Single tool 'count-r' has a basic description (47 chars) and minimal schema. Input parameter 'word' is typed as string with a short description (40 chars), meeting the bare minimum. No output schema is documented, LLMs cannot predict the return structure. No error handling guidance, no parameter constraints (e.g., min/max length), and no indication of what the tool returns (integer count as text? JSON?). Tool name lacks action verb prefix (should be 'count_r_letters' or similar). The implementation is trivial but the definition lacks production-grade rigor.

• Rename tool to 'count_letter_r' or 'count_r_occurrences' to start with a clear action verb and match verb_noun convention.
• Document the output schema explicitly: 'Returns an integer (0 or greater) representing the count of lowercase and uppercase R letters in the input word.'
• Add parameter constraints to 'word': 'A non-empty string (1-1000 characters). Counts both lowercase and uppercase R. Non-ASCII characters are ignored.'
• Expand tool description to 100-150 chars: 'Counts the number of R letters (case-insensitive) in a given word. Returns an integer count. Useful for word analysis or linguistic tasks.'
• Return structured error responses: instead of silently returning 0, return {content: [{type: 'text', text: 'Error: input must be a non-empty string'}], isError: true} so LLMs understand what went wrong.