remove test and duplicate text

Author: dsfaccini

docs/output.md

@@ -237,7 +237,7 @@ RouterFailure(explanation='I am not equipped to provide travel information, such
 
 #### Text output
 
-If you provide an output function that takes a string, Pydantic AI will by default create an output tool like for any other output function. If instead you'd like the model to provide the string using plain text output, you can wrap the function in the [`TextOutput`][pydantic_ai.output.TextOutput] marker class. 
+If you provide an output function that takes a string, Pydantic AI will by default create an output tool like for any other output function. If instead you'd like the model to provide the string using plain text output, you can wrap the function in the [`TextOutput`][pydantic_ai.output.TextOutput] marker class.
 
 If desired, this marker class can be used alongside one or more [`ToolOutput`](#tool-output) marker classes (or unmarked types or functions) in a list provided to `output_type`.
 
@@ -262,8 +262,6 @@ print(result.output)
 
 _(This example is complete, it can be run "as is")_
 
-Like other output functions, text output functions can optionally take [`RunContext`][pydantic_ai.tools.RunContext] as the first argument, and can raise [`ModelRetry`][pydantic_ai.exceptions.ModelRetry] to ask the model to try again with modified arguments (or with a different output type).
-
 ### Output modes
 
 Pydantic AI implements three different methods to get a model to output structured data:

tests/test_agent.py

@@ -1167,24 +1167,6 @@ def call_tool(messages: list[ModelMessage], info: AgentInfo) -> ModelResponse:
     )
 
 
-def test_output_type_text_output_function_with_deps():
-    """Test that TextOutput functions can use RunContext with custom deps type."""
-
-    @dataclass
-    class Deps:
-        prefix: str
-
-    def add_prefix_and_split(ctx: RunContext[Deps], text: str) -> list[str]:
-        return f'{ctx.deps.prefix}: {text}'.split()
-
-    def return_text(messages: list[ModelMessage], info: AgentInfo) -> ModelResponse:
-        return ModelResponse(parts=[TextPart(content='Hello world')])
-
-    agent = Agent(FunctionModel(return_text), deps_type=Deps, output_type=TextOutput(add_prefix_and_split))
-    result = agent.run_sync('test prompt', deps=Deps(prefix='Response'))
-    assert result.output == snapshot(['Response:', 'Hello', 'world'])
-
-
 @pytest.mark.parametrize(
     'output_type',
     [[str, str], [str, TextOutput(upcase)], [TextOutput(upcase), TextOutput(upcase)]],