# ICE Locator MCP Server - Technical Specifications
## Enhanced User Experience Features
### 1. Natural Language Query Processing
#### Smart Query Parser
```python
class SmartQueryParser:
"""Parses natural language queries into structured search parameters"""
async def parse_query(self, query: str, context: str = None) -> SearchRequest:
"""
Examples:
- "Find John Doe from Mexico born around 1990"
- "Search for A123456789"
- "Locate Maria Rodriguez detained in Texas"
"""
patterns = {
'name_pattern': r'(?:find|search|locate)\s+([A-Za-z\s]+?)(?:\s+from|\s+born|\s+detained|$)',
'country_pattern': r'from\s+([A-Za-z\s]+?)(?:\s+born|\s+detained|$)',
'birth_year_pattern': r'(?:born|birth)\s+(?:around\s+)?(\d{4})',
'alien_number_pattern': r'A\d{8,9}',
'location_pattern': r'(?:detained|in)\s+([A-Za-z\s]+)'
}
# Extract components using NLP and regex patterns
extracted = await self._extract_components(query, patterns)
# Apply auto-corrections and suggestions
corrected = await self._apply_corrections(extracted)
return SearchRequest(**corrected)
```
#### Auto-Correction Engine
```python
class AutoCorrectionEngine:
"""Handles common misspellings and variations"""
def __init__(self):
self.name_corrections = {
'jose': ['josé', 'joseph'],
'maria': ['maría', 'mary'],
'gonzalez': ['gonzález', 'gonzales']
}
self.country_corrections = {
'mexico': ['méxico', 'mexican'],
'guatemala': ['guatamala', 'guatemalan'],
'el salvador': ['salvador', 'salvadoran']
}
async def suggest_corrections(self, text: str) -> List[str]:
"""Return list of suggested corrections"""
suggestions = []
# Check against known corrections
for correct, variations in self.name_corrections.items():
if text.lower() in [v.lower() for v in variations]:
suggestions.append(correct)
# Use fuzzy matching for unknown terms
fuzzy_matches = await self._fuzzy_match(text)
suggestions.extend(fuzzy_matches)
return suggestions
```
### 2. Fuzzy Matching System
#### Advanced Name Matching
```python
class FuzzyMatcher:
"""Handles name variations and phonetic matching"""
def __init__(self):
self.soundex = SoundexAlgorithm()
self.metaphone = MetaphoneAlgorithm()
self.levenshtein = LevenshteinDistance()
async def calculate_similarity(self, name1: str, name2: str) -> float:
"""Calculate similarity score between names (0.0-1.0)"""
# Exact match
if name1.lower() == name2.lower():
return 1.0
# Phonetic similarity
soundex_score = self.soundex.similarity(name1, name2)
metaphone_score = self.metaphone.similarity(name1, name2)
# Edit distance similarity
levenshtein_score = self.levenshtein.similarity(name1, name2)
# Weighted combination
final_score = (
soundex_score * 0.3 +
metaphone_score * 0.4 +
levenshtein_score * 0.3
)
return final_score
async def find_fuzzy_matches(self, target_name: str, candidates: List[str],
threshold: float = 0.7) -> List[Tuple[str, float]]:
"""Find fuzzy matches above threshold"""
matches = []
for candidate in candidates:
score = await self.calculate_similarity(target_name, candidate)
if score >= threshold:
matches.append((candidate, score))
return sorted(matches, key=lambda x: x[1], reverse=True)
```
### 3. Bulk Search Operations
#### Concurrent Search Manager
```python
class BulkSearchManager:
"""Manages multiple concurrent searches with rate limiting"""
def __init__(self, max_concurrent: int = 3):
self.max_concurrent = max_concurrent
self.semaphore = asyncio.Semaphore(max_concurrent)
self.rate_limiter = RateLimiter(requests_per_minute=10)
async def bulk_search(self, requests: List[SearchRequest]) -> BulkSearchResult:
"""Execute multiple searches concurrently"""
results = []
failed = []
async def process_single_search(request: SearchRequest) -> Optional[SearchResult]:
async with self.semaphore:
try:
await self.rate_limiter.acquire()
result = await self.search_engine.search(request)
return result
except Exception as e:
failed.append((request, str(e)))
return None
# Execute searches with progress tracking
tasks = [process_single_search(req) for req in requests]
