Refactor monitoring module: modularized EndpointMonitor and SchemaRegistry into separate submodules under endpoint and schema respectively. Centralized endpoint definitions, improved structure, and updated imports accordingly.

src/geoguessr_mcp/monitoring/__init__.py

@@ -1,13 +1,9 @@
 """Monitoring module for API endpoint tracking and schema detection."""
 
-from .endpoint_monitor import EndpointMonitor, endpoint_monitor, MONITORED_ENDPOINTS
+from .endpoint.EndpointMonitor import EndpointMonitor, endpoint_monitor, MONITORED_ENDPOINTS
-from .schema_manager import (
+from schema.EndpointSchema import EndpointSchema
-    SchemaDetector,
+from schema.SchemaRegistry import SchemaRegistry, schema_registry
-    SchemaRegistry,
+from schema.SchemaDetector import SchemaDetector, SchemaField
-    EndpointSchema,
-    SchemaField,
-    schema_registry,
-)
 
 __all__ = [
     "EndpointMonitor",

src/geoguessr_mcp/monitoring/endpoint/EndpointDefinition.py

@@ -0,0 +1,19 @@
+"""
+Definition of API endpoint data structure to monitor.
+
+This module provides the `EndpointDefinition` class to encapsulate necessary
+details about an API endpoint, such as its path, request method, authentication
+requirement, and additional parameters.
+"""
+
+from dataclasses import dataclass, field
+
+@dataclass
+class EndpointDefinition:
+    """Definition of an API endpoint to monitor."""
+    path: str
+    method: str = "GET"
+    requires_auth: bool = True
+    use_game_server: bool = False
+    params: dict = field(default_factory=dict)
+    description: str = ""

src/geoguessr_mcp/monitoring/endpoint/EndpointMonitor.py

@@ -1,35 +1,30 @@
 """
-API Endpoint Monitor.
+Monitors GeoGuessr API endpoints for availability and schema changes.
 
-This module provides automated monitoring of GeoGuessr API endpoints,
+This module is responsible for monitoring a set of known GeoGuessr API endpoints
-checking their availability and detecting response format changes.
+to ensure their availability and detect schema changes. It periodically checks
+each endpoint, logs relevant details about its status, and updates a schema
+registry when necessary.
+
+Classes:
+    EndpointMonitor: Manages periodic checks of API endpoints, updates schema
+    registry, and logs activity.
 """
 
 import asyncio
 import logging
-from dataclasses import dataclass, field
 from datetime import datetime, UTC
 from typing import Optional
 
 import httpx
 
-from ..config import settings
+from ...config import settings
-from .schema_manager import SchemaRegistry, schema_registry
+from .EndpointDefinition import EndpointDefinition
+from .EndpointMonitoringResult import MonitoringResult
+from ..schema.SchemaRegistry import SchemaRegistry, schema_registry
 
 logger = logging.getLogger(__name__)
 
-
-@dataclass
-class EndpointDefinition:
-    """Definition of an API endpoint to monitor."""
-    path: str
-    method: str = "GET"
-    requires_auth: bool = True
-    use_game_server: bool = False
-    params: dict = field(default_factory=dict)
-    description: str = ""
-
-
 # Known GeoGuessr API endpoints to monitor
 MONITORED_ENDPOINTS = [
     # Profile endpoints
@@ -121,19 +116,6 @@ MONITORED_ENDPOINTS = [
     ),
 ]
 
-
-@dataclass
-class MonitoringResult:
-    """Result of monitoring an endpoint."""
-    endpoint: str
-    is_available: bool
-    response_code: int
-    response_time_ms: float
-    schema_changed: bool
-    error_message: Optional[str] = None
-    timestamp: datetime = field(default_factory=lambda: datetime.now(UTC))
-
-
 class EndpointMonitor:
     """
     Monitors API endpoints for availability and schema changes.

