Single tool 'execute_skill_action' has a verb-noun name (positive), but the implementation is severely underdeveloped. The tool definition exists only in mcp_server.py as a JSON string listing, not as a proper MCP tool registration. Input schema is present with two parameters (search_query, max_target_results) and basic type info, but parameter descriptions are minimal (under 20 chars each). Tool description is 67 chars, borderline acceptable but lacks WHEN/WHY guidance. No output schema is documented anywhere. The client.py and example_usage.py files show mock behavior, not real implementation. No error handling, no validation, no recovery guidance. The server itself is a stub that prints a JSON status and exits, it does not implement the MCP protocol's request/response loop.

• Implement a proper MCP server that listens for tool calls, parses requests, invokes the tool, and returns structured responses. Use the MCP SDK or a reference implementation.
• Document the complete output schema for execute_skill_action. Specify all fields returned (crawl_task_id, extracted_results_count, fact_density_token_ratio_pct, clean_markdown_snippets, agent_llm_ready_json), their types, and what they mean.
• Expand parameter descriptions to 50 - 150 chars each. For max_target_results, specify the valid range (e.g., '1 - 100, default 5'). For search_query, explain format and any restrictions.
• Add error handling and recovery guidance. Document what happens if the crawl times out, returns no results, or encounters a network error. Provide actionable next steps for the LLM.
• Replace the mock client.py with actual web crawling and fact extraction logic, or clearly document that this is a reference implementation and the real backend is external.