for i, coro in enumerate(asyncio.as_completed(tasks)):
result = await coro
if result:
results.append(result)
# Report progress
progress = (i + 1) / len(requests) * 100
await self._report_progress(progress)
return BulkSearchResult(
successful=results,
failed=failed,
total_processed=len(requests),
success_rate=len(results) / len(requests)
)
```
## Anti-Detection System Specifications
### 1. Proxy Management Architecture
#### Proxy Pool Manager
```python
class ProxyPoolManager:
"""Manages rotating pool of proxy servers"""
def __init__(self):
self.proxy_pool: List[ProxyConfig] = []
self.failed_proxies: Set[str] = set()
self.proxy_metrics: Dict[str, ProxyMetrics] = {}
self.rotation_interval = 300 # 5 minutes
self.max_requests_per_proxy = 10
async def get_next_proxy(self) -> ProxyConfig:
"""Get next available proxy with health check"""
# Filter healthy proxies
healthy_proxies = [
proxy for proxy in self.proxy_pool
if proxy.endpoint not in self.failed_proxies
and self._is_proxy_healthy(proxy)
]
if not healthy_proxies:
await self._refresh_proxy_pool()
healthy_proxies = self.proxy_pool
# Select least used proxy
selected = min(
healthy_proxies,
key=lambda p: self.proxy_metrics.get(p.endpoint, ProxyMetrics()).request_count
)
# Update usage metrics
await self._update_proxy_metrics(selected)
return selected
async def mark_proxy_failed(self, proxy: ProxyConfig, error: Exception):
"""Mark proxy as failed and remove from pool"""
self.failed_proxies.add(proxy.endpoint)
await self._log_proxy_failure(proxy, error)
# Auto-replacement if pool size drops below threshold
if len(self.proxy_pool) - len(self.failed_proxies) < 3:
await self._replenish_proxy_pool()
```
#### IP Rotation Strategy
```python
class IPRotationStrategy:
"""Implements intelligent IP rotation patterns"""
def __init__(self):
self.rotation_patterns = ['round_robin', 'random', 'geographic', 'performance_based']
self.current_pattern = 'performance_based'
self.rotation_history: List[RotationEvent] = []
async def schedule_rotation(self) -> RotationSchedule:
"""Create rotation schedule based on traffic patterns"""
schedule = RotationSchedule()
# Analyze current traffic patterns
traffic_analysis = await self._analyze_traffic_patterns()
# Determine optimal rotation frequency
if traffic_analysis.risk_level == 'HIGH':
schedule.rotation_frequency = 60 # Every minute
schedule.pattern = 'random'
elif traffic_analysis.risk_level == 'MEDIUM':
schedule.rotation_frequency = 180 # Every 3 minutes
schedule.pattern = 'geographic'
else:
schedule.rotation_frequency = 300 # Every 5 minutes
schedule.pattern = 'performance_based'
return schedule
```
### 2. Request Obfuscation Engine
#### Header Randomization
```python
class HeaderRandomizer:
"""Randomizes HTTP headers to mimic different browsers"""
def __init__(self):
self.user_agents = self._load_user_agent_database()
self.browser_profiles = self._load_browser_profiles()
self.header_templates = self._load_header_templates()
async def randomize_headers(self, base_headers: Dict[str, str]) -> Dict[str, str]:
"""Generate randomized headers for anti-detection"""
# Select browser profile
profile = random.choice(self.browser_profiles)
headers = base_headers.copy()
# Randomize User-Agent
headers['User-Agent'] = self._select_user_agent(profile)
# Randomize Accept headers
headers['Accept'] = self._randomize_accept_header(profile)
headers['Accept-Language'] = self._randomize_accept_language()
headers['Accept-Encoding'] = 'gzip, deflate, br'
# Add browser-specific headers
if profile.browser == 'chrome':
headers['Sec-Ch-Ua'] = self._generate_chrome_sec_headers()
headers['Sec-Ch-Ua-Mobile'] = '?0'
headers['Sec-Ch-Ua-Platform'] = f'"{profile.platform}"'
# Randomize connection settings
headers['Connection'] = random.choice(['keep-alive', 'close'])
headers['Cache-Control'] = random.choice(['no-cache', 'max-age=0'])
return headers
```
#### Timing Obfuscation
```python
class TimingObfuscator:
