Nextcloud MCP Server

"""Unit tests for Pydantic response models.""" import pytest from nextcloud_mcp_server.models.notes import ( CreateNoteResponse, Note, NoteSearchResult, SearchNotesResponse, ) from nextcloud_mcp_server.models.semantic import ( SamplingSearchResponse, SemanticSearchResult, ) @pytest.mark.unit def test_note_model_creation(): """Test creating a Note model with required fields.""" note = Note( id=123, title="Test Note", content="# Test Content", modified=1700000000, etag="abc123", ) assert note.id == 123 assert note.title == "Test Note" assert note.content == "# Test Content" assert note.category == "" # default value assert note.favorite is False # default value assert note.etag == "abc123" @pytest.mark.unit def test_note_modified_datetime_property(): """Test that Note.modified_datetime converts Unix timestamp correctly.""" note = Note( id=1, title="Test", content="Content", modified=1700000000, etag="etag", ) dt = note.modified_datetime assert dt.year == 2023 # Nov 14, 2023 assert dt.month == 11 @pytest.mark.unit def test_create_note_response_serialization(): """Test CreateNoteResponse can serialize to JSON.""" response = CreateNoteResponse( id=42, title="New Note", category="Work", etag="xyz789", ) # Test serialization data = response.model_dump() assert data["id"] == 42 assert data["title"] == "New Note" assert data["category"] == "Work" assert data["etag"] == "xyz789" @pytest.mark.unit def test_search_notes_response_wraps_results(): """Test SearchNotesResponse wraps list of results correctly. This is critical - FastMCP mangles raw List[Dict] responses, so we must wrap them in a response model. """ results = [ NoteSearchResult(id=1, title="First Note", category="Work"), NoteSearchResult(id=2, title="Second Note", category="Personal"), ] response = SearchNotesResponse( results=results, query="test query", total_found=2, ) # Verify the response structure assert len(response.results) == 2 assert response.results[0].id == 1 assert response.results[1].title == "Second Note" assert response.query == "test query" assert response.total_found == 2 # Verify it serializes correctly data = response.model_dump() assert "results" in data assert isinstance(data["results"], list) assert len(data["results"]) == 2 assert data["results"][0]["id"] == 1 @pytest.mark.unit def test_note_search_result_with_score(): """Test NoteSearchResult with optional score field.""" result = NoteSearchResult( id=99, title="Relevant Note", category="Archive", score=0.95, ) assert result.id == 99 assert result.score == 0.95 @pytest.mark.unit def test_note_search_result_without_score(): """Test NoteSearchResult without optional score field.""" result = NoteSearchResult( id=99, title="Relevant Note", category="Archive", ) assert result.id == 99 assert result.score is None @pytest.mark.unit def test_sampling_search_response_with_answer(): """Test SamplingSearchResponse with LLM-generated answer.""" sources = [ SemanticSearchResult( id=1, doc_type="note", title="Python Guide", category="Development", excerpt="Use async/await for asynchronous programming", score=0.92, chunk_index=0, total_chunks=3, ), SemanticSearchResult( id=2, doc_type="note", title="Best Practices", category="Development", excerpt="Always use context managers with async operations", score=0.85, chunk_index=1, total_chunks=2, ), ] response = SamplingSearchResponse( query="How do I use async in Python?", generated_answer="Based on Document 1 and Document 2, use async/await for asynchronous programming and always use context managers.", sources=sources, total_found=2, search_method="semantic_sampling", model_used="claude-3-5-sonnet", stop_reason="endTurn", success=True, ) # Verify the response structure assert response.query == "How do I use async in Python?" assert "async/await" in response.generated_answer assert len(response.sources) == 2 assert response.sources[0].id == 1 assert response.sources[0].score == 0.92 assert response.total_found == 2 assert response.search_method == "semantic_sampling" assert response.model_used == "claude-3-5-sonnet" assert response.stop_reason == "endTurn" assert response.success is True # Verify it serializes correctly data = response.model_dump() assert "query" in data assert "generated_answer" in data assert "sources" in data assert isinstance(data["sources"], list) assert len(data["sources"]) == 2 assert data["sources"][0]["id"] == 1 assert data["model_used"] == "claude-3-5-sonnet" @pytest.mark.unit def test_sampling_search_response_fallback(): """Test SamplingSearchResponse when sampling fails (fallback mode).""" sources = [ SemanticSearchResult( id=1, doc_type="note", title="Note 1", category="Work", excerpt="Some content", score=0.75, chunk_index=0, total_chunks=1, ) ] response = SamplingSearchResponse( query="test query", generated_answer="[Sampling unavailable: Client does not support sampling]\n\nFound 1 relevant documents. Please review the sources below.", sources=sources, total_found=1, search_method="semantic_sampling_fallback", model_used=None, stop_reason=None, success=True, ) # Verify fallback behavior assert "[Sampling unavailable" in response.generated_answer assert response.search_method == "semantic_sampling_fallback" assert response.model_used is None assert response.stop_reason is None assert len(response.sources) == 1 @pytest.mark.unit def test_sampling_search_response_no_results(): """Test SamplingSearchResponse when no documents found.""" response = SamplingSearchResponse( query="nonexistent topic", generated_answer="No relevant documents found in your Nextcloud Notes for this query.", sources=[], total_found=0, search_method="semantic_sampling", success=True, ) # Verify no results case assert response.total_found == 0 assert len(response.sources) == 0 assert "No relevant documents" in response.generated_answer assert response.model_used is None assert response.stop_reason is None @pytest.mark.unit def test_sampling_search_response_serialization(): """Test SamplingSearchResponse serializes to JSON correctly.""" response = SamplingSearchResponse( query="test", generated_answer="Test answer", sources=[], total_found=0, search_method="semantic_sampling", model_used="claude-3-5-sonnet", stop_reason="maxTokens", success=True, ) data = response.model_dump() # Check all fields are present assert data["query"] == "test" assert data["generated_answer"] == "Test answer" assert data["sources"] == [] assert data["total_found"] == 0 assert data["search_method"] == "semantic_sampling" assert data["model_used"] == "claude-3-5-sonnet" assert data["stop_reason"] == "maxTokens" assert data["success"] is True