MCP supports structured JSON-based message formats for tool communication, ensuring that models and external tools exchange data in a predictable and machine-readable way. JSON is used because it is language-independent, easy to validate, and integrates well with schema definitions. Each tool in MCP declares its expected input and output through JSON Schema, which allows the model to understand what arguments must be provided and what structure the returned data will follow. This avoids ambiguity and keeps tool calls consistent across environments.
In practice, MCP message formats include fields describing the tool name, argument structure, error states, and response payloads. For example, a tool that expects an embedding vector would declare a JSON array of numbers, and the model must submit its arguments in that exact shape. This prevents failures caused by unexpected formats or incorrectly typed parameters. Because JSON Schema is part of the contract, developers can use existing validation libraries to ensure the implementation behaves correctly, and AI models can safely reason about tool capabilities and produce valid inputs.
For vector databases like Milvus, JSON formatting works well because embedding vectors, search parameters, and query results map cleanly to JSON structures. A search tool might accept a JSON payload with fields like "embedding": [0.12, 0.48, …], "top_k": 10, and "collection": "documents". The tool would respond with matched vector IDs, distances, and metadata encoded in JSON. This makes Milvus operations easy to wrap into MCP tools because both sides speak the same predictable language. Standardizing on JSON ensures that retrieval pipelines remain stable even as models change or the underlying database evolves.