"""Creates human-like timing patterns"""
def __init__(self):
self.timing_profiles = {
'human_fast': {'base': 1.0, 'variance': 0.5, 'pattern': 'burst'},
'human_normal': {'base': 2.0, 'variance': 1.0, 'pattern': 'steady'},
'human_slow': {'base': 4.0, 'variance': 2.0, 'pattern': 'deliberate'}
}
self.current_profile = 'human_normal'
async def calculate_delay(self, request_type: str, context: RequestContext) -> float:
"""Calculate human-like delay for next request"""
profile = self.timing_profiles[self.current_profile]
# Base delay
base_delay = profile['base']
# Add variance
variance = random.uniform(-profile['variance'], profile['variance'])
# Adjust for request type
type_multiplier = {
'search': 1.0,
'form_load': 0.5,
'result_parse': 0.3,
'navigation': 0.8
}.get(request_type, 1.0)
# Adjust for session context
if context.consecutive_requests > 3:
# Slow down if making many consecutive requests
context_multiplier = 1.5
elif context.error_count > 0:
# Slow down after errors
context_multiplier = 2.0
else:
context_multiplier = 1.0
final_delay = base_delay * type_multiplier * context_multiplier + variance
# Ensure minimum delay
return max(final_delay, 0.5)
```
### 3. Behavioral Simulation
#### Human Interaction Patterns
```python
class BehaviorSimulator:
"""Simulates realistic human browsing behavior"""
def __init__(self):
self.interaction_patterns = self._load_interaction_patterns()
self.session_state = SessionState()
async def simulate_form_interaction(self, form_data: Dict[str, str]) -> None:
"""Simulate human-like form filling"""
for field_name, value in form_data.items():
# Simulate typing speed
typing_delay = len(value) * random.uniform(0.05, 0.15)
await asyncio.sleep(typing_delay)
# Simulate occasional pauses (thinking)
if random.random() < 0.3:
thinking_pause = random.uniform(0.5, 2.0)
await asyncio.sleep(thinking_pause)
async def simulate_page_reading(self, content_length: int) -> None:
"""Simulate time spent reading page content"""
# Average reading speed: 200-300 words per minute
words_estimate = content_length / 5 # Rough word count
reading_time = words_estimate / random.uniform(200, 300) * 60
# Add scanning vs. detailed reading variation
if self.session_state.is_scanning:
reading_time *= 0.3
await asyncio.sleep(min(reading_time, 10.0)) # Cap at 10 seconds
async def simulate_navigation_pattern(self, action: str) -> None:
"""Simulate realistic navigation behavior"""
patterns = {
'initial_visit': {
'actions': ['load_page', 'scan_content', 'find_search_form'],
'timing': [2.0, 1.5, 1.0]
},
'form_submission': {
'actions': ['fill_form', 'review_input', 'submit'],
'timing': [3.0, 1.0, 0.5]
},
'result_processing': {
'actions': ['scan_results', 'read_details', 'process_info'],
'timing': [1.5, 3.0, 2.0]
}
}
pattern = patterns.get(action, patterns['initial_visit'])
for step, delay in zip(pattern['actions'], pattern['timing']):
await self._execute_behavior_step(step)
await asyncio.sleep(delay + random.uniform(-0.5, 0.5))
```
### 4. CAPTCHA Handling Strategy
#### Adaptive CAPTCHA Solver
```python
class CaptchaSolver:
"""Handles various CAPTCHA challenges"""
def __init__(self):
self.solving_strategies = {
'recaptcha_v2': self._solve_recaptcha_v2,
'recaptcha_v3': self._solve_recaptcha_v3,
'hcaptcha': self._solve_hcaptcha,
'image_captcha': self._solve_image_captcha
}
self.solver_services = self._initialize_solver_services()
async def detect_captcha_type(self, page_content: str) -> Optional[str]:
"""Detect CAPTCHA type from page content"""
captcha_indicators = {
'recaptcha_v2': ['recaptcha', 'g-recaptcha'],
'recaptcha_v3': ['recaptcha/api.js', 'grecaptcha.execute'],
'hcaptcha': ['hcaptcha', 'h-captcha'],
'image_captcha': ['captcha.jpg', 'captcha.png', 'verification image']
}
for captcha_type, indicators in captcha_indicators.items():
if any(indicator in page_content.lower() for indicator in indicators):
return captcha_type
return None
async def solve_captcha(self, captcha_type: str, captcha_data: Dict) -> Optional[str]:
