To write unit tests for Model Context Protocol (MCP) tools and resources, start by isolating individual components of the system and validating their behavior under specific conditions. Focus on testing the core logic of MCP components—such as configuration parsing, data validation, resource allocation, and protocol-specific operations—without relying on external systems. Use a testing framework like pytest or unittest to structure your tests, and employ mocking libraries (e.g., unittest.mock) to simulate dependencies like databases or external APIs. For example, if your MCP tool processes model configuration files, write tests to verify that invalid configurations trigger appropriate errors, or that valid configurations correctly initialize model parameters.
A practical example involves testing a configuration loader. Suppose your MCP tool reads a YAML file to set up a model’s training parameters. Create a test that passes a YAML snippet with missing required fields (e.g., learning_rate) and assert that the loader raises a ValidationError. Another test could validate that a correctly formatted file populates a configuration object accurately. Similarly, if your MCP manages computational resources, write tests to check how the system handles scenarios like GPU allocation failures. For instance, mock a GPU availability check to return False and verify that the tool falls back to CPU mode or raises a clear error. These tests ensure that each component behaves as expected in both normal and edge cases.
Adopt best practices to maintain effective tests. First, structure tests to cover inputs, outputs, and error conditions for every function. Use fixtures to reuse setup code, like predefining valid/invalid configuration templates. Second, prioritize deterministic tests—avoid relying on external services or randomness. For example, if your MCP tool generates unique resource IDs, mock the ID generator to return fixed values for predictable assertions. Finally, integrate testing into your CI/CD pipeline to catch regressions early. Tools like coverage.py can help identify untested code paths. By systematically validating each MCP component’s logic and interactions, you’ll build confidence that the system works reliably across updates.