memex-targeted-search-server

memex-targeted-search-server

3.2

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The Memex Targeted Search Server is a Model Context Protocol (MCP) server designed to provide efficient and targeted search capabilities across Memex conversation history and project files.

Memex Targeted Search Server

A Model Context Protocol (MCP) server that provides targeted search capabilities across Memex conversation history and project files.

Overview

This MCP server enables AI agents to efficiently search through:

  • Conversation History: 952+ conversation files from Memex with metadata, titles, summaries, and message content
  • Project Files: 516+ project directories in the user's workspace with various file types and technologies

Features

🔍 Core Search Tools

  1. search_conversations - Search conversation history by text, metadata, and filters
  2. get_conversation_snippet - Retrieve specific parts of conversations without context overload
  3. search_projects - Search project files by content, file types, and names
  4. get_project_overview - Get project summaries with technology detection
  5. find_command - NEW! Find specific commands, CLI usage, or code snippets from conversation history

🎯 Smart Context Management

  • Returns targeted snippets instead of full conversations
  • Limits search scope to prevent context explosion
  • Supports faceted filtering (dates, projects, file types)
  • Provides relevance scoring for search results

Installation

# Clone the repository
git clone https://github.com/memextech/memex-targeted-search-server.git
cd memex-targeted-search-server

# Install dependencies
npm install

# Build the project
npm run build

Configuration

The server is configured to search:

  • Conversation History: ~/Library/Application Support/Memex/history/
  • Project Files: ~/Workspace/

MCP Server Configuration

Add to your MCP configuration (e.g., Claude Desktop config):

{
  "mcpServers": {
    "memex-search": {
      "command": "node",
      "args": ["/path/to/memex-targeted-search-server/dist/index.js"]
    }
  }
}

Usage Examples

1. Find Forgotten Commands

"I don't remember what the command is to run the memex agent cli"
find_command({
  query: "memex agent cli",
  command_type: "cli",
  limit: 5
})
Find specific npm commands
find_command({
  query: "npm install",
  command_type: "cli",
  limit: 5
})

Example Response:

{
  "query": "npm install",
  "total_found": 3,
  "commands": [
    {
      "command": "npm install -g firebase-tools",
      "context": "Install Firebase CLI: `npm install -g firebase-tools`\n- Login to Firebase: `firebase login`",
      "conversation_id": "abc123",
      "conversation_title": "Firebase Setup Guide",
      "message_index": 7,
      "confidence": 0.9,
      "type": "cli"
    }
  ]
}

2. Search Conversations

Find conversations about specific topics
search_conversations({
  query: "3D modeling",
  limit: 5
})

Example Response:

{
  "total_found": 3,
  "conversations": [
    {
      "conversation_id": "a3edfc8f-0978-415e-9de8-18f4d94ea3a2",
      "title": "3D Interactive Solar System Model",
      "summary": "Design an engaging, visually appealing 3D representation of planets and celestial bodies",
      "created_at": "2025-05-27T17:13:26Z",
      "project": "Stellar 3d solar system",
      "message_count": 76,
      "relevance": "content"
    }
  ]
}
Filter by date range and project
search_conversations({
  query: "python",
  project: "cad_example",
  date_from: "2025-01-01",
  date_to: "2025-03-01",
  limit: 3
})

3. Get Conversation Details

Retrieve specific messages from a conversation
get_conversation_snippet({
  conversation_id: "bf283daa-25d3-434f-ad7e-9adda48cdcdd",
  message_start: 1,
  message_count: 3
})

Example Response:

{
  "conversation_id": "bf283daa-25d3-434f-ad7e-9adda48cdcdd",
  "title": "3D Model 3MF File Creation",
  "message_range": "1-3",
  "total_messages": 30,
  "messages": [
    {
      "index": 1,
      "role": "user",
      "content": "can I create a 3D model in .3mf?"
    },
    {
      "index": 2,
      "role": "assistant",
      "content": "I'll help you create a 3D model using PythonOCC and convert it to .3mf format..."
    }
  ]
}

4. Search Projects

Find files by technology
search_projects({
  query: "interface",
  file_types: ["ts", "js"],
  limit: 10
})
Search all project files
search_projects({
  query: "streamlit",
  limit: 5
})

