✨ Fallback to a Completions API when Sampling is not available (#1145)

2026-08-22 13:34:17 +02:00 · 2025-08-21 06:49:29 -05:00 · 2025-08-21 06:49:29 -05:00 · d32a2b953e
commit d32a2b953e
parent 76eb6dda25
18 changed files with 652 additions and 50 deletions
--- a/.github/workflows/run-static.yml
+++ b/.github/workflows/run-static.yml
@ -39,6 +39,7 @@ jobs:

      - name: Install dependencies
        run: uv sync
+
      - name: Check lockfile is up to date
        run: |
          if ! uv lock --check; then
--- a/docs/clients/sampling.mdx
+++ b/docs/clients/sampling.mdx
@ -150,3 +150,52 @@ client = Client(
    sampling_handler=basic_sampling_handler
 )
 ```
+
+## Sampling fallback
+
+Client support for sampling is optional, if the client does not support sampling, the server will report an error indicating
+that the client does not support sampling.
+
+A `sampling_handler` can also be provided to the FastMCP server, which will be used to handle sampling requests if the client
+does not support sampling. This sampling handler bypasses the client and sends sampling requests directly to the LLM provider.
+
+Sampling handlers can be implemented using any LLM provider but a sample implementation for OpenAI is provided as a Contrib
+module. Sampling lacks the full capabilities of typical LLM completions. For this reason, the OpenAI sampling handler, pointed at
+a third-party provider's OpenAI-compatible API, is often sufficient to implement a sampling handler.
+
+```python
+import asyncio
+import os
+
+from mcp.types import ContentBlock
+from openai import OpenAI
+
+from fastmcp import FastMCP
+from fastmcp.experimental.sampling.handlers.openai import OpenAISamplingHandler
+from fastmcp.server.context import Context
+
+
+async def async_main():
+    server = FastMCP(
+        name="OpenAI Sampling Fallback Example",
+        sampling_handler=OpenAISamplingHandler(
+            default_model="gpt-4o-mini",
+            client=OpenAI(
+                api_key=os.getenv("API_KEY"),
+                base_url=os.getenv("BASE_URL"),
+            ),
+        ),
+    )
+
+    @server.tool
+    async def test_sample_fallback(ctx: Context) -> ContentBlock:
+        return await ctx.sample(
+            messages=["hello world!"],
+        )
+
+    await server.run_http_async()
+
+
+if __name__ == "__main__":
+    asyncio.run(async_main())
+```
--- a/examples/sampling_fallback.py
+++ b/examples/sampling_fallback.py
@ -0,0 +1,34 @@
+import asyncio
+import os
+
+from mcp.types import ContentBlock
+from openai import OpenAI
+
+from fastmcp import FastMCP
+from fastmcp.experimental.sampling.handlers.openai import OpenAISamplingHandler
+from fastmcp.server.context import Context
+
+
+async def async_main():
+    server = FastMCP(
+        name="OpenAI Sampling Fallback Example",
+        sampling_handler=OpenAISamplingHandler(
+            default_model=os.getenv("MODEL") or "gpt-4o-mini",  # pyright: ignore[reportArgumentType]
+            client=OpenAI(
+                api_key=os.getenv("API_KEY"),
+                base_url=os.getenv("BASE_URL"),
+            ),
+        ),
+    )
+
+    @server.tool
+    async def test_sample_fallback(ctx: Context) -> ContentBlock:
+        return await ctx.sample(
+            messages=["hello world!"],
+        )
+
+    await server.run_http_async()
+
+
+if __name__ == "__main__":
+    asyncio.run(async_main())
--- a/pyproject.toml
+++ b/pyproject.toml
@ -15,6 +15,7 @@ dependencies = [
    "pydantic[email]>=2.11.7",
    "pyperclip>=1.9.0",
    "openapi-core>=0.19.5",
+    "openai>=1.95.1",
 ]
 requires-python = ">=3.10"
 readme = "README.md"
--- a/src/fastmcp/client/client.py
+++ b/src/fastmcp/client/client.py
@ -30,7 +30,11 @@ from fastmcp.client.roots import (
    RootsList,
    create_roots_callback,
 )
-from fastmcp.client.sampling import SamplingHandler, create_sampling_callback
+from fastmcp.client.sampling import (
+    ClientSamplingHandler,
+    SamplingHandler,
