Single tool with minimal definition quality. Tool name 'execute_skill_action' is generic and does not follow verb_noun pattern clearly (execute is vague). Description is 127 chars but lacks WHAT/WHEN/WHY clarity, does not explain when to use this vs other tools, what it actually does operationally, or what the output structure is. Input schema present but trivial: single string parameter with minimal description ('Input execution payload'). No output schema documented. No error handling guidance. Tool appears to be a pass-through echo that returns the input query wrapped in metadata, not a real skill executor. No parameter validation, no constraints, no recovery guidance.

• Rename tool to reflect actual operation: 'compress_dialogue_memory', 'consolidate_semantic_facts', or 'extract_salient_dialogue_turns'. Use verb_noun pattern.
• Expand description to 100-200 chars explaining: (1) This tool filters ephemeral chatter and extracts high-salience facts (preferences, commitments, credentials) into persistent memory blocks. (2) Call this after multi-turn conversations to distill long-term memory. (3) Returns consolidated facts with salience scores and compression ratio.
• Document input parameter: 'query_payload: JSON array of dialogue turns, each with {id, role, content}. Role must be "user" or "assistant". Content is the message text. Example: [{"id": 1, "role": "user", "content": "My budget is $5000"}]'.
• Document output schema: {total_turns: int, retained_memories_count: int, discarded_chatter_count: int, token_compression_pct: float, consolidated_semantic_memory: [{turn_id, role, extracted_fact, salience_score, trigger_keywords}]}.
• Implement actual memory consolidation logic in tool handler. Currently tool only echoes input. Integrate the HierarchicalMemoryConsolidationCompressor.consolidate_dialogue_turns() logic from client.py into the MCP tool handler.