Get full conversation thread by conv_id. Searches personal memory first, then library.

Return conversations ordered by recency (not semantic similarity).

Use this when the user asks for "recent conversations", "ここ2日の会話", etc. Each row header: [conv_id | platform | started→ended | title | topic | cluster | msgs | intensity] followed by a short snippet of the first message.

Args: days: look-back window in days (default 2) limit: max conversations to return (default 20) platform: filter by platform (e.g. "claude_code", "chatgpt") project: substring match on conversation title (e.g. "my-webapp", "JSAS2026")

Return recent conversations scoped to a project or workspace.

Matches on conversation title (case-insensitive substring). Covers cases like "my-webapp", "JSAS2026", "personal/memory/mcp", etc.

Args: project: substring to match against conversation title days: look-back window in days (default 14) limit: max conversations to return (default 30)

Get conversation metadata and a compact bounded excerpt without the full transcript.

Returns title, platform, dates, msg_count, topic/cluster, and up to max_messages messages sampled from the start and end of the conversation.

Args: conv_id: conversation ID (from search_personal_memory or list_recent_conversations) max_messages: max messages to include; split between first half and last half (default 12)

Search cross-project agent ghost memories (= this agent's rule/feedback accumulation).

Empty query → list top-ranked memories (= "what's in my ghost vault" overview). Non-empty query → hybrid FTS + vector semantic ranking via agent.search_ghost_ranked (SECURITY DEFINER, migration 020).

⚠️ current_project is caller-attested, NOT server-verified. shared-restricted requires current_project in per-memory allowlist (= pentest/commercial boundary).

Ranking: rank_score = base_score * recency_factor + semantic_sim * 0.5 base_score = 0.2activation + 0.5incident_prevention + 0.3endorsement - 0.4correction - 0.2pred_error + 0.1scope_bonus recency_factor = exp(-days_since_last_activated / 30) semantic_sim = 1 - cosine_distance(query_vec, memory.dense) (0 if empty query)

Each returned row triggers agent.bump_activation, so memories that surface in search naturally rise in rank over time (= self-tuning loop).