16 tools with complete schemas and descriptions. Naming is consistent (verb_noun pattern: linkedin_*). Descriptions are adequate (100-200 chars typical) but lack actionable context for LLM selection. Parameters are typed and mostly described, but lack constraints (enums, ranges, format hints). Output schemas are not documented, LLMs cannot predict response structure. Error handling is absent from tool definitions. No tool annotations (readOnlyHint, destructiveHint, idempotentHint) despite clear risk levels (READ_ONLY, WRITE, DESTRUCTIVE). Missing dependency hints and recovery guidance.

• Add output schemas to all tools. Document the response structure: e.g., linkedin_create_post returns {post_id: string, created_at: ISO8601, visibility: string}. This enables LLMs to chain tools and extract IDs for follow-up calls.
• Implement tool annotations in the MCP schema. Add readOnlyHint=true to all linkedin_get_* tools, destructiveHint=true to linkedin_delete_*, and idempotentHint=true to linkedin_get_* tools. This signals to LLMs which tools are safe to retry.
• Enhance descriptions with actionable context. E.g., 'Get follower statistics (location, seniority, industry breakdown). Call this to analyze audience demographics before targeting posts. Returns counts by segment and growth trends.' (120-150 chars).
• Add JSON Schema constraints: minLength/maxLength for text fields, minimum/maximum for count/start, pattern for URN formats. E.g., text: {type: string, maxLength: 3000, description: 'Post text (max 3000 chars)'}.
• Document error scenarios and recovery paths in descriptions. E.g., linkedin_create_post: 'If media_id is invalid, call linkedin_init_image_upload() first. If article_url is provided, article_title and article_description are required.'