codebase-memory-mcp
A Docker-based MCP server providing codebase memory and search tools. It enables storing/retrieving memories, regex search of code, and file summarization for AI assistants.
README
codebase-memory-mcp
A Docker stdio-based MCP Server providing codebase memory and search capabilities.
Features (6 Tools)
| Tool | Description |
|---|---|
store_memory |
Store or update memory entries (supports tags) |
retrieve_memory |
Search memories by keyword/tag |
list_memories |
List all stored memories |
delete_memory |
Delete a specific memory |
search_codebase |
Search code using ripgrep (supports regex) |
summarize_file |
Read file content for LLM summarization |
Quick Start
Build Docker Image
cd /path/to/codebase-memory-mcp
podman build -t codebase-memory-mcp .
VS Code Configuration
Create .vscode/mcp.json in your project root:
Windows / macOS / Linux (Universal)
{
"servers": {
"codebase-memory": {
"type": "stdio",
"command": "podman",
"args": [
"run", "-i", "--rm",
"-v", "${workspaceFolder}:/app/workspace:ro",
"codebase-memory-mcp"
]
}
}
}
Windows (Docker via WSL)
If Docker is installed in WSL instead of Docker Desktop:
{
"servers": {
"codebase-memory": {
"type": "stdio",
"command": "wsl",
"args": [
"-d", "Ubuntu-22.04",
"podman", "run", "-i", "--rm",
"-v", "/mnt/d/xxxx:/app/workspace:ro",
"codebase-memory-mcp"
]
}
}
}
Replace
Ubuntu-22.04with your WSL distribution name. Runwsl -l -vto list available distributions.
Claude Desktop Configuration
Edit %APPDATA%\Claude\claude_desktop_config.json (Windows) or ~/.config/Claude/claude_desktop_config.json (macOS/Linux):
{
"mcpServers": {
"codebase-memory": {
"command": "podman",
"args": [
"run", "-i", "--rm",
"-v", "C:\\Users\\YourName\\Projects\\my-project:/app/workspace:ro",
"codebase-memory-mcp"
]
}
}
}
Replace
C:\\Users\\YourName\\Projects\\my-projectwith your project path (Windows requires double backslashes).
Auto-Initialize Memory on Each Session
Since memory is not persisted (cleared when container exits), create an instruction file so AI automatically scans the project and builds memory at the start of each conversation.
Method 1: Create .github/copilot-instructions.md
## Project Memory Initialization
At the start of each conversation, execute these steps to build project memory:
1. Use `summarize_file` to read `package.json` for dependencies
2. Use `summarize_file` to read `README.md` for project purpose
3. Use `search_codebase` to explore main code structure
4. Use `store_memory` to save:
- key: `project-overview` - Project summary
- key: `tech-stack` - Tech stack (framework, language, main dependencies)
- key: `architecture` - Directory structure and architecture
## Project Info
- Project name: {your-project-name}
- Language: TypeScript
- Framework: Next.js 14
- Database: PostgreSQL
Method 2: Explicit Request at Conversation Start
At the beginning of each conversation, say:
Please initialize project memory:
1. Read package.json and README.md
2. Search main files in src/ directory
3. Store project architecture in store_memory(key: "architecture")
4. Store tech stack in store_memory(key: "tech-stack")
Method 3: Memory Init Script (Advanced)
Create MEMORY_INIT.md in project root:
# Memory Initialization Checklist
Execute these store_memory calls in order:
store_memory(key: "project", content: "This is a Next.js 14 project using TypeScript...")
store_memory(key: "conventions", content: "File naming: kebab-case, Components: PascalCase...")
store_memory(key: "architecture", content: "src/app/ for routes, src/components/ for components...")
Then tell the AI: "Please read MEMORY_INIT.md and execute the memory initialization inside"
Environment Variables
| Variable | Default | Description |
|---|---|---|
DATA_PATH |
/app/data |
SQLite database directory (in-container, not persisted) |
WORKSPACE_PATH |
/app/workspace |
Mounted codebase root directory |
MEMORY_DB_PATH |
$DATA_PATH/memory.db |
Full path to database file |
Directory Structure
Inside container:
/app
├── dist/ # Compiled JS
├── node_modules/
├── data/ # SQLite DB (in-container, reset each run)
│ └── memory.db
└── workspace/ # Project code (mounted via -v)
└── ...
Verify Installation
Test MCP Server Startup
printf '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2024-11-05","capabilities":{},"clientInfo":{"name":"test","version":"0"}}}\n{"jsonrpc":"2.0","id":2,"method":"tools/list","params":{}}\n' \
| docker run -i --rm codebase-memory-mcp
Should return a JSON response containing 6 tools.
Confirm Tools Count
printf '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2024-11-05","capabilities":{},"clientInfo":{"name":"test","version":"0"}}}\n{"jsonrpc":"2.0","id":2,"method":"tools/list","params":{}}\n' \
| docker run -i --rm codebase-memory-mcp 2>/dev/null | tail -1 | jq '.result.tools | length'
Output: 6
List All Tool Names
printf '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2024-11-05","capabilities":{},"clientInfo":{"name":"test","version":"0"}}}\n{"jsonrpc":"2.0","id":2,"method":"tools/list","params":{}}\n' \
| docker run -i --rm codebase-memory-mcp 2>/dev/null | tail -1 | jq '.result.tools[].name'
Output:
"store_memory"
"retrieve_memory"
"list_memories"
"delete_memory"
"search_codebase"
"summarize_file"
Verify AI Can Use Tools
Once configured in VS Code or Claude Desktop, test with these prompts:
Test 1: Store and retrieve memory
Please store a memory with key "test" and content "Hello MCP", then list all memories.
Expected: AI calls store_memory then list_memories, showing the stored entry.
Test 2: Search codebase
Search for "function" in the codebase.
Expected: AI calls search_codebase and returns matching lines.
Test 3: Summarize file
Summarize the package.json file.
Expected: AI calls summarize_file and provides a summary of dependencies.
Troubleshooting
- If tools don't appear: Check MCP panel in VS Code (View → MCP Servers) or restart the editor
- If container fails: Run
docker run -i --rm codebase-memory-mcpmanually to see errors - If path mount fails: Verify the workspace path exists and is accessible
FAQ
Q: When does memory disappear?
A: Memory is cleared each time the container exits (conversation ends or VS Code restarts). This is by design, allowing AI to re-understand the project each session.
Q: How to auto-initialize memory?
A: Use .github/copilot-instructions.md to set instructions, or explicitly request memory initialization at conversation start. See "Auto-Initialize Memory on Each Session" section above.
Q: What if I need persistent memory?
A: Add volume mount back:
"args": [
"run", "-i", "--rm",
"-v", "codebase-memory-data:/app/data",
"-v", "${workspaceFolder}:/app/workspace:ro",
"codebase-memory-mcp"
]
Then run podman volume create codebase-memory-data.
Q: Windows path conversion issues?
Docker Desktop automatically handles C:\ → /c/ conversion. For WSL Docker, store projects in WSL filesystem (e.g., /home/user/projects) to avoid path issues.
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