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.