TimeLooker MCP Server with x402 Payment Integration

A Model Context Protocol (MCP) server that provides automated search monitoring functionality with x402 payment integration. Monitor search queries, detect new content using AI-powered duplicate detection, and automatically process payments for task creation.

<�� Architecture Overview

The system consists of multiple components working together:

Core Components

src/api/task_manager_api.py - FastAPI service with x402 payment middleware
src/api/task_manager_client.py - HTTP client with x402 payment support
src/mcp/searcher_mcp.py - MCP server exposing tools to Claude Desktop
src/core/search_engine.py - Web search execution and AI comparison
src/core/task_manager.py - Database operations and task management
src/core/models.py - SQLAlchemy database models

Payment Integration

x402 Protocol - Automatic payments for task creation ($1.00 per task)
Payment Address - 0x671cE47E4F38051ba3A990Ba306E2885C2Fe4102
Network - base-sepolia
Free Endpoints - All operations except task creation

=' Setup Instructions

Option A: Local Development Setup

1. Install Dependencies

cd /path/to/MCP_Server
uv sync

2. Environment Configuration

Copy the example environment file and configure it:

cp .env.example .env

Edit .env with your configuration:

# x402 Payment Configuration  
PAY_TO_ADDRESS=0x671cE47E4F38051ba3A990Ba306E2885C2Fe4102
PRIVATE_KEY=your_ethereum_private_key_here

# API Configuration
TASK_MANAGER_API_URL=http://localhost:8000

# OpenAI Configuration
OPENAI_API_KEY=your_openai_api_key_here

# Database Configuration (optional)
DATABASE_URL=sqlite:///timelooker.db

Option B: AWS Cloud Deployment

1. Prerequisites

AWS CLI configured with appropriate permissions
AWS CDK installed: npm install -g aws-cdk
Python dependencies: uv sync

2. Deploy Infrastructure

# Deploy AWS infrastructure (RDS, Lambda roles, SES, etc.)
python scripts/deploy_infrastructure.py

This creates:

PostgreSQL RDS database (db.t3.micro)
S3 bucket for email templates
AWS Secrets Manager for API keys
IAM roles for Lambda execution
SES email identity

3. Configure Secrets

After deployment, update the secrets in AWS Secrets Manager:

# Update OpenAI API key
aws secretsmanager update-secret \
  --secret-id "timelooker/openai/api-key" \
  --secret-string '{"api_key":"your_openai_key_here"}'

# Update X402 private key  
aws secretsmanager update-secret \
  --secret-id "timelooker/x402/private-key" \
  --secret-string '{"private_key":"your_private_key_here"}'

4. Verify SES Email

Go to AWS Console > SES > Verified identities and verify your sender email address.

5. Environment Configuration

The deployment script creates .env.aws with the infrastructure details. Update it with your values:

cp .env.aws .env
# Edit .env to add your private key and sender email

6. Test Secrets Integration

Verify that the system can retrieve secrets from AWS:

# Test secrets retrieval
python scripts/test_secrets.py

This will show whether the system can automatically retrieve database credentials, API keys, and other secrets from AWS Secrets Manager.

3. Database Management

Initialize Database

# Basic initialization
python scripts/init_db.py

# Initialize with sample data
python scripts/init_db.py --sample

Database Operations

# Check schema version
python scripts/init_db.py --version

# Validate database integrity
python scripts/init_db.py --validate

# Run database migrations
python scripts/init_db.py --migrate

# Reset database (drop and recreate)
python scripts/init_db.py --reset

# Reset and create sample data
python scripts/init_db.py --reset --sample

4. Start the API Server

python run_api_server.py
# or
uv run run_api_server.py

5. Configure Claude Desktop

Edit your Claude Desktop configuration file:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json

Choose the appropriate configuration based on your deployment mode:

For Local Development:

cp claude_desktop_config_local.json ~/Library/Application\ Support/Claude/claude_desktop_config.json

For AWS Cloud Deployment:

cp claude_desktop_config_cloud.json ~/Library/Application\ Support/Claude/claude_desktop_config.json
# Then edit the file to replace YOUR_ACCOUNT and endpoint values with actual values from deployment

Manual Configuration:

You can also manually copy the contents from claude_config_example.json and add your values.

