Search Memories

Name: cems-recall
Rating: 88
Author: Chocksy

Use the memory_search MCP tool to find relevant memories.

Usage

When you need context before starting work, or the user asks to recall something:

•Formulate a natural language query
•Call memory_search with appropriate parameters
•Use the results to inform your work

MCP Tool Call

json

{
  "tool": "memory_search",
  "arguments": {
    "query": "authentication patterns in this project",
    "scope": "both",
    "max_results": 10,
    "max_tokens": 4000,
    "enable_graph": true,
    "enable_query_synthesis": true
  }
}

Parameters

Parameter	Default	Purpose
`query`	(required)	Natural language search query
`scope`	`"both"`	`"personal"`, `"shared"`, or `"both"`
`max_results`	`10`	Maximum results (1-20)
`max_tokens`	`4000`	Token budget for results
`enable_graph`	`true`	Include related memories via graph traversal
`enable_query_synthesis`	`true`	Expand query with LLM for better retrieval
`raw`	`false`	Debug mode: bypass relevance filtering
`project`	(optional)	Project ID to boost project-scoped memories

When to Search

•Before coding: Search for relevant patterns, conventions, preferences
•Before debugging: Search for similar issues and solutions
•Before decisions: Search for past architectural choices
•Before code review: Search for team conventions

Search Tips

•Use natural language: "how do we handle authentication" > "auth"
•Be specific: "Python backend database conventions" > "conventions"
•The system uses semantic matching, not just keywords
•Results include relevance scores and time decay ranking
•Use raw: true to debug retrieval when results seem wrong