Two tools with clear verb-noun naming (linkedin_create_post, linkedin_get_profile). Both have descriptions (60-80 chars, within baseline 34-392 range). Input schemas present for both: linkedin_create_post has a 'text' parameter with type and description; linkedin_get_profile has empty input. Error handling is comprehensive with redaction of sensitive data and categorized error codes. However, output schemas are not documented, LLMs cannot predict response structure. Parameter descriptions are minimal (e.g., 'text' lacks format constraints like max 3000 chars in the schema itself, only in code). No tool annotations (readOnlyHint, destructiveHint, idempotentHint). linkedin_create_post is a write operation but lacks explicit confirmation/dry-run pattern.

• Document output schemas for both tools. Example: linkedin_create_post returns {success: bool, data: {post_id: string, created_at: ISO8601}, message?: string} or {success: false, error: {code: string, message: string, details: object}}.
• Add tool annotations: mark linkedin_create_post with destructiveHint=true and idempotentHint=false to signal write semantics and retry risk.
• Expand linkedin_get_profile description to explain what fields are returned (name, email, profile_url, etc.) and when to call it (e.g., 'Call before creating a post to verify authenticated user identity').
• Add format and constraint details to 'text' parameter description: 'Plain text only (no HTML/markdown). Max 3000 characters. Newlines and URLs allowed. Hashtags and @mentions supported.'
• Implement a dry-run or preview mode for linkedin_create_post: add optional 'preview' parameter (bool, default false) that returns the post content without publishing, allowing agents to confirm before committing.