+    create_sampling_callback,
+)
 from fastmcp.exceptions import ToolError
 from fastmcp.mcp_config import MCPConfig
 from fastmcp.server import FastMCP
@ -60,6 +64,7 @@ __all__ = [
    "RootsList",
    "LogHandler",
    "MessageHandler",
+    "ClientSamplingHandler",
    "SamplingHandler",
    "ElicitationHandler",
    "ProgressHandler",
@ -208,7 +213,7 @@ class Client(Generic[ClientTransportT]):
            | str
        ),
        roots: RootsList | RootsHandler | None = None,
-        sampling_handler: SamplingHandler | None = None,
+        sampling_handler: ClientSamplingHandler | None = None,
        elicitation_handler: ElicitationHandler | None = None,
        log_handler: LogHandler | None = None,
        message_handler: MessageHandlerT | MessageHandler | None = None,
@ -292,7 +297,7 @@ class Client(Generic[ClientTransportT]):
        """Set the roots for the client. This does not automatically call `send_roots_list_changed`."""
        self._session_kwargs["list_roots_callback"] = create_roots_callback(roots)

-    def set_sampling_callback(self, sampling_callback: SamplingHandler) -> None:
+    def set_sampling_callback(self, sampling_callback: ClientSamplingHandler) -> None:
        """Set the sampling callback for the client."""
        self._session_kwargs["sampling_callback"] = create_sampling_callback(
            sampling_callback
--- a/src/fastmcp/client/sampling.py
+++ b/src/fastmcp/client/sampling.py
@ -3,16 +3,18 @@ from collections.abc import Awaitable, Callable
 from typing import TypeAlias

 import mcp.types
-from mcp import ClientSession, CreateMessageResult
-from mcp.client.session import SamplingFnT
+from mcp import CreateMessageResult
+from mcp.client.session import ClientSession, SamplingFnT
 from mcp.shared.context import LifespanContextT, RequestContext
 from mcp.types import CreateMessageRequestParams as SamplingParams
 from mcp.types import SamplingMessage

+from fastmcp.server.sampling.handler import ServerSamplingHandler
+
 __all__ = ["SamplingMessage", "SamplingParams", "SamplingHandler"]


-SamplingHandler: TypeAlias = Callable[
+ClientSamplingHandler: TypeAlias = Callable[
    [
        list[SamplingMessage],
        SamplingParams,
@ -21,8 +23,14 @@ SamplingHandler: TypeAlias = Callable[
    str | CreateMessageResult | Awaitable[str | CreateMessageResult],
 ]

+SamplingHandler: TypeAlias = (
+    ClientSamplingHandler[LifespanContextT] | ServerSamplingHandler[LifespanContextT]
+)

-def create_sampling_callback(sampling_handler: SamplingHandler) -> SamplingFnT:
+
+def create_sampling_callback(
+    sampling_handler: ClientSamplingHandler[LifespanContextT],
+) -> SamplingFnT:
    async def _sampling_handler(
        context: RequestContext[ClientSession, LifespanContextT],
        params: SamplingParams,
--- a/src/fastmcp/experimental/sampling/init.py
+++ b/src/fastmcp/experimental/sampling/init.py
--- a/src/fastmcp/experimental/sampling/handlers/init.py
+++ b/src/fastmcp/experimental/sampling/handlers/init.py
@ -0,0 +1,3 @@
+from .openai import OpenAISamplingHandler
+
+__all__ = ["OpenAISamplingHandler"]
--- a/src/fastmcp/experimental/sampling/handlers/base.py
+++ b/src/fastmcp/experimental/sampling/handlers/base.py
@ -0,0 +1,21 @@
+from abc import ABC, abstractmethod
+from collections.abc import Awaitable
+
+from mcp import ClientSession, CreateMessageResult
+from mcp.server.session import ServerSession
+from mcp.shared.context import LifespanContextT, RequestContext
+from mcp.types import CreateMessageRequestParams as SamplingParams
+from mcp.types import (
+    SamplingMessage,
+)
+
+
+class BaseLLMSamplingHandler(ABC):
+    @abstractmethod
+    def __call__(
+        self,
+        messages: list[SamplingMessage],
+        params: SamplingParams,
+        context: RequestContext[ServerSession, LifespanContextT]
+        | RequestContext[ClientSession, LifespanContextT],
+    ) -> str | CreateMessageResult | Awaitable[str | CreateMessageResult]: ...