Important: Replace /absolute/path/to/MCP_Server with your actual absolute path!

6. Restart Claude Desktop

Completely restart Claude Desktop after editing the configuration.

=�� MCP Tools Available

The MCP server provides 6 powerful tools for Claude Desktop:

=

create_search_task (Requires Payment) Create automated monitoring tasks for any search query

Payment: $1.00 per task creation
Parameters: query, frequency (min 1 min), email, runtime, sender email
Cloud Mode: Automatically deploys Lambda function + EventBridge schedule
Local Mode: Creates task in database for manual execution
Example: "Create a search task to monitor AI Ethics job postings every hour for the next 3 days"

� execute_search (Free)

Run a search for an existing task and get new results

Parameters: task_id
Example: "Execute search for task 1"

=� list_search_tasks (Free)

View all your active monitoring tasks

Example: "Show me all my search tasks"

=� get_task_status (Free)

Get detailed status and execution history for a task

Parameters: task_id, number of recent executions to show
Example: "What's the status of task 2?"

=�� delete_search_task (Free)

Deactivate a monitoring task

Parameters: task_id
Example: "Delete task 3"

=@ search_preview (Free)

Preview search results without creating a task

Parameters: query, max_results
Example: "Preview search results for 'Python developer remote jobs'"

=� Usage Examples

Once configured, you can use natural language in Claude Desktop:

Creating Tasks (Requires Payment)

"Create a search task to monitor new iPhone releases every 2 hours for the next week"
"Set up monitoring for 'remote Python jobs' checking every 5 minutes for 1 hour"
"Monitor AI safety research papers, check daily for 2 weeks"

Managing Tasks (Free)

"Show me all my active search tasks"
"What's the status of my first search task?"
"Execute a search for task 2"
"Delete the iPhone monitoring task"

Previewing (Free)

"Preview search results for 'Machine Learning conferences 2025'"
"Show me what results I'd get for monitoring crypto news"

( Key Features

>� AI-Powered Duplicate Detection

Uses OpenAI to identify genuinely new items vs. formatting variations
Significantly reduces false notifications
Considers content similarity, company, and location

=� x402 Payment Integration

Automatic payment processing for task creation
Free access to all other operations
Ethereum-based payments on base-sepolia network

=

Rich Search Results

Finds items from multiple sources across the web
Extracts titles, descriptions, URLs, locations, and more
Structured data format for easy processing

�� Flexible Monitoring

Any search query (jobs, products, news, research, etc.)
Configurable frequency (1 minute to days)
Customizable runtime periods (1 minute to weeks)

=� Email Notifications

Automatic notifications when new items are found
Structured email format with all item details
Configurable sender/recipient

=� Full History Tracking

Complete execution history for each task
Performance metrics and error tracking
Easy status monitoring through Claude Desktop

🗄️ Robust Database Management

Automated schema migrations and version tracking
Database integrity validation and orphan detection
Consistent session management with automatic cleanup
Comprehensive CLI tools for database operations

<�

B� Deployment Options

Local Development Mode

Setup: Use .env with local database (SQLite)
Task Execution: Manual via MCP tools or scheduled scripts
API Server: Run locally with python run_api_server.py
MCP Server: Run locally with python run_mcp_server.py
Database: SQLite file
Payments: Still processed via x402

