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Fix MCP client documentation inconsistencies #2357

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Fix MCP client documentation inconsistencies #2357

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18 changes: 9 additions & 9 deletions docs/mcp/client.md
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Original file line number Diff line number Diff line change
Expand Up @@ -16,7 +16,7 @@ pip/uv-add "pydantic-ai-slim[mcp]"

## Usage

Pydantic AI comes with two ways to connect to MCP servers:
Pydantic AI comes with three ways to connect to MCP servers:

- [`MCPServerStreamableHTTP`][pydantic_ai.mcp.MCPServerStreamableHTTP] which connects to an MCP server using the [Streamable HTTP](https://modelcontextprotocol.io/introduction#streamable-http) transport
- [`MCPServerSSE`][pydantic_ai.mcp.MCPServerSSE] which connects to an MCP server using the [HTTP SSE](https://spec.modelcontextprotocol.io/specification/2024-11-05/basic/transports/#http-with-sse) transport
Expand Down Expand Up @@ -62,9 +62,9 @@ agent = Agent('openai:gpt-4o', toolsets=[server]) # (2)!

async def main():
async with agent: # (3)!
result = await agent.run('How many days between 2000-01-01 and 2025-03-18?')
result = await agent.run('What is 25 + 17?')
print(result.output)
#> There are 9,208 days between January 1, 2000, and March 18, 2025.
#> The sum of 25 and 17 is 42.
```

1. Define the MCP server with the URL used to connect.
Expand All @@ -75,14 +75,14 @@ _(This example is complete, it can be run "as is" with Python 3.10+ — you'll n

**What's happening here?**

- The model is receiving the prompt "how many days between 2000-01-01 and 2025-03-18?"
- The model decides "Oh, I've got this `run_python_code` tool, that will be a good way to answer this question", and writes some python code to calculate the answer.
- The model is receiving the prompt "What is 25 + 17?"
- The model decides "Oh, I've got this `add` tool, that will be a good way to answer this question", and calls the add function with arguments 25 and 17.
- The model returns a tool call
- Pydantic AI sends the tool call to the MCP server using the SSE transport
- The model is called again with the return value of running the code
- Pydantic AI sends the tool call to the MCP server using the Streamable HTTP transport
- The model is called again with the return value from the add function (42)
- The model returns the final answer

You can visualise this clearly, and even see the code that's run by adding three lines of code to instrument the example with [logfire](https://logfire.pydantic.dev/docs):
You can visualise this clearly, and even see the tool call by adding three lines of code to instrument the example with [logfire](https://logfire.pydantic.dev/docs):

```python {title="mcp_sse_client_logfire.py" test="skip"}
import logfire
Expand All @@ -93,7 +93,7 @@ logfire.instrument_pydantic_ai()

Will display as follows:

![Logfire run python code](../img/logfire-run-python-code.png)
![Logfire MCP tool call](../img/logfire-run-python-code.png)

### SSE Client

Expand Down
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