Descriptions are present but generic (10-20 words), below the 50-200 char LLM-optimized baseline. No parameter descriptions exist (all tools have zero parameters). No output schemas documented. No error handling or recovery guidance. Tools are read-only and stateless, but lack the structured, LLM-friendly design needed for production use.

• Add explicit output schemas to each tool. Document the response structure: e.g., get_bio returns {type: 'text', text: string}. This enables LLMs to extract and chain data.
• Expand tool descriptions to 80-150 characters. Example: 'get_bio: Retrieve a detailed biographical paragraph about Allan Kimmer Jensen, including his expertise in cloud infrastructure, AI/ML, and MCP. Use this to introduce Allan in professional contexts.'
• Clarify the distinction between get_contact_info (email, Signal) and get_social_links (LinkedIn, GitHub, Instagram) in their descriptions so LLMs select the right tool.
• Add error handling: document what happens if the server is unavailable, and provide a fallback or retry hint.
• Consider adding a single 'get_all_info' tool that returns bio, contact, and social links in one call, reducing round-trips for common use cases.