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docs/MCP Python SDK/06_fastmcp_context___context__.md
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---
layout: default
title: "FastMCP Context (Context)"
parent: "MCP Python SDK"
nav_order: 6
---
# Chapter 6: Talking Back - FastMCP Context (`Context`)
In [Chapter 5: Reusable Chat Starters - FastMCP Prompts (`Prompt`, `PromptManager`)](05_fastmcp_prompts___prompt____promptmanager__.md), we learned how to create reusable message templates for interacting with AI models. We've seen how to build servers with data resources ([Chapter 3](03_fastmcp_resources___resource____resourcemanager__.md)) and action tools ([Chapter 4](04_fastmcp_tools___tool____toolmanager__.md)).
@@ -153,7 +160,7 @@ if __name__ == "__main__":
1. **`@server.resource(...)`**: We added a simple resource named `config://task_settings` that just returns a string.
2. **`resource_contents = await ctx.read_resource("config://task_settings")`**: Inside our `run_long_task` tool, we now use `ctx.read_resource()` to fetch the content of our configuration resource. This allows the tool to dynamically access data managed by the server without having direct access to the resource's implementation function (`get_task_settings`).
3. **Processing Content**: The `read_resource` method returns an iterable of `ReadResourceContents` objects (often just one). We extract the string content to use it.
3. **Processing Content**: The `read_resource` method returns an iterable of `ReadResourceContents` objects (often just one). We extracted the string content to use it.
Now, our tool can both communicate outwards (logs, progress) and interact inwards (read resources) using the same `Context` object, all within the scope of the single request it's handling.