Memory Search

Search past work across all sessions. Simple workflow: search -> filter -> fetch.

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?"

3-Layer 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:

code

search(query="authentication", limit=20, project="my-project")

Returns: Table with IDs, timestamps, types, titles (~50-100 tokens/result)

code

| 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:

code

timeline(anchor=11131, depth_before=3, depth_after=3, project="my-project")

Or find anchor automatically from query:

code

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:

code

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)

Saving Memories

Use the save_memory MCP tool to store manual observations:

code

save_memory(text="Important discovery about the auth system", title="Auth Architecture", project="my-project")

Parameters:

•text (string, required) - Content to remember
•title (string, optional) - Short title, auto-generated if omitted
•project (string, optional) - Project name, defaults to "claude-mem"

Examples

Find recent bug fixes:

code

search(query="bug", type="observations", obs_type="bugfix", limit=20, project="my-project")

Find what happened last week:

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search(type="observations", dateStart="2025-11-11", limit=20, project="my-project")

Understand context around a discovery:

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timeline(anchor=11131, depth_before=5, depth_after=5, project="my-project")

Batch fetch details:

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get_observations(ids=[11131, 10942, 10855], orderBy="date_desc")

Why This Workflow?

•Search index: ~50-100 tokens per result
•Full observation: ~500-1000 tokens each
•Batch fetch: 1 HTTP request vs N individual requests
•10x token savings by filtering before fetching