Supports storage of memory documents on Linux systems, with files stored in ~/.local/share/mcp-structured-memory/.
Supports storage of memory documents on macOS systems, with files stored in ~/Library/Application Support/mcp-structured-memory/.
Uses markdown files as the storage format for memory documents, allowing structured organization of information that can be easily read and updated.
Structured Memory MCP Server
A Model Context Protocol (MCP) server that provides structured, domain-specific memory management through markdown files. This is particularly useful for ongoing projects around a particular area of focus where you want to accumulate valuable context over time. Examples include focused domains like travel planning, research projects, real estate search, investment theses, product planning, and career development.
Why Structured Memory?
Traditional MCP memory servers use semantic search across scattered conversation snippets. This works well for general recall but fails for focused projects that need organized, categorical information.
Structured Memory instead maintains living documents with structured content that you can scan, update, and track over time - just like you would with a personal notebook, but with AI assistance.
Memory documents are stored as markdown files, primarily updated automatically by the LLM as it learns from your conversations to build rich context over time.
Typical usage
- Ask your LLM client to create a memory document for your focused project
Create a new travel advisor memory document and tell me how to use it. This should start empty and grow over time.
- Create a new Project for conversations in that area. Add the provided usage instructions to your project context Note: LLMs will, against all tool advice, occasionally fail to show you the installation instructions. If that happens, follow up with a request to the LLM to show you the installation instructions and it should comply.
- New chat conversations start by checking the available info from the project's memory. Your LLM will update this memory over time to build up valuable context.
- Optionally prompt your LLM to add/adjust the memory file on demand. It's also helpful to ask the LLM to prompt you for relevant info (e.g. "Ask me some questions to learn about my travel preferences")
Installation
Option 1: Install from npm (when published)
Option 2: Install from source
Configuration
For Claude Desktop
Edit the claude_desktop_config.json
file with the following entry:
Available Tools
- create_memory - Create a new memory document with optional initial content
- list_memories - List all available memory documents
- get_memory_summary - Get a high-level summary of a memory document
- get_section - Retrieve a specific section from a memory document
- get_full_memory - Retrieve the complete content of a memory document
- search_within_memory - Search for information within a memory document
- update_section - Update an entire section of a memory document
- add_to_list - Add an item to a list section
- update_list_item - Update an existing item in a list
- move_list_item - Move an item between list sections
Flexible Structure
Memory documents support any structure you need. The AI will help you organize sections based on your use case. Common examples:
- Travel Planning: destinations, itinerary, accommodations, activities, restaurants, travel tips, budget tracker
- Research Project: research questions, literature review, methodology, findings, data sources, next steps
- Real Estate Search: search criteria, active listings, visited properties, rejected properties, market insights, agent contacts
Example: Automatic Memory Building
Backup and Version Control
The server automatically creates timestamped backups before major updates.
Storage Locations
Memory document files are stored as markdown files in:
- macOS:
~/Library/Application Support/mcp-structured-memory/
- Windows:
%LOCALAPPDATA%\mcp-structured-memory\
- Linux:
~/.local/share/mcp-structured-memory/
This server cannot be installed
local-only server
The server can only run on the client's local machine because it depends on local resources.
Make it easy for agents to build their context about your projects over time
The server provides a set of tools to help agents accumulate knowledge about a project over time in a structured way.
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