QSerial exposes 6 tools with basic schemas and descriptions in Chinese. All tools have input schemas with typed parameters and descriptions, but quality is inconsistent. Tool names use dot notation (assistant.search, conn.analyze.custom) rather than verb_noun convention, making intent less obvious to LLMs. Descriptions are present but brief (avg ~60 chars), lacking context on WHEN to use each tool or dependencies between them. No output schemas documented. Error handling and recovery guidance absent. Security considerations (credentials, permissions) not visible in tool definitions.

• Rename tools to follow verb_noun convention: 'search_knowledge', 'ask_assistant', 'analyze_log', 'generate_command', 'list_knowledge_modules', 'analyze_connections'. This makes intent obvious from the name alone.
• Expand descriptions to 100-200 chars. Include WHAT the tool does, WHEN to use it (e.g., 'Use after search_knowledge to get detailed answers'), and any prerequisites (e.g., 'Requires AI service configured'). Example: 'Search the local knowledge base for serial/embedded topics. Returns up to topK results with relevance scores. Use before ask_assistant for context.'
• Document output schemas for all tools. Specify field names, types, and what each field contains. Example for search: {results: [{id, title, text, score}], total_count, next_cursor}. This enables LLMs to chain tools and extract required data.
• Add error handling guidance. Document common failures (e.g., 'AI service not configured', 'knowledge base empty', 'invalid query') and recovery steps. Example: 'If AI service error occurs, check configuration and retry. If knowledge base is empty, call list_knowledge_modules first.'
• Expose tool annotations in the MCP tool definition. Add readOnlyHint=true to search/ask/analyze tools and destructiveHint=true to generateCommand. This lets LLMs reason about side effects and plan safely.