AWS Cloud Mode

Setup: Use .env with DEPLOY_TO_CLOUD=true
Task Execution: Automatic via Lambda functions + EventBridge
Infrastructure: RDS PostgreSQL, Lambda functions, SES, S3
Scaling: Serverless, automatically handles multiple tasks
Cost: ~$15-30/month for typical usage
Benefits:
- No manual task execution needed
- Automatic cleanup after runtime expires
- Professional email templates via SES
- Scalable and fault-tolerant

Hybrid Mode

API Server: Local development with cloud database
Task Creation: Creates Lambda functions for execution
Best for: Development while using production infrastructure

Automation Options

Option 1: Cron Job

# Check every 5 minutes, run eligible tasks
*/5 * * * * cd /path/to/MCP_Server && python scripts/run_scheduled_tasks.py

Option 2: Background Service

# Run continuously, checking every 60 seconds
python scripts/run_scheduled_tasks.py --daemon --interval 60

=' API Endpoints

The FastAPI server provides these endpoints:

POST /tasks/ - Create task (requires payment)
GET /tasks/ - List active tasks
GET /tasks/{task_id} - Get task details
DELETE /tasks/{task_id} - Deactivate task
POST /executions/ - Create execution record
PUT /executions/{execution_id} - Update execution
GET /tasks/{task_id}/should-run - Check if task should run
POST /tasks/{task_id}/results - Save search results
GET /tasks/{task_id}/results - Get previous results
POST /tasks/{task_id}/notify - Send email notification

=� Troubleshooting

MCP Server Not Showing Up

Check Claude Desktop logs: ~/Library/Logs/Claude/mcp*.log
Verify the absolute path in your configuration
Ensure all dependencies are installed
Restart Claude Desktop completely

Payment Issues

Verify PRIVATE_KEY is set in .env
Ensure you have funds on base-sepolia network
Check that the private key is valid (without 0x prefix)

Search Errors

Verify OPENAI_API_KEY or ANTHROPIC_API_KEY is set and valid
Check internet connection
Look at API server logs for detailed error messages

Database Issues

Check if timelooker.db exists and is writable
Run python scripts/init_db.py --validate to check database integrity
Run python scripts/init_db.py --reset to reinitialize if corrupted
Use python scripts/init_db.py --version to check schema version
Verify SQLAlchemy connection string in environment variables

=� Task Frequency Guidelines

Real-time Testing: 1-5 minutes (short duration)
Breaking News: 5-30 minutes
Job Postings: 1-6 hours
Product Releases: 6-24 hours
Research Papers: 1-7 days

Note: Very frequent checks are great for testing, but be mindful of OpenAI API costs for long-running tasks.

=� Related Files

Project Organization

MCP_Server/
├── src/
│   ├── api/          # HTTP API layer
│   ├── core/         # Core business logic  
│   └── mcp/          # MCP server interface
├── tests/            # All test files
├── scripts/          # Utility scripts
├── run_mcp_server.py # MCP server entry point
└── run_api_server.py # API server entry point

Testing: tests/test_search_quality.py, tests/quick_quality_test.py, tests/monitor_query_test.py
Lambda: scripts/lambda_function.py, tests/test_lambda.py
Automation: scripts/run_scheduled_tasks.py
Entry Points: run_mcp_server.py, run_api_server.py

🧪 Testing

The project includes a comprehensive test suite covering search quality, payment integration, and API functionality.

Quick Test Run

# Run all tests
python tests/run_all_tests.py

# Interactive test selection
python tests/run_quality_tests.py

Test Categories

1. Search Quality Tests

Validate search result quality and duplicate detection:

python tests/quick_quality_test.py
python tests/test_search_quality.py

2. X402 Payment Integration Tests

Test payment flows with mocked x402 client:

python tests/test_x402_integration.py

Tests include:

TaskManager initialization with payment config
Create task with payment flow (mocked)
Free endpoints work without payment
Missing private key handling
Payment failure scenarios

3. API Integration Tests

Test FastAPI endpoints and database integration:

# Start API server first
python run_api_server.py

# In another terminal, run tests
python tests/test_api_integration.py