Critical failures across all dimensions. The single tool 'execute_skill_action' has a minimal description (58 chars, below the 10-1024 baseline), lacks output schema documentation, and the input schema is present but parameters lack descriptions. The mcp_server.py does not implement actual MCP protocol, it merely prints a JSON status message and exits. No tool registration, no request/response handling, no error handling. The implementation is a stub that cannot function as an MCP server.

• Implement the full MCP protocol: read JSON-RPC requests from stdin, parse tool calls, invoke the skill logic, and write JSON-RPC responses to stdout. Use the official MCP Python SDK or a reference implementation.
• Expand tool description to 100-200 characters explaining: (1) what bottleneck detection does, (2) when to call it (e.g., 'when analyzing slow LLM requests'), (3) what it returns (bottleneck span ID and optimization advice). Example: 'Analyzes distributed trace spans to identify the slowest component and suggest optimizations. Call this when an LLM request exceeds SLA. Returns the bottleneck span ID, latency contribution percentage, and actionable optimization advice.'
• Add descriptions to all input parameters: 'distributed_trace_spans' should describe the expected array structure (e.g., 'Array of span objects, each with {span_id, span_name, duration_ms, parent_span_id}'); 'sla_threshold_ms' should clarify the default and range (e.g., 'SLA threshold in milliseconds. Default: 800. Valid range: 100 - 60000.').
• Document the output schema in the tool definition. Declare that the response is a JSON object with fields: bottleneck_span_id (string), latency_contribution_pct (number 0 - 100), optimization_advice (string). Include example output.
• Add error handling: validate that distributed_trace_spans is a non-empty array, that each span has required fields (span_id, duration_ms), and that sla_threshold_ms is within a valid range. Return structured errors like '{"error": "Invalid span: missing duration_ms", "span_id": "auth"}' so the LLM can self-correct.