GeoGuessrMCP/CLAUDE.md

CLAUDE.md - Development Guide for AI Assistants

This document provides context for AI assistants (like Claude) working on the GeoGuessr MCP Server project.

Project Overview

GeoGuessr MCP Server is a Model Context Protocol (MCP) server that provides Claude with tools to analyze GeoGuessr game statistics. The server features automatic API monitoring and dynamic schema adaptation to handle API changes gracefully.

Key Architecture Components

geoguessr-mcp/
├── src/geoguessr_mcp/
│   ├── api/              # GeoGuessr API client and response handling
│   ├── auth/             # Authentication management
│   ├── models/           # Data models (Profile, Stats, Games, etc.)
│   ├── monitoring/       # Dynamic schema detection and API monitoring
│   │   ├── endpoint/     # Endpoint monitoring logic
│   │   └── schema/       # Schema detection and registry
│   ├── services/         # Business logic services
│   ├── tools/            # MCP tool definitions
│   ├── config.py         # Configuration and settings
│   └── main.py           # Application entry point
├── tests/                # Unit and integration tests
├── Dockerfile            # Container definition
├── docker-compose.yml    # Development deployment
└── docker-compose.prod.yml # Production deployment with nginx

Core Concepts

1. Dynamic Schema System

The server automatically adapts to GeoGuessr API changes:

• Schema Detection: Analyzes API responses to detect field types, structure, and nesting
• Change Detection: Uses hash-based comparison to identify schema modifications
• Persistence: Caches schemas to disk for survival across restarts
• Monitoring: Runs periodic checks (every 24 hours by default) on all known endpoints

Key Files:

• src/geoguessr_mcp/monitoring/schema/schema_detector.py - Analyzes response structure
• src/geoguessr_mcp/monitoring/schema/schema_registry.py - Manages schema storage
• src/geoguessr_mcp/monitoring/endpoint/endpoint_monitor.py - Periodic monitoring

2. MCP Tool Architecture

Tools are organized by domain:

• auth_tools.py - Authentication (login, logout, cookie management)
• profile_tools.py - User profiles and statistics
• game_tools.py - Game history and details
• analysis_tools.py - Performance analysis and recommendations
• monitoring_tools.py - API health and endpoint exploration

Each tool returns a DynamicResponse which includes both the data and schema information.

3. Authentication Flow

The server supports three authentication methods:

1. Environment variable: GEOGUESSR_NCFA_COOKIE in .env
2. Login tool: Email/password authentication via MCP
3. Manual cookie: Direct cookie setting via tool

Session state is managed in src/geoguessr_mcp/auth/session.py.

Docker Deployment

Build Process

The Dockerfile uses a multi-stage approach:

1. Install dependencies from pyproject.toml using uv for speed
2. Copy source code
3. Run the server via python -m geoguessr_mcp.main

Important: The Dockerfile does NOT use a separate requirements.txt. It installs directly from pyproject.toml.

Deployment Options

1. Local Build: docker compose up -d --build
2. Docker Hub: Build, tag, push, then pull on VPS
3. Production: Use docker-compose.prod.yml with nginx reverse proxy

Monitoring Configuration

Monitoring runs every 24 hours by default:

• Set MONITORING_ENABLED=true to enable
• Set MONITORING_INTERVAL_HOURS=24 for interval
• Schemas are persisted in /app/data/schemas volume

Development Workflow

Running Tests

# Unit tests
pytest tests/unit/ -v

# Integration tests (requires auth)
pytest tests/integration/ -v

# With coverage
pytest --cov=src/geoguessr_mcp tests/

Local Development

# Install dependencies
pip install -e ".[dev]"

# Run server locally
python -m geoguessr_mcp.main

# Or use the entry point
geoguessr-mcp

Code Quality

• Formatter: Black (line length 100)
• Linter: Ruff
• Type Checker: MyPy (strict mode)

Run checks:

black src/ tests/
ruff check src/ tests/
mypy src/

Common Tasks

Adding a New Tool

1. Create tool function in appropriate tools/*.py file
2. Use @mcp.tool() decorator
3. Return DynamicResponse or structured data
4. Add to tool registration in __init__.py
5. Write unit tests

Example:

@mcp.tool()
async def my_new_tool(param: str) -> str:
    """Tool description for Claude."""
    # Implementation
    return result

