skill definitions into dedicated command files with full schemas and descriptions.