Critical failures across all dimensions. The single tool 'execute_skill_action' has a reasonable name and description, but the implementation is fundamentally broken: no actual MCP protocol implementation visible, no input schema validation, no output schema documentation, and no error handling. The mcp_server.py file merely prints a JSON status message and exits, it does not implement the MCP protocol's request/response loop. Tool definitions appear inferred from client.py rather than explicitly registered via MCP. No parameter descriptions provided for 'complex_hypothesis' or 'search_depth'. Output structure is undocumented. This is a stub, not a functional MCP server.

• Implement the full MCP protocol: create a request/response loop that listens for JSON-RPC messages on stdin, parses tool calls, invokes the skill, and returns structured responses on stdout.
• Add complete parameter descriptions: 'complex_hypothesis' should explain the expected research topic format and length; 'search_depth' should specify the valid range (e.g., 1-10) and what each level means.
• Document the output schema with field types and descriptions. Declare that the response is a JSON object with fields: search_tree_id (string), hypothesis (string), sub_queries_decomposed (integer), peer_reviewed_citations_verified (integer), cross_evidence_consensus_score_pct (number 0-100), synthesis_markdown_report_url (string, URI), epistemic_confidence_index (number 0-1).
• Add input validation and error handling: validate search_depth is within range, catch exceptions from the deliberative search, and return structured error responses with actionable recovery hints (e.g., 'search_depth must be 1-10; got 15').
• Explicitly register the tool via MCP's tools/list response with full schema, not as a side effect of client.py. Use JSON Schema to declare input types, required fields, and constraints.