"""Attempt to solve CAPTCHA using multiple strategies"""
strategy = self.solving_strategies.get(captcha_type)
if not strategy:
return None
try:
# Try automated solving first
solution = await strategy(captcha_data)
if solution:
return solution
# Fall back to external services
for service in self.solver_services:
if service.supports(captcha_type):
solution = await service.solve(captcha_data)
if solution:
return solution
except Exception as e:
await self._log_captcha_error(captcha_type, e)
return None
```
## Repository Discoverability Strategy
### 1. GitHub SEO Optimization
#### Repository Metadata
```yaml
# .github/repository-metadata.yml
name: "ICE Locator MCP Server"
description: "Model Context Protocol server for programmatic access to ICE detainee location system"
topics:
- mcp-server
- ice-locator
- immigration
- legal-tech
- detainee-search
- llm-tools
- ai-agents
- web-scraping
- model-context-protocol
- government-api
homepage: "https://github.com/yourusername/ice-locator-mcp"
documentation: "https://yourusername.github.io/ice-locator-mcp"
# Social preview
social_preview:
title: "ICE Locator MCP Server"
description: "Connect AI agents to ICE detainee location services"
image: "docs/images/social-preview.png"
```
#### README Optimization
```markdown
# 🔍 ICE Locator MCP Server
[](https://modelcontextprotocol.io)
[](https://www.python.org/downloads/)
[](https://opensource.org/licenses/MIT)
[](https://github.com/yourusername/ice-locator-mcp/actions)
> **Empower AI agents with ICE detainee location capabilities**
Connect your LLM applications to the U.S. Immigration and Customs Enforcement (ICE) Online Detainee Locator System through a standardized Model Context Protocol (MCP) interface.
## 🚀 Quick Start
```bash
# Install via pip
pip install ice-locator-mcp
# Configure for Claude Desktop
echo '{
"mcpServers": {
"ice-locator": {
"command": "ice-locator-mcp",
"args": []
}
}
}' >> ~/Library/Application\ Support/Claude/claude_desktop_config.json
```
## ✨ Features
- 🔍 **Smart Search**: Natural language queries with fuzzy matching
- 🛡️ **Anti-Detection**: Advanced IP rotation and behavioral simulation
- 📊 **Bulk Operations**: Search multiple detainees simultaneously
- 🌐 **Multilingual**: Support for English, Spanish, and more
- 📋 **Legal Reports**: Generate comprehensive reports for legal use
- 🔒 **Privacy-First**: Local processing with optional caching
## 🎯 Use Cases
- **Legal Representatives**: Quickly locate clients in ICE custody
- **Family Members**: Find detained relatives with AI assistance
- **Advocacy Organizations**: Streamline detainee location workflows
- **AI Assistants**: Enable immigration-related support capabilities
```
### 2. MCP Registry Integration
#### Server Manifest
```json
{
"name": "ice-locator",
"version": "1.0.0",
"description": "MCP server for ICE detainee location services",
"author": "Your Name <your.email@example.com>",
"license": "MIT",
"homepage": "https://github.com/trose/ice-locator-mcp",
"repository": {
"type": "git",
"url": "https://github.com/trose/ice-locator-mcp.git"
},
"keywords": [
"mcp",
"ice",
"immigration",
"detainee",
"legal",
"government",
"ai-tools"
],
"capabilities": {
"tools": [
"search_detainee_by_name",
"search_detainee_by_alien_number",
"smart_detainee_search",
"bulk_search_detainees",
"generate_search_report"
],
"resources": [
"facility_information",
"legal_resources",
"help_documentation"
]
},
"requirements": {
"python": ">=3.10",
"mcp_sdk": ">=1.2.0"
},
"configuration": {
"proxy_enabled": {
"type": "boolean",
"default": true,
"description": "Enable proxy rotation for anti-detection"
},
"cache_duration": {
"type": "integer",
"default": 3600,
"description": "Cache duration in seconds"
}
}
}
```
This comprehensive technical specification provides the foundation for building a robust, discoverable, and user-friendly ICE Locator MCP Server that can be delivered within the accelerated 3-4 week timeline.