--- a/src/fastmcp/experimental/sampling/handlers/openai.py
+++ b/src/fastmcp/experimental/sampling/handlers/openai.py
@ -0,0 +1,163 @@
+from collections.abc import Iterator, Sequence
+from typing import get_args
+
+from mcp import ClientSession, ServerSession
+from mcp.shared.context import LifespanContextT, RequestContext
+from mcp.types import CreateMessageRequestParams as SamplingParams
+from mcp.types import (
+    CreateMessageResult,
+    ModelPreferences,
+    SamplingMessage,
+    TextContent,
+)
+from openai import NOT_GIVEN, OpenAI
+from openai.types.chat import (
+    ChatCompletion,
+    ChatCompletionAssistantMessageParam,
+    ChatCompletionMessageParam,
+    ChatCompletionSystemMessageParam,
+    ChatCompletionUserMessageParam,
+)
+from openai.types.shared.chat_model import ChatModel
+from typing_extensions import override
+
+from fastmcp.experimental.sampling.handlers.base import BaseLLMSamplingHandler
+
+
+class OpenAISamplingHandler(BaseLLMSamplingHandler):
+    def __init__(self, default_model: ChatModel, client: OpenAI | None = None):
+        self.client: OpenAI = client or OpenAI()
+        self.default_model: ChatModel = default_model
+
+    @override
+    async def __call__(
+        self,
+        messages: list[SamplingMessage],
+        params: SamplingParams,
+        context: RequestContext[ServerSession, LifespanContextT]
+        | RequestContext[ClientSession, LifespanContextT],
+    ) -> CreateMessageResult:
+        openai_messages: list[ChatCompletionMessageParam] = (
+            self._convert_to_openai_messages(
+                system_prompt=params.systemPrompt,
+                messages=messages,
+            )
+        )
+
+        model: ChatModel = self._select_model_from_preferences(params.modelPreferences)
+
+        response = self.client.chat.completions.create(
+            model=model,
+            messages=openai_messages,
+            temperature=params.temperature or NOT_GIVEN,
+            max_tokens=params.maxTokens,
+            stop=params.stopSequences or NOT_GIVEN,
+        )
+
+        return self._chat_completion_to_create_message_result(response)
+
+    @staticmethod
+    def _iter_models_from_preferences(
+        model_preferences: ModelPreferences | str | list[str] | None,
+    ) -> Iterator[str]:
+        if model_preferences is None:
+            return
+
+        if isinstance(model_preferences, str) and model_preferences in get_args(
+            ChatModel
+        ):
+            yield model_preferences
+
+        if isinstance(model_preferences, list):
+            yield from model_preferences
+
+        if isinstance(model_preferences, ModelPreferences):
+            if not (hints := model_preferences.hints):
+                return
+
+            for hint in hints:
+                if not (name := hint.name):
+                    continue
+
+                yield name
+
+    @staticmethod
+    def _convert_to_openai_messages(
+        system_prompt: str | None, messages: Sequence[SamplingMessage]
+    ) -> list[ChatCompletionMessageParam]:
+        openai_messages: list[ChatCompletionMessageParam] = []
+
+        if system_prompt:
+            openai_messages.append(
+                ChatCompletionSystemMessageParam(
+                    role="system",
+                    content=system_prompt,
+                )
+            )
+
+        if isinstance(messages, str):
+            openai_messages.append(
+                ChatCompletionUserMessageParam(
+                    role="user",
+                    content=messages,
+                )
+            )
+
+        if isinstance(messages, list):
+            for message in messages:
+                if isinstance(message, str):
+                    openai_messages.append(
+                        ChatCompletionUserMessageParam(
+                            role="user",
+                            content=message,
+                        )
+                    )
+                    continue
+
+                if not isinstance(message.content, TextContent):
+                    raise ValueError("Only text content is supported")
+
+                if message.role == "user":
+                    openai_messages.append(
+                        ChatCompletionUserMessageParam(
+                            role="user",
+                            content=message.content.text,
+                        )
+                    )
+                else:
+                    openai_messages.append(
+                        ChatCompletionAssistantMessageParam(