src/geoguessr_mcp/monitoring/endpoint/EndpointMonitoringResult.py

@@ -0,0 +1,23 @@
+"""
+This module defines a data structure representing the result of monitoring a
+network endpoint.
+
+It contains the necessary details to capture the status and performance of
+an endpoint, including its availability, response time, and any errors encountered.
+"""
+
+from dataclasses import dataclass, field
+from datetime import datetime, UTC
+from typing import Optional
+
+
+@dataclass
+class MonitoringResult:
+    """Result of monitoring an endpoint."""
+    endpoint: str
+    is_available: bool
+    response_code: int
+    response_time_ms: float
+    schema_changed: bool
+    error_message: Optional[str] = None
+    timestamp: datetime = field(default_factory=lambda: datetime.now(UTC))

src/geoguessr_mcp/monitoring/schema/EndpointSchema.py

@@ -0,0 +1,97 @@
+"""
+Schema definitions and utility methods for managing API endpoints.
+
+This module provides the `EndpointSchema` class, which offers the ability
+to define the schema of an API endpoint, serialize and deserialize data, and
+manage metadata such as response codes and availability. The class also
+includes helper utilities for handling data transformations and validating
+schema information.
+"""
+
+import logging
+from dataclasses import dataclass, field
+from datetime import datetime, UTC
+from typing import Any, Optional
+
+from .SchemaField import SchemaField
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class EndpointSchema:
+    """Schema definition for an API endpoint."""
+    endpoint: str
+    method: str
+    fields: dict[str, SchemaField] = field(default_factory=dict)
+    last_updated: datetime = field(default_factory=lambda: datetime.now(UTC))
+    schema_hash: str = ""
+    response_code: int = 200
+    is_available: bool = True
+    error_message: Optional[str] = None
+    sample_response: Optional[dict] = None
+
+    def to_dict(self) -> dict:
+        """Convert to dictionary for serialization."""
+        return {
+            "endpoint": self.endpoint,
+            "method": self.method,
+            "fields": {
+                name: {
+                    "name": f.name,
+                    "field_type": f.field_type,
+                    "nullable": f.nullable,
+                    "nested_schema": f.nested_schema,
+                    "example_value": self._serialize_example(f.example_value),
+                    "description": f.description,
+                }
+                for name, f in self.fields.items()
+            },
+            "last_updated": self.last_updated.isoformat(),
+            "schema_hash": self.schema_hash,
+            "response_code": self.response_code,
+            "is_available": self.is_available,
+            "error_message": self.error_message,
+            "sample_response": self.sample_response,
+        }
+
+    @staticmethod
+    def _serialize_example(value: Any) -> Any:
+        """Safely serialize example values."""
+        if isinstance(value, (str, int, float, bool, type(None))):
+            return value
+        if isinstance(value, (list, dict)):
+            return str(value)[:100] + "..." if len(str(value)) > 100 else value
+        return str(value)
+
+    @classmethod
+    def from_dict(cls, data: dict) -> "EndpointSchema":
+        """Create from dictionary."""
+        fields = {}
+        for name, f_data in data.get("fields", {}).items():
+            fields[name] = SchemaField(
+                name=f_data["name"],
+                field_type=f_data["field_type"],
+                nullable=f_data.get("nullable", False),
+                nested_schema=f_data.get("nested_schema"),
+                example_value=f_data.get("example_value"),
+                description=f_data.get("description", ""),
+            )
+
+        last_updated = data.get("last_updated")
+        if isinstance(last_updated, str):
+            last_updated = datetime.fromisoformat(last_updated)
+        else:
+            last_updated = datetime.now(UTC)
+
+        return cls(
+            endpoint=data["endpoint"],
+            method=data.get("method", "GET"),
+            fields=fields,
+            last_updated=last_updated,
+            schema_hash=data.get("schema_hash", ""),
+            response_code=data.get("response_code", 200),
+            is_available=data.get("is_available", True),
+            error_message=data.get("error_message"),
+            sample_response=data.get("sample_response"),
+        )

