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.
Search past work across all sessions. Simple workflow: search -> filter -> fetch.
Use when users ask about PREVIOUS sessions (not current conversation):
NEVER fetch full details without filtering first. 10x token savings.
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 termlimit (number) - Max results, default 20, max 100project (string) - Project name filtertype (string, optional) - "observations", "sessions", or "prompts"obs_type (string, optional) - Comma-separated: bugfix, feature, decision, discovery, changedateStart (string, optional) - YYYY-MM-DD or epoch msdateEnd (string, optional) - YYYY-MM-DD or epoch msoffset (number, optional) - Skip N resultsorderBy (string, optional) - "date_desc" (default), "date_asc", "relevance"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 aroundquery (string, optional) - Find anchor automatically if anchor not provideddepth_before (number, optional) - Items before anchor, default 5, max 20depth_after (number, optional) - Items after anchor, default 5, max 20project (string) - Project name filterReview 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 fetchorderBy (string, optional) - "date_desc" (default), "date_asc"limit (number, optional) - Max observations to returnproject (string, optional) - Project name filterReturns: Complete observation objects with title, subtitle, narrative, facts, concepts, files (~500-1000 tokens each)
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")
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.