The server defines 4 tools with explicit schemas and basic descriptions. However, several critical issues prevent a higher score: (1) descriptions are minimal (most under 80 chars, well below the 194-char baseline for A+ tools); (2) parameter descriptions are generic or missing ('visibility' enum lacks explanation of what PUBLIC vs CONNECTIONS means); (3) no output schema documentation, callers must infer what fields are returned; (4) error handling is present but not actionable (returns generic success strings rather than structured data with field-level guidance); (5) tool names are adequate but descriptions lack the WHEN and WHY context that guides LLM selection. Most parameters have types, but descriptions fail to explain constraints, formats, or dependencies.

• Expand each tool description to 150-250 chars, answering: WHAT does it do? WHEN should the LLM call it instead of a similar tool? WHAT are prerequisites (e.g., valid authentication)? Example for create_linkedin_post: 'Create a text-only LinkedIn post visible to a specified audience (PUBLIC or CONNECTIONS only). Use this when sharing text updates. Requires valid LinkedIn authentication. Returns post ID for reference.'
• Add explicit descriptions to every parameter. For 'visibility', explain: 'Post visibility scope. PUBLIC = visible to all LinkedIn users; CONNECTIONS = visible only to your direct connections. Default: PUBLIC.' For 'imageUrl', add: 'HTTPS URL of the image to post. Must be a valid, accessible URL returning a 2xx status. Supported formats: JPEG, PNG, GIF. Maximum file size: 5MB.'
• Document the output schema for each tool in the description or as a JSON Schema example. Example: 'Returns {postId: string, status: 'success'|'pending', message: string, createdAt: ISO8601string}'. This allows LLMs to extract and chain results programmatically.
• Implement structured error responses. Instead of plain text, return {error: {code: 'INVALID_PARAM', message: 'imageUrl must be HTTPS', recoveryHint: 'Ensure the URL starts with https:// and the domain is reachable'}}. This guides the LLM to self-correct.
• Add tool annotations (readOnlyHint, destructiveHint) to the tool response. Mark create_linkedin_post and create_linkedin_image_post with destructiveHint: true to signal side effects.
• Add a confirm_before_execute pattern for write operations. Return a result with status 'pending_confirmation' and require the user/LLM to approve before posting. Example: 'Prepared post with text: "..." Return required: {confirmed: true} to post, or cancel.'