The server has one well-defined tool with proper schema, description, and parameter annotations. The tool uses modern FastMCP patterns with context support for progress and logging. However, the server is feature-limited (only 1 tool), lacks error handling guidance, does not implement destructive/idempotent/readOnly annotations in the tool definition, and the simple arithmetic operation does not reflect real-world tool complexity. The code is clean and follows conventions, but scoring reflects conservative calibration, a single basic tool with good fundamentals lands in the B range.

• Add tool annotations to the @mcp.tool() decorator: @mcp.tool(readOnlyHint=True) to declare that 'add' is a read-only operation with no side effects. This enables agents to reason about idempotency and caching.
• Implement input validation: check that both 'a' and 'b' are within reasonable bounds (e.g., -1e9 to 1e9) and raise a clear ValueError with the constraint if violated. Example: 'Invalid parameter a: must be between -1000000000 and 1000000000, got 9999999999999.'
• Document the return schema: wrap the integer result in a structured response like {"result": int, "operation": "addition", "inputs": {"a": int, "b": int}}. This enables downstream tool chaining and clearer LLM reasoning.
• Add a dry-run mode: even though 'add' is arithmetic, consider a 'preview_calculation' parameter (default false) that returns the expected result without side effects. This pattern is useful for reusable templates.
• Enhance error messages: if the operation fails (e.g., integer overflow), return a structured error with guidance: {"error": "Calculation overflow", "suggestion": "Try smaller numbers or use a different operation"}.
• Remove unnecessary context calls if not always available: ctx.report_progress() and ctx.debug() are useful for long-running tools but are noise for a simple arithmetic operation. Either document that progress is always reported, or remove the calls to keep the tool contract simple.