docs[patch]: Update tool concepts docs (#6134)

* Update tool concepts docs

* Fix typo

docs/core_docs/docs/concepts.mdx

@@ -261,7 +261,7 @@ This is where information like log-probs and token usage may be stored.
 These represent a decision from an language model to call a tool. They are included as part of an `AIMessage` output.
 They can be accessed from there with the `.tool_calls` property.
 
-This property returns an array of objects. Each object has the following keys:
+This property returns a list of `ToolCall`s. A `ToolCall` is an object with the following arguments:
 
 - `name`: The name of the tool that should be called.
 - `args`: The arguments to that tool.
@@ -558,72 +558,72 @@ A tool consists of:
 When a tool is bound to a model, the name, description and JSON schema are provided as context to the model.
 
 Given a list of tools and a set of instructions, a model can request to call one or more tools with specific inputs.
-Once the chosen tools are invoked, the results can be passed back to the model so that it can complete whatever task
-it's performing.
-
-#### Tool inputs
-
-A tool can take arbitrary arguments as input. At runtime, these arguments can be passed in either:
-
-1. As just the raw arguments.
-2. As a `ToolCall`, which contains the arguments along with other metadata like the tool call ID.
+Typical usage may look like the following:
 
 ```ts
-const tool = ...
+// Define a list of tools
+const tools = [...];
 const llmWithTools = llm.bindTools([tool]);
-const aiMessage = await llmWithTools.invoke("do xyz...");
 
+const aiMessage = await llmWithTools.invoke("do xyz...");
 // AIMessage(tool_calls=[ToolCall(...), ...], ...)
+```
 
-const toolCall = aiMessage.tool_calls[0];
+The `AIMessage` returned from the model MAY have `tool_calls` associated with it.
+Read [this guide](/docs/concepts/#aimessage) for more information on what the response type may look like.
 
-// ToolCall(args={...}, id=..., ...)
+Once the tools are chosen, you will usually want to invoke them and then pass the results back to the model so that it can complete whatever task
+it's performing.
 
-// 1. pass in args directly
-await tool.invoke(toolCall.args);
+There are generally two different ways to invoke the tool and pass back the response:
 
-// 2. pass in the whole ToolCall
-await tool.invoke(toolCall);
-```
+#### Invoke with just the arguments
 
-A tool also has access to the `RunnableConfig` that's passed into whatever chain the tool is a part of. This allows you to write tool logic that can be parameterized by the chain config.
+When you invoke a tool with just the arguments, you will get back the raw tool output (usually a string).
+Here's what this looks like:
 
 ```ts
-const config = { configurable: { tool_param_foo: ... }};
-await tool.invoke(toolCall, config);
+import { ToolMessage } from "@langchain/core/messages";
+
+const toolCall = aiMessage.tool_calls[0]; // ToolCall(args={...}, id=..., ...)
+const toolOutput = await tool.invoke(toolCall.args);
+const toolMessage = new ToolMessage({
+  content: toolOutput,
+  name: toolCall.name,
+  tool_call_id: toolCall.id,
+});
 ```
 
-See the how-to guide for [passing in configs here](/docs/how_to/tool_configure/).
-
-#### Tool outputs
-
-The format of a tool's output depends on the format of the input. If a tool is called:
-
-1. With a dict of its arguments then it will produce an arbitrary output that we assume can be passed to a model as the `ToolMessage.content` field,
-2. A `ToolCall` then it will produce a `ToolMessage(content=..., ...)` where the tool output has already been assigned to the `ToolMessage.content` field.
+Note that the `content` field will generally be passed back to the model.
+If you do not want the raw tool response to be passed to the model, but you still want to keep it around,
+you can transform the tool output but also pass it as an artifact (read more about [`ToolMessage.artifact` here](/docs/concepts/#toolmessage))
 
 ```ts
-// 1. pass in args directly
-
-tool.invoke(toolCall.args);
-// -> "tool result foobar..."
-
-// 2. pass in the whole ToolCall
-
-await tool.invoke(toolCall);
-// -> ToolMessage(content="tool result foobar...", tool_call_id=..., name="tool_name")
+// Same code as above
+const responseForModel = someTransformation(response);
+const toolMessage = new ToolMessage({
+  content: responseForModel,
+  tool_call_id: toolCall.id,
+  name: toolCall.name,
+  artifact: response,
+});
 ```
 
-A tool can also be defined to include an artifact when invoked with a `ToolCall`. An artifact is some element of the
-tool's execution which is useful to return but shouldn't be sent to the model. The artifact can _only_ be returned
-when the tool input is a `ToolCall`:
+#### Invoke with `ToolCall`
+
+The other way to invoke a tool is to call it with the full `ToolCall` that was generated by the model.
+When you do this, the tool will return a `ToolMessage`.
+The benefits of this are that you don't have to write the logic yourself to transform the tool output into a ToolMessage.
+Here's what this looks like:
 
 ```ts
-await toolWithArtifact.invoke(toolCall);
-// -> ToolMessage(content="tool result foobar...", tool_call_id=..., name="tool_name", artifact=...).
+const toolCall = aiMessage.tool_calls[0];
+const toolMessage = await tool.invoke(toolCall);
 ```
 
-Learn about [`ToolMessage.artifact` here](/docs/concepts/#toolmessage) and about [defining tools that return artifacts here](/docs/how_to/tool_artifacts/).
+If you are invoking the tool this way and want to include an [artifact](/docs/concepts/#toolmessage) for the `ToolMessage`, you will need to have the tool return a tuple
+with two items: the `content` and the `artifact`.
+Read more about [defining tools that return artifacts here](/docs/how_to/tool_artifacts/).
 
 #### Best practices