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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 → get IDs → fetch details by ID.

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?"

The Workflow

ALWAYS follow this exact flow:

1. Search - Get an index of results with IDs
2. Timeline - Get context around top results to understand what was happening
3. Review - Look at titles/dates/context, pick relevant IDs
4. Fetch - Get full details ONLY for those IDs

Step 1: Search Everything

Use the search MCP tool:

Required parameters:

• query - Search term
• limit: 20 - You can request large indexes as necessary
• project - Project name (required)

Example:

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

Returns:

| ID | Time | T | Title | Read | Work |
|----|------|---|-------|------|------|
| #11131 | 3:48 PM | 🟣 | Added JWT authentication | ~75 | 🛠️ 450 |
| #10942 | 2:15 PM | 🔴 | Fixed auth token expiration | ~50 | 🛠️ 200 |

Step 2: Get Timeline Context

You MUST understand "what was happening" around a result.

Use the timeline MCP tool:

Example with observation ID:

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

Example with query (finds anchor automatically):

timeline(query="authentication", depth_before=3, depth_after=3, project="my-project")

Returns exactly depth_before + 1 + depth_after items - observations, sessions, and prompts interleaved chronologically around the anchor.

When to use:

• User asks "what was happening when..."
• Need to understand sequence of events
• Want broader context around a specific observation

Step 3: Pick IDs

Review the index results (and timeline if used). Identify which IDs are actually relevant. Discard the rest.

Step 4: Fetch by ID

For each relevant ID, fetch full details using MCP tools:

Fetch multiple observations (ALWAYS use for 2+ IDs):

get_batch_observations(ids=[11131, 10942, 10855])

With ordering and limit:

get_batch_observations(
  ids=[11131, 10942, 10855],
  orderBy="date_desc",
  limit=10,
  project="my-project"
)

Fetch single observation (only when fetching exactly 1):

get_observation(id=11131)

Fetch session:

get_session(id=2005)  # Just the number from S2005

Fetch prompt:

get_prompt(id=5421)

ID formats:

• Observations: Just the number (11131)
• Sessions: Just the number (2005) from "S2005"
• Prompts: Just the number (5421)

Batch optimization:

• ALWAYS use get_batch_observations for 2+ observations
• 10-100x more efficient than individual fetches
• Single HTTP request vs N requests
• Returns all results in one response
• Supports ordering and filtering

Search Parameters

Basic:

• query - What to search for (required)
• limit - How many results (default 20)
• project - Filter by project name (required)

Filters (optional):

• type - Filter to "observations", "sessions", or "prompts"
• dateStart - Start date (YYYY-MM-DD or epoch timestamp)
• dateEnd - End date (YYYY-MM-DD or epoch timestamp)
• obs_type - Filter observations by type (comma-separated): bugfix, feature, decision, discovery, change

Examples

Find recent bug fixes:

Use the search MCP tool with filters:

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

Find what happened last week:

Use date filters:

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

Search everything:

Simple query search:

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

Get detailed instructions:

Use the progressive_description tool to load full instructions on-demand:

progressive_description(topic="workflow")  # Get 4-step workflow
progressive_description(topic="search_params")  # Get parameters reference
progressive_description(topic="examples")  # Get usage examples
progressive_description(topic="all")  # Get complete guide

Why This Workflow?

Token efficiency:

• Search results: ~50-100 tokens per result (table index)
• Full observation: ~500-1000 tokens each
• 10x savings - only fetch full when you know it's relevant

Batch fetching:

• Individual fetches: 10 HTTP requests, ~5-10s latency
• Batch fetch: 1 HTTP request, ~0.5-1s latency
• 10-100x faster for multi-observation queries

Clarity:

• See everything first (table index)
• Get timeline context around interesting results
• Pick what matters based on context
• Fetch details only for what you need (batch when possible)

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Remember:

• ALWAYS get timeline context to understand what was happening
• ALWAYS use get_batch_observations when fetching 2+ observations
• The workflow is optimized: search → timeline → batch fetch = 10-100x faster