This server exposes 2 tools with minimal documentation. Both tools lack parameter descriptions entirely, descriptions are extremely brief (under 30 chars), and there is no documented output schema. Tool names are non-standard (Echo, ReverseEcho instead of verb_noun patterns like greet_user, reverse_message). The implementation uses C# attributes for tool registration, which makes definitions visible in code, but parameter constraints, error handling, and composition patterns are absent. Per the rubric baseline, 100% of A+ tools have parameter descriptions, this server has 0%. Average tool description length baseline is 194 chars; these tools are 20-40 chars each.

• Rename tools to follow verb_noun convention: 'Echo' → 'greet_user', 'ReverseEcho' → 'reverse_message'. This makes tool purpose obvious to LLMs.
• Expand tool descriptions to 100 - 200 chars, answering: What does it do? When should the LLM call it? What does it return? Example: 'Greet a user by name and return a personalized greeting message. Use this to welcome users to a chat. Returns: a string greeting.'
• Add descriptions to all parameters in the schema. For 'username', add: 'The user's name to greet. Should be 1-100 characters, alphanumeric.' For 'message', add: 'The text message to reverse and echo back. Should be 1-500 characters.'
• Document output schemas. Add to tool metadata: 'Returns: a string (plain text response, no special formatting)'.
• Add input constraints: Define max length for username (e.g., 100 chars) and message (e.g., 500 chars) in parameter descriptions. This prevents unbounded inputs.
• Add error handling hints. E.g., 'If username is empty, return an error: "Username cannot be empty."' This guides LLMs on expected error cases.
• Consider tool composition: are these tools meant to be used together, or independently? If independently, ensure the descriptions clarify which to call first or under what conditions.