+                            role="assistant",
+                            content=message.content.text,
+                        )
+                    )
+
+        return openai_messages
+
+    @staticmethod
+    def _chat_completion_to_create_message_result(
+        chat_completion: ChatCompletion,
+    ) -> CreateMessageResult:
+        if len(chat_completion.choices) == 0:
+            raise ValueError("No response for completion")
+
+        first_choice = chat_completion.choices[0]
+
+        if content := first_choice.message.content:
+            return CreateMessageResult(
+                content=TextContent(type="text", text=content),
+                role="assistant",
+                model=chat_completion.model,
+            )
+
+        raise ValueError("No content in response from completion")
+
+    def _select_model_from_preferences(
+        self, model_preferences: ModelPreferences | str | list[str] | None
+    ) -> ChatModel:
+        for model_option in self._iter_models_from_preferences(model_preferences):
+            if model_option in get_args(ChatModel):
+                chosen_model: ChatModel = model_option  # pyright: ignore[reportAssignmentType]
+                return chosen_model
+
+        return self.default_model
--- a/src/fastmcp/server/context.py
+++ b/src/fastmcp/server/context.py
@ -2,6 +2,7 @@ from __future__ import annotations

 import asyncio
 import copy
+import inspect
 import warnings
 import weakref
 from collections.abc import Generator, Mapping
@ -16,15 +17,18 @@ from mcp.server.lowlevel.helper_types import ReadResourceContents
 from mcp.server.lowlevel.server import request_ctx
 from mcp.shared.context import RequestContext
 from mcp.types import (
+    ClientCapabilities,
    ContentBlock,
    CreateMessageResult,
    IncludeContext,
    ModelHint,
    ModelPreferences,
    Root,
+    SamplingCapability,
    SamplingMessage,
    TextContent,
 )
+from mcp.types import CreateMessageRequestParams as SamplingParams
 from pydantic.networks import AnyUrl
 from starlette.requests import Request

@ -386,13 +390,50 @@ class Context:
                for m in messages
            ]

+        should_fallback = (
+            self.fastmcp.sampling_handler_behavior == "fallback"
+            and not self.session.check_client_capability(
+                capability=ClientCapabilities(sampling=SamplingCapability())
+            )
+        )
+
+        if self.fastmcp.sampling_handler_behavior == "always" or should_fallback:
+            if self.fastmcp.sampling_handler is None:
+                raise ValueError("Client does not support sampling")
+
+            create_message_result = self.fastmcp.sampling_handler(
+                sampling_messages,
+                SamplingParams(
+                    systemPrompt=system_prompt,
+                    messages=sampling_messages,
+                    temperature=temperature,
+                    maxTokens=max_tokens,
+                    modelPreferences=_parse_model_preferences(model_preferences),
+                ),
+                self.request_context,
+            )
+
+            if inspect.isawaitable(create_message_result):
+                create_message_result = await create_message_result
+
+            if isinstance(create_message_result, str):
+                return TextContent(text=create_message_result, type="text")
+
+            if isinstance(create_message_result, CreateMessageResult):
+                return create_message_result.content
+
+            else:
+                raise ValueError(
+                    f"Unexpected sampling handler result: {create_message_result}"
+                )
+
        result: CreateMessageResult = await self.session.create_message(
            messages=sampling_messages,
            system_prompt=system_prompt,
            include_context=include_context,
            temperature=temperature,
            max_tokens=max_tokens,
-            model_preferences=self._parse_model_preferences(model_preferences),
+            model_preferences=_parse_model_preferences(model_preferences),
            related_request_id=self.request_id,
        )

@ -592,44 +633,43 @@ class Context:
                # Don't let notification failures break the request
                pass

-    def _parse_model_preferences(
-        self, model_preferences: ModelPreferences | str | list[str] | None
-    ) -> ModelPreferences | None:
-        """
-        Validates and converts user input for model_preferences into a ModelPreferences object.

