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The real magic of a fleet is composition: feeding one tool's output straight into another, even when they live on different servers. 🔗

Imagine a search server that finds a file path, then a files server that reads it. Linking those steps forms a useful chain from a single request.

You rarely hardcode these flows. The model reads one tool's result, decides the next step, and calls the right tool on the right server itself.

Composition only flows if results are clean. Returning structured output, not loose prose, lets the next tool receive exactly the field it expects.

When chaining, the model pulls a value like an id from step one and feeds it as an argument to step two. Predictable schemas make that handoff reliable.

Several chained calls form an agent loop: act, observe the result, act again. The host runs each step and feeds the output back to the model.

Because tools share one pool, the model does not care that search and read sit on different servers. To it, the workflow is just one smooth sequence.

Each step adds its result to the context. Trim bulky outputs so a long chain does not blow the model's budget before it reaches the goal.

If a middle step fails, return a clear error the model can read. It can then retry, pick another tool, or stop instead of chaining onto garbage.

Tools that do one thing well and return tidy data are the easiest to compose. Small, sharp tools combine into powerful workflows. 🧩

Composition is where MCP shines: independent servers chain into rich, multi-step workflows that feel like one capable assistant. ✨

What makes chaining a tool's output into another tool reliable?

The model chains tools across servers, feeding clean structured output from one into the next. Small, sharp tools compose into rich workflows. 🎉