Implement Search and Filtering for MCP Servers

## Overview
Add comprehensive search and filtering capabilities to help users find servers quickly in large deployments.

## Business Value
As deployments grow, finding specific servers becomes difficult. Search and filtering are essential for usability at scale.

## Technology Stack
- Backend: Go, PostgreSQL full-text search
- Frontend: TypeScript, React, Next.js
- Database: PostgreSQL GIN indexes, tsvector
- UI: Search components, filter chips, URL state management

## Skill Level Required
- Intermediate
- Understanding of database full-text search
- Experience with React state management
- Knowledge of URL query parameter handling
- Performance optimization awareness

## Learning Opportunities
- Learn PostgreSQL full-text search implementation
- Understand search indexing strategies
- Practice building performant search UIs
- Gain experience with URL state management
- Learn about search result highlighting techniques

## Impact
This feature dramatically improves usability for large deployments. Contributors will learn about search implementation, a common requirement in modern applications, and performance optimization techniques.

## Current State
- Basic server listing exists
- No search functionality
- No filtering options

## Subtasks
- [ ] Implement full-text search
- [ ] Add filter by status
- [ ] Add filter by type
- [ ] Add filter by tags
- [ ] Implement date range filtering
- [ ] Add sorting options
- [ ] Create search UI component
- [ ] Add search result highlighting

## Implementation Steps
1. Add search to backend repository methods
2. Implement PostgreSQL full-text search
3. Add filter parameters to API endpoints
4. Create search UI in frontend
5. Add filter chips and controls

## Getting Started
1. Review existing server listing code
2. Study PostgreSQL full-text search documentation
3. Check frontend component patterns
4. Review URL state management in Next.js

## Acceptance Criteria
- [ ] Full-text search works across server names and descriptions
- [ ] Multiple filters can be combined
- [ ] Search results are highlighted
- [ ] Filters persist in URL
- [ ] Search is performant (<100ms)
- [ ] UI is intuitive and accessible

## Estimated Effort
- Hours: 12-16
- Complexity: Medium