This MCP server has critical quality gaps across multiple dimensions. The single tool 'calculate_nps' has a reasonable name and adequate description, but the implementation lacks any MCP protocol structure. The code shows a plain Python class with no evidence of MCP server registration, tool schema formalization, or protocol implementation. The input parameter is documented in an informal way (only visible in the example, not in a formal schema definition visible to MCP clients). No output schema documentation exists. There is no error handling guidance, no input validation framework, and no evidence that this is even a functioning MCP server rather than a standalone Python utility. The rubric requires that tool definitions be explicitly visible in the source with proper MCP registration, here, only a plain Python class is present.

• Implement a full MCP server using the official MCP Python SDK (mcp library). Add Tool registration with name, description, and inputSchema in JSON Schema format.
• Define and publish the complete output schema for calculate_nps: {type: 'object', properties: {nps_score: {type: 'integer', description: '...'}, percentage_promoters: {type: 'number'}, ...}}
• Add input validation: reject arrays with <1 score or >10 scores; reject non-integer or out-of-range values. Return structured error messages like 'Invalid score at index 2: 15. Scores must be integers 0-10.'
• Expand parameter description: 'List of NPS scores (0-10 scale). Must contain at least one score. Scores outside 0-10 are rejected.' Use minimum/maximum in the schema.
• Add idempotentHint=true to the tool schema to signal that repeated calls with the same input return the same output.
• Consider adding supporting tools: validate_scores (pre-flight check), get_nps_categories (describe promoter/detractor/passive definitions), analyze_nps_trends (if multi-period data is needed).
• Document example valid and invalid inputs in the tool description to guide LLM selection and prevent hallucinated parameters.