Testing MCP tools before integrating them involves validating tool schemas, verifying backend logic, and simulating realistic model interactions. The first step is ensuring that each tool’s JSON Schema accurately reflects its expected inputs and outputs. Developers can use standard schema validators to test whether sample payloads match the specification. This helps catch format mismatches early, such as incorrect field types or missing required keys, which would otherwise cause runtime issues when the model tries to call the tool.
The next step is testing the tool implementation directly within the MCP server. Developers can use scripts or lightweight test clients to send mock tool invocation requests that mirror what a model would send. This allows testing backend logic—for example, ensuring that an embedding insertion tool correctly writes to Milvus or that a search tool returns expected results for known vectors. These tests should also cover error states, such as invalid embeddings, oversized batches, or missing parameters, to ensure the tool responds with informative error messages.
Finally, end-to-end testing simulates how an AI model would interact with the tool. This involves connecting an MCP client, retrieving the tool list, and performing a tool call using the same message flow the model would use. These tests confirm that the tool registers correctly, follows MCP semantics, and interacts with downstream systems such as Milvus as expected. By combining schema validation, backend logic testing, and simulated model interactions, developers can ensure that MCP tools are reliable before they become part of production retrieval workflows.