MCP-Memos
MCP-Memos
MCP-Memos is an memo tool based on MCP that allows users to record and retrieve text information.
It's perfect for developers to quickly save and find information in their workflow(may be cursor, etc) without switching to other applications.
🔍 Advanced LLM-Powered Search
MCP-Memos uses large language models for retrieval, providing the most powerful fuzzy search capability available:
- Semantic understanding: Find content based on meaning, not just keywords
- Context-aware: Understands what you're looking for even with incomplete descriptions
- Natural language queries: Search as you would ask a human, no special syntax needed
- Conceptual matching: Retrieves information by understanding concepts, not just text matching
Unlike traditional vector or text-based fuzzy search, MCP-Memos leverages the full power of LLMs to truly understand your retrieval intent, making it the most effective information retrieval approach available today.
Tool Documentation
| Tool Name | Description | Input Parameters |
|---|---|---|
| store_memo | Save important text information and add tags for easy retrieval later | tag:keyword or tag or description content: that you want to save |
| retrieve_memo | Retrieve previously saved text content based on keywords | text keyword or tag or description |
How to Use
Installation
Download the mcp-memos binary file according to your computer's architecture from releases
Configuration
Add MCP-Memos to the macPilotCli configuration file
{
"mcpServers": {
"MCP-Memos":{
"command": "path/to/mcp-memos",
"env": {
"LLM_TOKEN": "xxxxxx",
"LLM_BASE_URL": "xxxxxxxx/v1",
"ANTHROPIC_MODEL": "xxxxx"
}
}
}
}
Basic Usage
Record information
When you need to save information during development without switching to another app:
record this memo. its {description}
{content}
Retrieve information
When you need to find previously saved information:
Find this memo about {description}
NOTE
Descriptions can vary as MCP-Memo's LLM understands concepts, not just keywords, matching similar content regardless of wording.
LLM_BASE_URL optional, default is https://api.anthropic.com
ANTHROPIC_MODEL optional, default is claude-3-7-sonnet-20250219
Will accelerate the implementation of the sampling feature, which will eliminate the need to configure all environment parameters