Revert MCP docs changes (split into separate PR)

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <[email protected]>

Author: cpsievert

docs/misc/mcp-tools.qmd

@@ -3,105 +3,67 @@ title: MCP tools
 callout-appearance: simple
 ---
 
-[Model Context Protocol (MCP)](https://modelcontextprotocol.io) provides a standard
+[Model Context Protocol (MCP)](https://modelcontextprotocol.io) provides a standard 
 way to build services that LLMs can use to gain context.
-This includes a standard way to provide [tools](../get-started/tools.qmd) (i.e., functions) for an LLM to call from another program or machine.
-There are now [many useful MCP server implementations available](https://glama.ai/mcp/servers) to help extend the capabilities of your chat application with minimal effort.
-
-In this article, you'll learn how to both register existing MCP tools with chatlas as well as author your own custom MCP tools.
+Most significantly, MCP provides a standard way to serve [tools](../get-started/tools.qmd) (i.e., functions) for an LLM to call from another program or machine.
+As a result, there are now [many useful MCP server implementations available](https://github.com/punkpeye/awesome-mcp-servers?tab=readme-ov-file#server-implementations) to help extend the capabilities of your chat application.
+In this article, you'll learn the basics of implementing and using MCP tools in chatlas.
 
 
 ::: callout-note
 ## Prerequisites
 
-To leverage MCP tools from chatlas, you'll want to install the `mcp` extra.
+To leverage MCP tools from chatlas, you'll need to install the `mcp` library.
 
 ```bash
 pip install 'chatlas[mcp]'
 ```
 :::
 
 
-## Registering tools
+## Basic usage
 
-### Quick start {#quick-start}
+Chatlas provides two ways to register MCP tools: [`.register_mcp_tools_http_stream_async()`](../reference/Chat.qmd#register_mcp_tools_http_stream_async) and [`.register_mcp_tools_stdio_async()`](../reference/Chat.qmd#register_mcp_tools_stdio_async).
 
-Let's start with a practical example: using the [MCP Fetch server](https://github.com/modelcontextprotocol/servers/tree/main/src/fetch) to give an LLM the ability to fetch and read web pages.
-This server is maintained by Anthropic and can be run via `uvx` (which comes with [uv](https://docs.astral.sh/uv/)).
 
-For simplicity and convenience, we'll use the [`.register_mcp_tools_stdio_async()`](../reference/Chat.qmd#register_mcp_tools_stdio_async) method to both run the MCP Fetch server locally and register its tools with our `ChatOpenAI` instance:
+The main difference is how they interact with the MCP server: the former connects to an already running HTTP server, while the latter executes a system command to run the server locally.
+Roughly speaking, usage looks something like this:
 
-```python
-import asyncio
-from chatlas import ChatOpenAI
+::: panel-tabset
 
-async def main():
-    chat = ChatOpenAI()
-    await chat.register_mcp_tools_stdio_async(
-        command="uvx",
-        args=["mcp-server-fetch"],
-    )
-    await chat.chat_async(
-        "Summarize the first paragraph of https://en.wikipedia.org/wiki/Python_(programming_language)"
-    )
-    await chat.cleanup_mcp_tools()
+### Streaming HTTP
 
-asyncio.run(main())
-```
-
-::: chatlas-response-container
 ```python
-# 🔧 tool request
-fetch(url="https://en.wikipedia.org/wiki/Python_(programming_language)")
-```
-
-Python is a high-level, general-purpose programming language known for its emphasis on code readability through significant indentation. It supports multiple programming paradigms including structured, object-oriented, and functional programming, and is dynamically typed with garbage collection.
-:::
-
-::: callout-tip
-### Built-in fetch/search tools
-
-For providers with native web fetch support (Claude, Google), consider using [`tool_web_fetch()`](../reference/tool_web_fetch.qmd) instead -- it's simpler and doesn't require MCP setup.
-Similarly, [`tool_web_search()`](../reference/tool_web_search.qmd) provides native web search for OpenAI, Claude, and Google.
-:::
-
-
-### Basic usage {#basic-usage}
-
-Chatlas provides three ways to register MCP tools: 
-
-1. Stdio ([`.register_mcp_tools_stdio_async()`](../reference/Chat.qmd#register_mcp_tools_stdio_async)) 
-2. Streamble HTTP [`.register_mcp_tools_http_stream_async()`](../reference/Chat.qmd#register_mcp_tools_http_stream_async).
-3. JSON configuration file [`.register_mcp_tools_from_config_async()`](../reference/Chat.qmd#register_mcp_tools_from_config_async).
-
-The main difference is how they communicate with the MCP server: the former (Stdio) executes a system command to run the server locally, while the latter (HTTP) connects to an already running HTTP server.
+from chatlas import ChatOpenAI
 
-This makes the Stdio method more ergonomic for local development and testing. For instance, recall the example above, which runs `uvx mcp-server-fetch` locally to provide web fetching capabilities to the chat instance:
+chat = ChatOpenAI()
 
-```python
-# Run a server via uvx, npx, or any other command
-await chat.register_mcp_tools_stdio_async(
-    command="uvx",
-    args=["mcp-server-fetch"],
+# Assuming you have an MCP server running at the specified URL
+await chat.register_mcp_tools_http_stream_async(
+    url="http://localhost:8000/mcp",
 )
 ```
 