src/geoguessr_mcp/monitoring/schema/SchemaDetector.py

@@ -0,0 +1,127 @@
+"""
+This module provides a dynamic JSON schema detection and analysis utility.
+
+It includes functionality to infer types of various data values, analyze JSON
+response structures, compute schema hashes, and identify nested schemas.
+The module is particularly useful for understanding and working with
+dynamic JSON datasets and detecting changes in schema over time.
+"""
+
+import hashlib
+import json
+import logging
+from datetime import datetime
+from typing import Any
+
+from .SchemaField import SchemaField
+
+logger = logging.getLogger(__name__)
+
+
+class SchemaDetector:
+    """Detects and analyzes JSON response schemas dynamically."""
+
+    @staticmethod
+    def detect_type(value: Any) -> str:
+        """Detect the type of value."""
+        if value is None:
+            return "null"
+        if isinstance(value, bool):
+            return "boolean"
+        if isinstance(value, int):
+            return "integer"
+        if isinstance(value, float):
+            return "number"
+        if isinstance(value, str):
+            # Try to detect special string types
+            if SchemaDetector._is_iso_datetime(value):
+                return "datetime"
+            if SchemaDetector._is_uuid(value):
+                return "uuid"
+            if SchemaDetector._is_url(value):
+                return "url"
+            return "string"
+        if isinstance(value, list):
+            return "array"
+        if isinstance(value, dict):
+            return "object"
+        return "unknown"
+
+    @staticmethod
+    def _is_iso_datetime(value: str) -> bool:
+        """Check if string is ISO datetime format."""
+        try:
+            datetime.fromisoformat(value.replace("Z", "+00:00"))
+            return True
+        except (ValueError, AttributeError):
+            return False
+
+    @staticmethod
+    def _is_uuid(value: str) -> bool:
+        """Check if string is UUID format."""
+        import re
+        uuid_pattern = r'^[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}$'
+        return bool(re.match(uuid_pattern, value.lower()))
+
+    @staticmethod
+    def _is_url(value: str) -> bool:
+        """Check if string is URL format."""
+        return value.startswith(("http://", "https://"))
+
+    def analyze_response(self, data: Any, max_depth: int = 5) -> dict[str, SchemaField]:
+        """
+        Analyze a JSON response and extract its schema.
+
+        Args:
+            data: The JSON response data
+            max_depth: Maximum depth for nested object analysis
+
+        Returns:
+            Dictionary mapping field names to SchemaField objects
+        """
+        if not isinstance(data, dict):
+            return {}
+
+        fields = {}
+        self._analyze_object(data, fields, "", max_depth)
+        return fields
+
+    def _analyze_object(
+            self,
+            obj: dict,
+            fields: dict,
+            prefix: str,
+            remaining_depth: int
+    ) -> None:
+        """Recursively analyze an object and extract field information."""
+        if remaining_depth <= 0:
+            return
+
+        for key, value in obj.items():
+            field_name = f"{prefix}.{key}" if prefix else key
+            field_type = self.detect_type(value)
+
+            nested_schema = None
+            if field_type == "object" and isinstance(value, dict):
+                nested_schema = {}
+                self._analyze_object(value, nested_schema, "", remaining_depth - 1)
+            elif field_type == "array" and value and isinstance(value[0], dict):
+                nested_schema = {}
+                self._analyze_object(value[0], nested_schema, "", remaining_depth - 1)
+
+            fields[field_name] = SchemaField(
+                name=field_name,
+                field_type=field_type,
+                nullable=value is None,
+                nested_schema=nested_schema if nested_schema else None,
+                example_value=value,
+            )
+
+    @staticmethod
+    def compute_schema_hash(fields: dict[str, SchemaField]) -> str:
+        """Compute a hash of the schema for change detection."""
+        schema_repr = json.dumps(
+            {name: (f.field_type, f.nullable) for name, f in sorted(fields.items())},
+            sort_keys=True
+        )
+        return hashlib.sha256(schema_repr.encode()).hexdigest()[:16]

