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Improve the v4 docs (#4707)

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Jeremiah Lowin 2026-07-29 09:51:13 -04:00 • committed by GitHub
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docs/tutorials/mcp.mdx
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@ -21,7 +21,7 @@ The answer lies in **standardization**. The AI ecosystem is fragmented. Every mo
1. **Interoperability:** Build one MCP server, and it can be used by any MCP-compliant client (Claude, Gemini, OpenAI, custom agents, etc.) without custom integration code. This is the protocol's most important promise.
2. **Discoverability:** Clients can dynamically ask a server what it's capable of at runtime. They receive a structured, machine-readable "menu" of tools and resources.
3. **Security & Safety:** MCP provides a clear, sandboxed boundary. An LLM can't execute arbitrary code on your server; it can only *request* to run the specific, typed, and validated functions you explicitly expose.
3. **Explicit boundaries:** MCP gives hosts and servers a typed inventory of the capabilities they expose. That creates a clear place to apply authorization, user confirmation, input validation, and sandboxing; the protocol defines the interface, while your application supplies those security policies.
4. **Composability:** You can build small, specialized MCP servers and combine them to create powerful, complex applications.
## Core MCP Components
@ -111,10 +111,6 @@ def summarize_text(text_to_summarize: str) -> str:
## Advanced Capabilities
Beyond the core components, MCP also supports more advanced interaction patterns, such as a server requesting that the *client's* LLM generate a completion (known as **sampling**), or a server sending asynchronous **notifications** to a client. These features enable more complex, bidirectional workflows and are fully supported by FastMCP.
Beyond tools, resources, and prompts, MCP supports richer interaction patterns such as notifications, progress updates, user elicitation, and argument completion. Extensions add capabilities such as durable background tasks.
## Next Steps
Now that you understand the core concepts of the Model Context Protocol, you're ready to start building. The best place to begin is our step-by-step tutorial.
[**Tutorial: How to Create an MCP Server in Python →**](/tutorials/create-mcp-server)
FastMCP exposes these patterns through typed Python APIs. For example, [elicitation](/servers/elicitation) lets tools request missing information or confirmation, while [background tasks](/servers/tasks) let long-running work continue after the original request returns.