Adding a New Endpoint to Monitor

1. Add endpoint definition to MONITORED_ENDPOINTS in endpoint_monitor.py
2. Specify path, method, params, and description
3. Schema detection happens automatically

Example:

EndpointDefinition(
    path="/v3/profiles/new-data",
    description="New profile data endpoint",
)

Updating Configuration

Configuration is managed via environment variables in config.py:

• Add new setting to Settings dataclass
• Use field(default_factory=lambda: os.getenv(...)) pattern
• Document in README.md environment variables section

Testing Strategy

Unit Tests

• Mock external API calls using respx
• Test each component in isolation
• Located in tests/unit/

Integration Tests

• Use fixtures from tests/conftest.py
• Mock authentication for CI/CD
• Test full request/response flows
• Located in tests/integration/

Fixtures

• mock_auth_session - Provides authenticated session
• mock_http_client - Mocked httpx client
• sample_* - Sample API responses

Key Dependencies

• mcp[cli] - Model Context Protocol framework
• httpx - Async HTTP client for API calls
• uvicorn - ASGI server
• starlette - Web framework (used by FastMCP)
• python-dotenv - Environment variable management

Troubleshooting

Common Issues

1. "server.py not found": The entry point is main.py, not server.py
2. Schema not persisting: Check volume mount for /app/data/schemas
3. Monitoring not running: Verify MONITORING_ENABLED=true and check logs
4. Auth failures: Ensure valid NCFA cookie or use login tool

Debugging

Enable debug logging:

LOG_LEVEL=DEBUG docker compose up

Check monitoring status via tool:

# In Claude
check_api_status()

CI/CD Considerations

Building for Production

# Multi-architecture build
docker buildx build \
  --platform linux/amd64,linux/arm64 \
  -t username/geoguessr-mcp:latest \
  --push .

# Single architecture
docker build -t username/geoguessr-mcp:latest .
docker push username/geoguessr-mcp:latest

Environment Variables for Deployment

Required:

• GEOGUESSR_NCFA_COOKIE - For authenticated endpoints

Optional:

• MONITORING_INTERVAL_HOURS - Default: 24
• LOG_LEVEL - Default: INFO
• MCP_PORT - Default: 8000

Schema Registry Details

Schema Hash Computation

Schemas are identified by a hash of:

• Field names and types
• Nested structure
• Array element types
• Null/undefined handling

Schema Change Detection

When a schema changes:

1. New hash is computed
2. Comparison with previous hash
3. If different, schema_changed flag is set
4. New schema is persisted to disk
5. Monitoring result includes change notification

Schema Persistence Format

Schemas are stored as JSON in SCHEMA_CACHE_DIR:

{
  "endpoint": "/v3/profiles",
  "schema_hash": "abc123...",
  "fields": [...],
  "last_updated": "2025-11-29T...",
  "http_status": 200
}

API Monitoring System

Monitored Endpoints

Currently monitoring ~15 endpoints:

• Profile & Stats (v3, v4)
• Games & Social (feed, friends, badges)
• Competitive (seasons)
• Explorer & Objectives
• Challenges (daily)

Monitoring Loop

1. Every 24 hours (configurable)
2. Checks each endpoint sequentially
3. 0.5s delay between requests (rate limiting)
4. Updates schema registry on changes
5. Logs availability and response times
6. Generates monitoring report

Monitoring Tools

Users can:

• check_api_status() - Get current status of all endpoints
• get_endpoint_schema(path) - Get schema for specific endpoint
• list_available_endpoints() - List all known endpoints
• explore_endpoint(path) - Manually check a new endpoint

Best Practices

1. Always use DynamicResponse for tools that return API data
2. Mock authentication in tests using fixtures
3. Keep schemas generic - don't hardcode field expectations
4. Log schema changes at INFO level for visibility
5. Use async/await consistently throughout the codebase
6. Handle rate limiting with delays between requests
7. Validate configuration in Settings.post_init
8. Document tools with clear docstrings for Claude

Future Enhancements

Potential areas for improvement:

• Add metrics collection (Prometheus/Grafana)
• Implement rate limiting middleware
• Add caching layer for frequently accessed data
• Support for webhooks/real-time updates
• Advanced analytics and ML-based recommendations
• Multi-user support with session management
• GraphQL endpoint for flexible queries

Resources

• MCP Documentation
• FastMCP Guide
• GeoGuessr API (unofficial)
• Docker Best Practices

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Last Updated: 2025-11-29 Maintainer: See pyproject.toml for author information