This server is barely functional as an MCP implementation. The source code shows a minimal stub that does not actually implement the MCP protocol. The mcp_server.py file contains only a single print statement that outputs a JSON initialization message, it never enters an event loop, never reads from stdin, and never handles tool invocations. The tool 'execute_skill_action' is mentioned in the supported_tools list but is never actually registered with a schema, description, or input/output definitions. The accompanying client.py and example_usage.py files are Python client utilities, not MCP protocol handlers. There is no evidence of proper MCP message handling, tool schema definitions, parameter validation, or error recovery. The server would fail immediately when an MCP client attempted to invoke any tool.

• Implement a proper STDIO event loop in mcp_server.py using the official Python MCP SDK (or equivalent). The server must read JSON-RPC messages from stdin, parse them, dispatch to handlers (initialize, call_tool), and write responses to stdout.
• Define a full input schema for execute_skill_action with typed parameters. Add 'problem_domain' as an enum (e.g., COMPETITIVE_PROGRAMMING, ALGORITHMIC_GRAPH_THEORY, etc.); 'search_breadth' as an integer with min=1, max=256; 'verifier_check_depth' as an integer with min=1, max=32.
• Expand the tool description to 80 - 150 characters explaining: (1) What it does, 'Performs test-time search tree verification across candidate solution rollouts'; (2) When to use it, 'Call this to evaluate solution quality and identify prunable branches'; (3) What it returns, 'A verification session with branch evaluation metrics, pass rates, and a trace URL'.
• Document all output fields in a structured schema. Include field names (verification_session_id, candidate_branches_evaluated, pruned_deadlock_branches, test_time_compute_multiplier_gain_pct, optimal_path_pass_rate_pct, execution_tree_trace_url), types (string, integer, number), and brief descriptions (e.g., 'verification_session_id: string, unique identifier for this verification run').
• Add parameter descriptions. E.g., 'problem_domain: The category of problem being verified (e.g., COMPETITIVE_PROGRAMMING for coding contests, ALGORITHMIC_GRAPH_THEORY for graph algorithms). Determines tree heuristics.'; 'search_breadth: Maximum number of candidate branches to explore (1 - 256). Higher values increase coverage but multiply compute cost.'; 'verifier_check_depth: Depth of rollout verification within the tree (1 - 32). Deeper checks catch more edge cases but reduce overall breadth.'.