src/geoguessr_mcp/monitoring/schema/SchemaField.py

@@ -0,0 +1,20 @@
+"""
+Represents a single field in a schema.
+
+This module defines a dataclass that encapsulates the attributes of
+a schema field, including its name, type, and other relevant metadata.
+"""
+
+from dataclasses import dataclass
+from typing import Any, Optional
+
+
+@dataclass
+class SchemaField:
+    """Represents a single field in a schema."""
+    name: str
+    field_type: str
+    nullable: bool = False
+    nested_schema: Optional[dict] = None
+    example_value: Any = None
+    description: str = ""

src/geoguessr_mcp/monitoring/schema/SchemaRegistry.py

@@ -1,223 +1,29 @@
 """
-Dynamic Schema Detection and Management.
+Handles schema versioning, storage, and updates for API endpoints.
 
-This module automatically detects, tracks, and adapts to changes in API response formats.
+The module provides functionality to detect and manage changes in API response
-It maintains a versioned history of schemas and provides tools for the LLM to understand
+schemas, maintain history of schema versions, and persist schema data to disk.
-the current data structure.
+It supports use cases such as tracking endpoint availability, generating schema
+summaries, and providing dynamic endpoint descriptions.
+
+Classes:
+    SchemaRegistry
 """
 
-import hashlib
 import json
 import logging
-from dataclasses import dataclass, field
 from datetime import datetime, UTC
 from pathlib import Path
 from typing import Any, Optional
 
-from ..config import settings
+
+from ...config import settings
+from .EndpointSchema import EndpointSchema
+from .SchemaDetector import SchemaDetector
 
 logger = logging.getLogger(__name__)
 
