Server has 12 tools with complete JSON schemas and descriptions, but quality is inconsistent. Naming follows verb_noun convention (list_, get_, execute_, etc.), which is good. However, descriptions are often generic and lack actionable context for LLM selection. Many parameters lack type constraints (enums, ranges). Output schemas are not documented. Error handling guidance is absent. No tool annotations (readOnlyHint/destructiveHint). Transaction-based memory model is novel but underdocumented for agent reasoning.

• Expand tool descriptions to 100-200 characters. Include WHAT the tool does, WHEN to use it (vs similar tools), and what it returns. Example: 'Execute a skill with provided inputs. Use this to run a stored skill artifact. Returns execution result with status, output, and any errors. Requires skill name and valid input parameters.'
• Document output schemas for all tools. Add a 'returns' field to each tool definition specifying the response structure (e.g., list_skills returns {skills: [{name, version, tags, ref}], total, cursor}).
• Add tool annotations: mark execute_skill, stage_memory, begin_transaction, commit_transaction, rollback_transaction with destructiveHint=true. Mark list_* and get_* with readOnlyHint=true. Mark idempotent operations with idempotentHint=true.
• Add parameter constraints: limit should be integer with minimum=1, maximum=100. status should be enum ['pending', 'committed', 'rolled_back']. confidence_threshold should be number with minimum=0, maximum=1.
• Add error handling guidance to execute_skill, get_skill, and stage_memory. Document common failures (skill not found, invalid inputs, transaction not found) and suggest recovery actions (e.g., 'If skill not found, call list_skills to discover available skills').