How do I use Memex Targeted Search Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Memex Targeted Search Server find the command I used to run the memex agent cli" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

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

search_conversations - Search conversation history by text, metadata, and filters
get_conversation_snippet - Retrieve specific parts of conversations without context overload
search_projects - Search project files by content, file types, and names
get_project_overview - Get project summaries with technology detection
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

// 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 noreply@memex.tech

Memex Targeted Search Server

Overview

Features

🔍 Core Search Tools

🎯 Smart Context Management

Installation

Configuration

MCP Server Configuration

Usage Examples

1. Find Forgotten Commands

"I don't remember what the command is to run the memex agent cli"

Find specific npm commands

2. Search Conversations

Find conversations about specific topics

Filter by date range and project

3. Get Conversation Details

Retrieve specific messages from a conversation

4. Search Projects

Find files by technology

Search all project files

5. Get Project Overview

Analyze project structure and tech stack

Real-World Usage Scenarios

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

Scenario 2: Finding Related Work

Scenario 3: Code Reference Lookup

Scenario 4: Cross-Reference Discovery

API Reference

search_conversations

get_conversation_snippet

search_projects

get_project_overview

find_command

Architecture

Performance Considerations

Agent Experience

Development

Running in Development

Building for Production

Testing

Contributing

License

Resources

New MCP Servers

Latest Blog Posts

MCP directory API