 
-@dataclass
-class SchemaField:
-    """Represents a single field in a schema."""
-    name: str
-    field_type: str
-    nullable: bool = False
-    nested_schema: Optional[dict] = None
-    example_value: Any = None
-    description: str = ""
-
-
-@dataclass
-class EndpointSchema:
-    """Schema definition for an API endpoint."""
-    endpoint: str
-    method: str
-    fields: dict[str, SchemaField] = field(default_factory=dict)
-    last_updated: datetime = field(default_factory=lambda: datetime.now(UTC))
-    schema_hash: str = ""
-    response_code: int = 200
-    is_available: bool = True
-    error_message: Optional[str] = None
-    sample_response: Optional[dict] = None
-
-    def to_dict(self) -> dict:
-        """Convert to dictionary for serialization."""
-        return {
-            "endpoint": self.endpoint,
-            "method": self.method,
-            "fields": {
-                name: {
-                    "name": f.name,
-                    "field_type": f.field_type,
-                    "nullable": f.nullable,
-                    "nested_schema": f.nested_schema,
-                    "example_value": self._serialize_example(f.example_value),
-                    "description": f.description,
-                }
-                for name, f in self.fields.items()
-            },
-            "last_updated": self.last_updated.isoformat(),
-            "schema_hash": self.schema_hash,
-            "response_code": self.response_code,
-            "is_available": self.is_available,
-            "error_message": self.error_message,
-            "sample_response": self.sample_response,
-        }
-
-    @staticmethod
-    def _serialize_example(value: Any) -> Any:
-        """Safely serialize example values."""
-        if isinstance(value, (str, int, float, bool, type(None))):
-            return value
-        if isinstance(value, (list, dict)):
-            return str(value)[:100] + "..." if len(str(value)) > 100 else value
-        return str(value)
-
-    @classmethod
-    def from_dict(cls, data: dict) -> "EndpointSchema":
-        """Create from dictionary."""
-        fields = {}
-        for name, f_data in data.get("fields", {}).items():
-            fields[name] = SchemaField(
-                name=f_data["name"],
-                field_type=f_data["field_type"],
-                nullable=f_data.get("nullable", False),
-                nested_schema=f_data.get("nested_schema"),
-                example_value=f_data.get("example_value"),
-                description=f_data.get("description", ""),
-            )
-
-        last_updated = data.get("last_updated")
-        if isinstance(last_updated, str):
-            last_updated = datetime.fromisoformat(last_updated)
-        else:
-            last_updated = datetime.now(UTC)
-
-        return cls(
-            endpoint=data["endpoint"],
-            method=data.get("method", "GET"),
-            fields=fields,
-            last_updated=last_updated,
-            schema_hash=data.get("schema_hash", ""),
-            response_code=data.get("response_code", 200),
-            is_available=data.get("is_available", True),
-            error_message=data.get("error_message"),
-            sample_response=data.get("sample_response"),
-        )
-
-
-class SchemaDetector:
-    """Detects and analyzes JSON response schemas dynamically."""
-
-    @staticmethod
-    def detect_type(value: Any) -> str:
-        """Detect the type of value."""
-        if value is None:
-            return "null"
-        if isinstance(value, bool):
-            return "boolean"
-        if isinstance(value, int):
-            return "integer"
-        if isinstance(value, float):
-            return "number"
-        if isinstance(value, str):
-            # Try to detect special string types
-            if SchemaDetector._is_iso_datetime(value):
-                return "datetime"
-            if SchemaDetector._is_uuid(value):
-                return "uuid"
-            if SchemaDetector._is_url(value):
-                return "url"
-            return "string"
-        if isinstance(value, list):
-            return "array"
-        if isinstance(value, dict):
-            return "object"
-        return "unknown"
-
-    @staticmethod
-    def _is_iso_datetime(value: str) -> bool:
-        """Check if string is ISO datetime format."""
-        try:
-            datetime.fromisoformat(value.replace("Z", "+00:00"))
-            return True
-        except (ValueError, AttributeError):
-            return False
-
-    @staticmethod
-    def _is_uuid(value: str) -> bool:
-        """Check if string is UUID format."""
-        import re
-        uuid_pattern = r'^[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}$'
-        return bool(re.match(uuid_pattern, value.lower()))
-
-    @staticmethod
-    def _is_url(value: str) -> bool:
-        """Check if string is URL format."""
-        return value.startswith(("http://", "https://"))
-
-    def analyze_response(self, data: Any, max_depth: int = 5) -> dict[str, SchemaField]:
-        """
-        Analyze a JSON response and extract its schema.
-
-        Args:
-            data: The JSON response data
-            max_depth: Maximum depth for nested object analysis
-
-        Returns:
-            Dictionary mapping field names to SchemaField objects
-        """
-        if not isinstance(data, dict):
-            return {}
-
-        fields = {}
-        self._analyze_object(data, fields, "", max_depth)
-        return fields
-
-    def _analyze_object(
-            self,
-            obj: dict,
-            fields: dict,
-            prefix: str,
-            remaining_depth: int
-    ) -> None:
-        """Recursively analyze an object and extract field information."""
-        if remaining_depth <= 0:
-            return
-
-        for key, value in obj.items():
-            field_name = f"{prefix}.{key}" if prefix else key
-            field_type = self.detect_type(value)
-
-            nested_schema = None
-            if field_type == "object" and isinstance(value, dict):
-                nested_schema = {}
-                self._analyze_object(value, nested_schema, "", remaining_depth - 1)
-            elif field_type == "array" and value and isinstance(value[0], dict):
-                nested_schema = {}
-                self._analyze_object(value[0], nested_schema, "", remaining_depth - 1)
-
-            fields[field_name] = SchemaField(
-                name=field_name,
-                field_type=field_type,
-                nullable=value is None,
-                nested_schema=nested_schema if nested_schema else None,
-                example_value=value,
-            )
-
-    @staticmethod
-    def compute_schema_hash(fields: dict[str, SchemaField]) -> str:
-        """Compute a hash of the schema for change detection."""
-        schema_repr = json.dumps(
-            {name: (f.field_type, f.nullable) for name, f in sorted(fields.items())},
-            sort_keys=True
-        )
-        return hashlib.sha256(schema_repr.encode()).hexdigest()[:16]
-
-
 class SchemaRegistry:
     """
     Manages schema storage, versioning, and change detection.