mem-search

name mem-search description Search claude-mem's persistent cross-session memory database. Use when user asks "did we already solve this?", "how did we do X last time?", or needs work from previous sessions. Memory Search Search past work across all sessions. Simple workflow: search -> filter -> fetch -> (rarely) disclose raw tool I/O. When to Use Use when users ask about PREVIOUS sessions (not current conversation): "Did we already fix this?" "How did we solve X last time?" "What happened last week?" Layered Workflow (ALWAYS Follow) NEVER fetch full details without filtering first. 10x token savings. Step 1: Search - Get Index with IDs Use the search MCP tool: search(query="authentication", limit=20, project="my-project") Returns: Table with IDs, timestamps, types, titles (~50-100 tokens/result) | ID | Time | T | Title | Read | |----|------|---|-------|------| | #11131 | 3:48 PM | 🟣 | Added JWT authentication | ~75 | | #10942 | 2:15 PM | 🔴 | Fixed auth token expiration | ~50 | Parameters: query (string) - Search term limit (number) - Max results, default 20, max 100 project (string) - Project name filter type (string, optional) - "observations", "sessions", or "prompts" obs_type (string, optional) - Comma-separated: bugfix, feature, decision, discovery, change dateStart (string, optional) - YYYY-MM-DD or epoch ms dateEnd (string, optional) - YYYY-MM-DD or epoch ms offset (number, optional) - Skip N results orderBy (string, optional) - "date_desc" (default), "date_asc", "relevance" Step 2: Timeline - Get Context Around Interesting Results Use the timeline MCP tool: timeline(anchor=11131, depth_before=3, depth_after=3, project="my-project") Or find anchor automatically from query: timeline(query="authentication", depth_before=3, depth_after=3, project="my-project") Returns: depth_before + 1 + depth_after items in chronological order with observations, sessions, and prompts interleaved around the anchor. Parameters: anchor (number, optional) - Observation ID to center around query (string, optional) - Find anchor automatically if anchor not provided depth_before (number, optional) - Items before anchor, default 5, max 20 depth_after (number, optional) - Items after anchor, default 5, max 20 project (string) - Project name filter Step 3: Fetch - Get Full Details ONLY for Filtered IDs Review titles from Step 1 and context from Step 2. Pick relevant IDs. Discard the rest. Use the get_observations MCP tool: get_observations(ids=[11131, 10942]) ALWAYS use get_observations for 2+ observations - single request vs N requests. Parameters: ids (array of numbers, required) - Observation IDs to fetch orderBy (string, optional) - "date_desc" (default), "date_asc" limit (number, optional) - Max observations to return project (string, optional) - Project name filter Returns: Complete observation objects with title, subtitle, narrative, facts, concepts, files (~500-1000 tokens each) Step 4: Disclose Raw Tool I/O - Only When Step 3 Was Not Enough Observations are summaries . When the answer needs the literal bytes a tool returned — the exact diff, the exact command output, the exact API response — use the get_tool_uses MCP tool: get_tool_uses(ids=["toolu_01ABC..."], project="my-project") Do not start here. Raw tool bodies are unsummarized and can run to thousands of tokens each; that is the whole reason claude-mem compresses them into observations in the first place. Reach for this layer only after search / timeline / get_observations pointed you at specific tool calls. Parameters: ids (array, required) - Numeric tool_uses ids OR opaque tool_use_id strings limit (number, optional) - Max rows to return project (string, optional) - Project name filter contentSessionId (string, optional) - Restrict to one session Returns: The stored tool_input / tool_response for those calls, plus the tool name, session ids, and the observation each was folded into. Payloads over 64 KB were truncated on write and carry a …[truncated: N bytes] marker. Examples Find recent bug fixes: search(query="bug", type="observations", obs_type="bugfix", limit=20, project="my-project") Find what happened last week: search(type="observations", dateStart="2025-11-11", limit=20, project="my-project") Understand context around a discovery: timeline(anchor=11131, depth_before=5, depth_after=5, project="my-project") Batch fetch details: get_observations(ids=[11131, 10942, 10855], orderBy="date_desc") Recover the exact output of a command we ran last week: search(query="migration failed", limit=20, project="my-project") get_observations(ids=[11131]) # read the summary first get_tool_uses(ids=["toolu_01ABC..."]) # only if the summary omitted the detail Why This Workflow? Search index: ~50-100 tokens per result Full observation: ~500-1000 tokens each Raw tool body: up to 64 KB each — the layer you skip 95% of the time Batch fetch: 1 HTTP request vs N individual requests 10x token savings by filtering before fetching Knowledge Agents Want synthesized answers instead of raw records? Use /knowledge-agent to build a queryable corpus from your observation history. The knowledge agent reads all matching observations and answers questions conversationally.