-On the other hand, the HTTP method is better for production environments where the server is hosted remotely or in a longer-running process.
-For example, if you have an MCP server already running at `http://localhost:8000/mcp`, you can connect to it as follows:
+### Stdio (Standard Input/Output)
 
 ```python
-# Connect to a server already running at the specified URL
-await chat.register_mcp_tools_http_stream_async(
-    url="http://localhost:8000/mcp",
+from chatlas import ChatOpenAI
+
+chat = ChatOpenAI()
+
+# Assuming my_mcp_server.py is a valid MCP server script
+await chat.register_mcp_tools_stdio_async(
+    command="mcp",
+    args=["run", "my_mcp_server.py"],
 )
 ```
 
-Later on in this article, you'll learn 
+:::
 
 ::: callout-warning
 ### Async methods
 
-For performance reasons, the methods for registering MCP tools are asynchronous, so you'll need to use `await` when calling them.
+For performance reasons, the methods for registering MCP tools are asynchronous, so you'll need to use `await` when calling them. 
 In some environments, such as Jupyter notebooks and the [Positron IDE](https://positron.posit.co/) console, you can simply use `await` directly (as is done above).
 However, in other environments, you may need to wrap your code in an `async` function and use `asyncio.run()` to execute it.
 The examples below use `asyncio.run()` to run the asynchronous code, but you can adapt them to your environment as needed.
@@ -118,14 +80,13 @@ Note that these methods work by:
 ### Cleanup
 
 When you no longer need the MCP tools, it's important to clean up the connection to the MCP server, as well `Chat`'s tool state.
-This is done by calling [`.cleanup_mcp_tools()`](../reference/Chat.qmd#cleanup_mcp_tools) at the end of your chat session (the examples demonstrate how to do this).
+This is done by calling [`.cleanup_mcp_tools()`](../reference/Chat.qmd#cleanup_mcp_tools) at the end of your chat session (the examples demonstrate how to do this). 
 :::
 
 
-## Authoring tools
+## Basic example
 
-If existing MCP servers don't meet your needs, you can implement your own.
-Let's walk through a full-fledged example, including implementing a simple MCP server.
+Let's walk through a full-fledged example of using MCP tools in chatlas, including implementing our own MCP server.
 
 ### Basic server {#basic-server}
 
@@ -149,7 +110,7 @@ The `mcp` library provides a CLI tool to run the MCP server over HTTP transport.
 As long as you have `mcp` installed, and the [server above](#basic-server) saved as `my_mcp_server.py`, this can be done as follows:
 
 ```bash
-$ mcp run -t sse my_mcp_server.py
+$ mcp run -t sse my_mcp_server.py 
 INFO:     Started server process [19144]
 INFO:     Waiting for application startup.
 INFO:     Application startup complete.
@@ -180,12 +141,12 @@ asyncio.run(do_chat("What is 5 - 3?"))
 ::: chatlas-response-container
 
 ```python
-# 🔧 tool request
+# 🔧 tool request                        
 add(x=5, y=-3)
 ```
 
 ```python
-# ✅ tool result
+# ✅ tool result                  
 2
 ```
 
@@ -225,27 +186,27 @@ asyncio.run(do_chat("What is 5 - 3?"))
 ::: chatlas-response-container
 
 ```python
-# 🔧 tool request
+# 🔧 tool request                        
 add(x=5, y=-3)
 ```
 
 ```python
-# ✅ tool result
+# ✅ tool result                  
 2
 ```
 
 5 - 3 equals 2.
 :::
 
 
-## Advanced example: Code execution
+## Motivating example
 
 Let's look at a more compelling use case for MCP tools: code execution.
 A tool that can execute code and return the results is a powerful way to extend the capabilities of an LLM.
 This way, LLMs can generate code based on natural language prompts (which they are quite good at!) and then execute that code to get precise and reliable results from data (which LLMs are not so good at!).
 However, allowing an LLM to execute arbitrary code is risky, as the generated code could potentially be destructive, harmful, or even malicious.
 
-To mitigate these risks, it's important to implement safeguards around code execution.
+To mitigate these risks, it's important to implement safeguards around code execution. 
 This can include running code in isolated environments, restricting access to sensitive resources, and carefully validating and sanitizing inputs to the code execution tool.
 One such implementation is Pydantic's [Run Python MCP server](https://github.com/pydantic/pydantic-ai/tree/main/mcp-run-python), which provides a sandboxed environment for executing Python code safely via [Pyodide](https://pyodide.org/en/stable/) and [Deno](https://deno.com/).
 
@@ -281,4 +242,4 @@ async def _(user_input: str):
     await chat.append_message_stream(stream)
 ```
 
-![Screenshot of a LLM executing Python code via a tool call in a Shiny chatbot](../images/shiny-mcp-run-python.png){class="shadow rounded"}
+![Screenshot of a LLM executing Python code via a tool call in a Shiny chatbot](../images/shiny-mcp-run-python.png){class="shadow rounded"}