-        Args:
-            model_preferences (ModelPreferences | str | list[str] | None):
-                The model preferences to use. Accepts:
-                - ModelPreferences (returns as-is)
-                - str (single model hint)
-                - list[str] (multiple model hints)
-                - None (no preferences)
+def _parse_model_preferences(
+    model_preferences: ModelPreferences | str | list[str] | None,
+) -> ModelPreferences | None:
+    """
+    Validates and converts user input for model_preferences into a ModelPreferences object.

-        Returns:
-            ModelPreferences | None: The parsed ModelPreferences object, or None if not provided.
+    Args:
+        model_preferences (ModelPreferences | str | list[str] | None):
+            The model preferences to use. Accepts:
+            - ModelPreferences (returns as-is)
+            - str (single model hint)
+            - list[str] (multiple model hints)
+            - None (no preferences)

-        Raises:
-            ValueError: If the input is not a supported type or contains invalid values.
-        """
-        if model_preferences is None:
-            return None
-        elif isinstance(model_preferences, ModelPreferences):
-            return model_preferences
-        elif isinstance(model_preferences, str):
-            # Single model hint
-            return ModelPreferences(hints=[ModelHint(name=model_preferences)])
-        elif isinstance(model_preferences, list):
-            # List of model hints (strings)
-            if not all(isinstance(h, str) for h in model_preferences):
-                raise ValueError(
-                    "All elements of model_preferences list must be"
-                    " strings (model name hints)."
-                )
-            return ModelPreferences(
-                hints=[ModelHint(name=h) for h in model_preferences]
-            )
-        else:
+    Returns:
+        ModelPreferences | None: The parsed ModelPreferences object, or None if not provided.
+
+    Raises:
+        ValueError: If the input is not a supported type or contains invalid values.
+    """
+    if model_preferences is None:
+        return None
+    elif isinstance(model_preferences, ModelPreferences):
+        return model_preferences
+    elif isinstance(model_preferences, str):
+        # Single model hint
+        return ModelPreferences(hints=[ModelHint(name=model_preferences)])
+    elif isinstance(model_preferences, list):
+        # List of model hints (strings)
+        if not all(isinstance(h, str) for h in model_preferences):
            raise ValueError(
-                "model_preferences must be one of: ModelPreferences, str, list[str], or None."
+                "All elements of model_preferences list must be"
+                " strings (model name hints)."
            )
+        return ModelPreferences(hints=[ModelHint(name=h) for h in model_preferences])
+    else:
+        raise ValueError(
+            "model_preferences must be one of: ModelPreferences, str, list[str], or None."