Example Response:

{
  "total_found": 3,
  "results": [
    {
      "project": "ad_campaign_dashboard",
      "file": "ad_campaign_dashboard/app.py",
      "match": "import streamlit as st",
      "line": 1
    }
  ]
}

5. Get Project Overview

Analyze project structure and tech stack
get_project_overview({
  project_name: "memex_targeted_search_server"
})

Example Response:

{
  "name": "memex_targeted_search_server",
  "path": "/Users/user/Workspace/memex_targeted_search_server",
  "file_count": 8,
  "directories": ["dist", "src"],
  "file_types": {
    "ts": 1,
    "js": 1,
    "json": 3,
    "md": 1
  },
  "main_files": ["package.json", "README.md"],
  "technologies": ["JavaScript/TypeScript"]
}

Real-World Usage Scenarios

Scenario 1: "I forgot that command..."

// User: "I don't remember what the command is to run the memex agent cli"
find_command({
  query: "memex agent",
  command_type: "cli",
  limit: 5
})

// User: "What was that firebase command to deploy?"
find_command({
  query: "firebase deploy",
  command_type: "cli",
  limit: 3
})

// Result: Finds exact commands with context from previous conversations

Scenario 2: Finding Related Work

// Agent: "I need to find previous conversations about Blender projects"
search_conversations({
  query: "blender",
  limit: 5
})

// Result: Finds 2 conversations about 3D Manhattan cityscape and geometric skyscraper
// Agent can then drill down into specific conversations for details

Scenario 3: Code Reference Lookup

// Agent: "Show me Python projects that use Streamlit"
search_projects({
  query: "streamlit",
  file_types: ["py"],
  limit: 10
})

// Result: Finds specific Python files with Streamlit imports
// Agent can then examine project structure and implementation patterns

Scenario 4: Cross-Reference Discovery

// Agent: "Find conversations from January 2025 about 3D modeling"
search_conversations({
  query: "3D model",
  date_from: "2025-01-01",
  date_to: "2025-01-31",
  limit: 5
})

// Agent: "Now show me the related project files"
get_project_overview({
  project_name: "cad_example"
})

API Reference

search_conversations

  • Purpose: Search conversation history with flexible filtering
  • Parameters: query (required), limit, project, date_from, date_to
  • Returns: Array of conversation metadata with relevance scoring

get_conversation_snippet

  • Purpose: Retrieve specific message ranges from conversations
  • Parameters: conversation_id (required), message_start, message_count
  • Returns: Conversation snippet with message details

search_projects

  • Purpose: Search project files by content and metadata
  • Parameters: query (required), file_types, limit
  • Returns: Array of file matches with context

get_project_overview

  • Purpose: Analyze project structure and technology stack
  • Parameters: project_name (required)
  • Returns: Project summary with file counts and tech detection

find_command

  • Purpose: Find specific commands, CLI usage, or code snippets from conversation history
  • Parameters: query (required), command_type (cli/code/config/any), limit
  • Returns: Array of commands with context, confidence scoring, and conversation references

Architecture

Built with:

  • TypeScript - Type-safe development
  • MCP SDK - Official Model Context Protocol SDK
  • Node.js - Runtime environment
  • File System APIs - Direct file access for performance

Performance Considerations

  • Limits search scope to prevent overwhelming results
  • Uses streaming JSON parsing for large files
  • Implements intelligent file filtering
  • Caches frequently accessed metadata
  • Returns truncated content with full context available on demand

Agent Experience

The server is designed for optimal agent interaction:

  • Targeted Search: Find specific information without context overload
  • Faceted Filtering: Multiple search dimensions (date, project, file type)
  • Progressive Discovery: Start with summaries, drill down to details
  • Context Preservation: Maintain conversation and project relationships

Development

Running in Development

npm run dev

Building for Production

npm run build
npm start

Testing

The server includes comprehensive error handling and graceful degradation for:

  • Missing or corrupted conversation files
  • Inaccessible project directories
  • Invalid JSON parsing
  • Large file handling

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

ISC License

🤖 Generated with Memex
Co-Authored-By: Memex