To test a Model Context Protocol (MCP) server locally, start by setting up a controlled environment that mirrors production as closely as possible. Use a local development server instance, and ensure dependencies like databases or caching systems are containerized (e.g., via Docker) to isolate testing. Write unit tests for individual API endpoints using frameworks like pytest (Python) or Mocha (JavaScript), validating request handling, response formats, and error conditions. For example, test if the server returns a 400 error when sent invalid JSON or a 200 response with correct data when inputs are valid. Mock external services or databases to avoid side effects and ensure tests run quickly and reproducibly.
Next, perform integration testing by simulating client-server interactions. Create a test client that sends requests to the local MCP server and validates end-to-end behavior. For instance, if the MCP server processes model inference requests, send a payload with sample input data and check if the response matches expected output structures. Use tools like Postman or curl for manual testing, or automate workflows with scripts (e.g., Bash or Python). Monitor logs and metrics to identify bottlenecks or unexpected behavior, such as slow response times under load. Additionally, test edge cases, such as large payloads or high concurrency, to uncover resource leaks or performance issues. For example, use Apache Bench (ab) or Locust to simulate multiple clients and measure how the server handles 100+ simultaneous requests.
Finally, validate protocol-specific features unique to MCP, such as context management or session persistence. If the protocol requires maintaining state across requests (e.g., tracking a conversation history), write tests that verify state is stored and updated correctly. For example, send a sequence of requests with incremental context and check if the server’s responses reflect the accumulated data. Use tools like WireMock to mock dependent services if the MCP server interacts with external APIs. Additionally, test security measures like authentication tokens or rate limiting by sending unauthorized requests or exceeding allowed quotas. For thorough validation, integrate testing into a CI/CD pipeline (e.g., GitHub Actions or GitLab CI) to run tests automatically on code changes, ensuring regressions are caught early.