+        )
--- a/src/fastmcp/server/sampling/handler.py
+++ b/src/fastmcp/server/sampling/handler.py
@ -0,0 +1,19 @@
+from collections.abc import Awaitable, Callable
+from typing import TypeAlias
+
+from mcp import CreateMessageResult
+from mcp.server.session import ServerSession
+from mcp.shared.context import LifespanContextT, RequestContext
+from mcp.types import CreateMessageRequestParams as SamplingParams
+from mcp.types import (
+    SamplingMessage,
+)
+
+ServerSamplingHandler: TypeAlias = Callable[
+    [
+        list[SamplingMessage],
+        SamplingParams,
+        RequestContext[ServerSession, LifespanContextT],
+    ],
+    str | CreateMessageResult | Awaitable[str | CreateMessageResult],
+]
--- a/src/fastmcp/server/server.py
+++ b/src/fastmcp/server/server.py
@ -70,6 +70,7 @@ from fastmcp.utilities.types import NotSet, NotSetT

 if TYPE_CHECKING:
    from fastmcp.client import Client
+    from fastmcp.client.sampling import ServerSamplingHandler
    from fastmcp.client.transports import ClientTransport, ClientTransportT
    from fastmcp.experimental.server.openapi import FastMCPOpenAPI as FastMCPOpenAPINew
    from fastmcp.experimental.server.openapi.routing import (
@ -167,6 +168,8 @@ class FastMCP(Generic[LifespanResultT]):
        streamable_http_path: str | None = None,
        json_response: bool | None = None,
        stateless_http: bool | None = None,
+        sampling_handler: ServerSamplingHandler[LifespanResultT] | None = None,
+        sampling_handler_behavior: Literal["always", "fallback"] | None = None,
    ):
        self.resource_prefix_format: Literal["protocol", "path"] = (
            resource_prefix_format or fastmcp.settings.resource_prefix_format
@ -242,6 +245,9 @@ class FastMCP(Generic[LifespanResultT]):
            dependencies or fastmcp.settings.server_dependencies
        )  # TODO: Remove (deprecated in v2.11.4)

+        self.sampling_handler = sampling_handler
+        self.sampling_handler_behavior = sampling_handler_behavior or "fallback"
+
        self.include_fastmcp_meta = (
            include_fastmcp_meta
            if include_fastmcp_meta is not None
--- a/src/fastmcp/utilities/types.py
+++ b/src/fastmcp/utilities/types.py
@ -10,6 +10,7 @@ from pathlib import Path
 from types import EllipsisType, UnionType
 from typing import (
    Annotated,
+    Protocol,
    TypeAlias,
    TypeVar,
    Union,
@ -19,7 +20,7 @@ from typing import (
 )

 import mcp.types
-from mcp.types import Annotations
+from mcp.types import Annotations, ContentBlock, ModelPreferences, SamplingMessage
 from pydantic import AnyUrl, BaseModel, ConfigDict, Field, TypeAdapter, UrlConstraints

 T = TypeVar("T")
@ -407,3 +408,14 @@ def replace_type(type_, type_map: dict[type, type]):
        return Union[new_args]  # type: ignore # noqa: UP007
    else:
        return origin[new_args]
+
+
+class ContextSamplingFallbackProtocol(Protocol):
+    async def __call__(
+        self,
+        messages: str | list[str | SamplingMessage],
+        system_prompt: str | None = None,
+        temperature: float | None = None,
+        max_tokens: int | None = None,
+        model_preferences: ModelPreferences | str | list[str] | None = None,
+    ) -> ContentBlock: ...
--- a/tests/client/test_sampling.py
+++ b/tests/client/test_sampling.py
@ -1,4 +1,6 @@
 import json
+from typing import cast
+from unittest.mock import AsyncMock

 import pytest
 from mcp.types import TextContent
@ -89,8 +91,12 @@ async def test_sampling_with_messages(fastmcp_server: FastMCP):
        messages: list[SamplingMessage], params: SamplingParams, ctx: RequestContext
    ) -> str:
        assert len(messages) == 2
+
+        assert isinstance(messages[0].content, TextContent)
        assert messages[0].content.type == "text"
        assert messages[0].content.text == "Hello!"
