claude-memory-search-mcp
Enables keyword search, filtering, and retrieval of Claude Code memory files, reducing context overhead by fetching only relevant memories on demand.
README
claude-memory-search-mcp
MCP server that searches and links across Claude Code memory files.
Why
Claude Code's auto-memory directory grows large quickly (100+ files in my setup). Loading every memory into context defeats the point of having them — and the auto-loaded MEMORY.md only carries a 150-char hook per entry. This server lets Claude:
- search by keyword across every memory body, not just titles,
- filter by type (
user/feedback/project/reference), - fetch a single memory in full only when needed,
- traverse
[[wikilinks]]to pull related context on demand.
In short: stop trying to hold the whole memory in the window. Index it, then pull what you need.
Tools
| Tool | Args | Description |
|---|---|---|
search_memory |
query, limit? |
Ranked keyword search across all memory bodies. Bonuses for title and description hits. |
get_memory |
name |
Fetch the full body of one memory by file name (no .md). |
list_memories |
type? |
List every memory, optionally filtered by type. |
find_related |
name |
Return memories linked from or to a given memory via [[wikilinks]]. |
Install
From source (recommended for Claude Code MCP config)
git clone https://github.com/yangchoi/claude-memory-search-mcp.git
cd claude-memory-search-mcp
npm install
npm run build
Run directly via npx (no local clone)
npx -y github:yangchoi/claude-memory-search-mcp
Slower on first invocation (npx fetches and builds), then cached.
Configure in Claude Code
Add to your Claude Code MCP config (~/.claude/settings.json or project .mcp.json):
{
"mcpServers": {
"memory-search": {
"command": "node",
"args": ["/absolute/path/to/claude-memory-search-mcp/dist/index.js"],
"env": {
"CLAUDE_MEMORY_DIR": "/Users/you/.claude/projects/<your-project-id>/memory"
}
}
}
}
CLAUDE_MEMORY_DIR is optional — by default the server derives the path from $HOME (e.g. /Users/jane → ~/.claude/projects/-Users-jane/memory on macOS, /home/jane → ~/.claude/projects/-home-jane/memory on Linux). Set it explicitly if your memory lives elsewhere.
Restart Claude Code. The four tools above will appear under mcp__memory-search__*.
Example
> search_memory query="database migration"
[42] backend-project-notes (project)
matches: database, migration
Postgres migration patterns and rollback strategy
[18] team-conventions (reference)
matches: migration
...
License
MIT
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.