Yes, models can chain tools together using the Model Context Protocol (MCP). MCP is a framework designed to enable AI models to collaborate by passing structured context between them, allowing sequential or parallel execution of tasks. It acts as a communication layer that lets models share inputs, outputs, and intermediate data, making it possible to build workflows where each model or tool handles a specific part of a larger problem. For example, a text-generation model could pass its output to a translation model, which then sends results to a validation tool, with MCP managing the flow of data between each step.
A practical example of MCP in action is building a customer support pipeline. Suppose a user submits a query in French. The first model detects the language, the second translates it to English, and a third generates a response. MCP ensures each tool receives the necessary context (e.g., the original query, intermediate translations) and routes outputs to the next step. Developers define these workflows using MCP’s standardized data format, which includes metadata like timestamps, tool identifiers, and error flags. This structure allows tools to access prior outputs, modify them, and append new results without manual intervention.
From a technical standpoint, MCP requires developers to implement handlers for context ingestion and output formatting. For instance, each tool in the chain might expose an API endpoint that accepts MCP-compliant JSON payloads. The payload includes fields like task_id, input_data, and previous_steps, ensuring traceability. Error handling is simplified by MCP’s built-in mechanisms: if a translation tool fails, the protocol can route the context to a fallback service or flag the issue for review. This modularity makes it easier to scale workflows—adding a sentiment analysis step to the customer support pipeline, for example, would involve plugging in a new tool without disrupting existing steps. By abstracting communication logic, MCP lets developers focus on individual tool functionality while ensuring interoperability.