> ## Documentation Index
> Fetch the complete documentation index at: https://docs.memorybase.app/llms.txt
> Use this file to discover all available pages before exploring further.

# search_memory

> Semantic search over your entire MemoryBase -- the primary way your coding tool finds relevant context.

The most important MCP tool. Your coding assistant uses this to find conversations and project context that are relevant to what you're working on. It uses semantic search (meaning-based, not just keywords) -- the same search that powers the browser extension.

## Parameters

| Parameter          | Type      | Required | Description                                             |
| ------------------ | --------- | -------- | ------------------------------------------------------- |
| `q`                | string    | Yes      | The search query (max 2,000 characters)                 |
| `topLayers`        | number    | No       | Max layers to return (1-10)                             |
| `projectIds`       | string\[] | No       | Only search within these project IDs (max 100)          |
| `pinnedProjectIds` | string\[] | No       | Always include these projects in scoring (max 50)       |
| `render`           | boolean   | No       | If `true`, returns a ready-to-use markdown context pack |

## What It Returns

Ranked results containing:

* Conversation summaries and context snippets
* Project layers that match the query
* Relevant entities (people, tools, concepts) mentioned across your history

When `render: true`, returns a single markdown document your assistant can use directly for context.

## When Your Assistant Uses This

* You ask about something you've discussed before
* You reference a past decision or conversation
* Your assistant needs background context for a task
* You say things like "remember when we discussed..." or "use the approach from last week"