Scoring was not performed

• Rename tools to follow snake_case verb_noun convention: execute_tool → execute_<specific_tool_category> (e.g., execute_file_operation, execute_api_call); get_tools → list_available_tools or discover_tools.
• Expand tool descriptions to 50 - 200 characters with explicit guidance: 'Executes a tool from the C4 GenAI Suite. Call this after list_available_tools to run a specific tool. Provide the tool name and required arguments. Returns success status and result data.' Include WHEN to use it (after discovery), WHAT it requires, and WHAT it returns.
• Document the executeRequestDto input schema with explicit field definitions: name (string, required, enum or pattern for valid tool names), arguments (object, required, schema depends on selected tool, provide a discovery pattern or link to tool documentation).
• Document output schemas for both tools. executeTool should return {success: boolean, result: object, error?: string}. list_available_tools should return {tools: [{name: string, description: string, parameters: object}]}. Make schemas JSON Schema compliant and visible.
• Add error handling guidance: 'If executeTool fails, check that the tool name matches list_available_tools output. If a required argument is missing, the error will specify which field is expected. Retry only if the error is marked retryable (e.g., temporary service unavailability).'
• Consider splitting executeTool into domain-specific tools (e.g., execute_data_query, execute_file_operation) based on the actual tools exposed by the C4 suite. This allows the LLM to reason about tool intent without a generic proxy.