Two tools with clear, thematic naming (log_causality_point, time_leap_retrieval) and reasonable descriptions (120 - 180 chars). However, input schemas lack formal type constraints (no enums, ranges, or format specs). Parameter descriptions are present but minimal (10 - 30 chars). Output is unstructured text rather than typed JSON objects, forcing LLMs to parse narrative responses. No error handling guidance, no pagination for retrieval results, no idempotency markers. Schemas are visible and valid JSON Schema, but sparse. Naming follows verb_noun convention well; descriptions explain WHAT but not WHEN or dependencies.

• Restructure outputs as JSON objects: log_causality_point should return {"id": int, "timestamp": ISO8601, "status": "anchored"} instead of narrative text. time_leap_retrieval should return {"anchors": [{"id", "timestamp", "decision", "alternatives", "justification"}], "total": int, "query_matched": int}.
• Add length constraints to parameters: decision_made (1 - 2000 chars), alternatives_rejected (0 - 2000 chars), justification (0 - 2000 chars), query (0 - 500 chars). Document in descriptions: 'decision_made: The final decision (1 - 2000 chars, required).'
• Add pagination to time_leap_retrieval: accept optional 'limit' (1 - 100, default 10) and 'offset' (default 0) parameters. Return total count so LLM knows if more results exist.
• Enhance error messages with recovery hints: 'decision_made cannot be empty. Provide a clear, concise description of the decision (e.g., "Migrate to PostgreSQL").' instead of generic ValueError.
• Add tool annotations (destructiveHint for log_causality_point, readOnlyHint for time_leap_retrieval) to signal to clients which tools modify state and which are safe to call repeatedly.