This server is non-functional as an MCP implementation. The mcp_server.py file contains only a print statement that outputs a JSON status message, it does not implement the MCP protocol at all. No tool registration, no request/response handling, no schema definitions. The execute_skill_action tool is mentioned in the status output but never actually defined or registered. The client.py and example_usage.py files are standalone Python classes unrelated to MCP. There is no evidence of tool definitions, parameter schemas, or descriptions in the codebase.

• Implement the MCP protocol: add a request/response loop that reads JSON-RPC messages from stdin and writes responses to stdout. Use the official MCP Python SDK or implement the protocol directly.
• Define the execute_skill_action tool with a complete JSON Schema: declare all input parameters (space_title, tracked_sources_count, etc.) with types (string, integer), descriptions, and constraints (e.g., min/max for counts).
• Expand the tool description to 50 - 200 characters explaining: (1) what it does (synthesizes research space knowledge), (2) when to use it (when user wants to create or query a collaborative research space), (3) what it returns (space metadata, entity counts, report URL).
• Document the output schema with all returned fields (space_id, title, tracked_sources_count, synthesized_consensus_briefs, semantic_graph_entities_count, collaborative_team_members_synced, continuous_web_alert_crawler_active, exportable_markdown_report_url) and their types.
• Add error handling: if the skill action fails, return a structured error with a recovery hint (e.g., 'Space creation failed: title must be 1 - 256 characters. Try again with a shorter title.').