+
+        assert isinstance(messages[1].content, TextContent)
        assert messages[1].content.type == "text"
        assert messages[1].content.text == "How can I assist you today?"
        return "I need to think."
@ -102,6 +108,26 @@ async def test_sampling_with_messages(fastmcp_server: FastMCP):
        assert result.data == "I need to think."


+async def test_sampling_with_fallback(fastmcp_server: FastMCP):
+    openai_sampling_handler = AsyncMock(return_value="But I need to think")
+
+    fastmcp_server = FastMCP(
+        sampling_handler=openai_sampling_handler,
+    )
+
+    @fastmcp_server.tool
+    async def sample_with_fallback(context: Context) -> str:
+        sampling_result = await context.sample("Do not think.")
+        return cast(TextContent, sampling_result).text
+
+    client = Client(fastmcp_server)
+
+    async with client:
+        call_tool_result = await client.call_tool("sample_with_fallback")
+
+    assert call_tool_result.data == "But I need to think"
+
+
 async def test_sampling_with_image(fastmcp_server: FastMCP):
    def sampling_handler(
        messages: list[SamplingMessage], params: SamplingParams, ctx: RequestContext
--- a/tests/experimental/sampling/test_openai_handler.py
+++ b/tests/experimental/sampling/test_openai_handler.py
@ -0,0 +1,100 @@
+from unittest.mock import MagicMock
+
+import pytest
+from mcp.types import (
+    CreateMessageResult,
+    ModelHint,
+    ModelPreferences,
+    SamplingMessage,
+    TextContent,
+)
+from openai import OpenAI
+from openai.types.chat import (
+    ChatCompletion,
+    ChatCompletionAssistantMessageParam,
+    ChatCompletionMessage,
+    ChatCompletionSystemMessageParam,
+    ChatCompletionUserMessageParam,
+)
+from openai.types.chat.chat_completion import Choice
+
+from fastmcp.experimental.sampling.handlers.openai import OpenAISamplingHandler
+
+
+def test_convert_sampling_messages_to_openai_messages():
+    msgs = OpenAISamplingHandler._convert_to_openai_messages(
+        system_prompt="sys",
+        messages=[
+            SamplingMessage(
+                role="user", content=TextContent(type="text", text="hello")
+            ),
+            SamplingMessage(
+                role="assistant", content=TextContent(type="text", text="ok")
+            ),
+        ],
+    )
+
+    assert msgs == [
+        ChatCompletionSystemMessageParam(content="sys", role="system"),
+        ChatCompletionUserMessageParam(content="hello", role="user"),
+        ChatCompletionAssistantMessageParam(content="ok", role="assistant"),
+    ]
+
+
+def test_convert_to_openai_messages_raises_on_non_text():
+    from fastmcp.utilities.types import Image
+
+    with pytest.raises(ValueError):
+        OpenAISamplingHandler._convert_to_openai_messages(
+            system_prompt=None,
+            messages=[
+                SamplingMessage(
+                    role="user",
+                    content=Image(data=b"abc").to_image_content(),
+                )
+            ],
+        )
+
+
+@pytest.mark.parametrize(
+    "prefs,expected",
+    [
+        ("gpt-4o-mini", "gpt-4o-mini"),
+        (ModelPreferences(hints=[ModelHint(name="gpt-4o-mini")]), "gpt-4o-mini"),
+        (["gpt-4o-mini", "other"], "gpt-4o-mini"),
+        (None, "fallback-model"),
+        (["unknown-model"], "fallback-model"),
+    ],
+)
+def test_select_model_from_preferences(prefs, expected):
+    mock_client = MagicMock(spec=OpenAI)
+    handler = OpenAISamplingHandler(default_model="fallback-model", client=mock_client)  # type: ignore[arg-type]
+    assert handler._select_model_from_preferences(prefs) == expected
+
+
+async def test_chat_completion_to_create_message_result():
+    mock_client = MagicMock(spec=OpenAI)
+    handler = OpenAISamplingHandler(default_model="fallback-model", client=mock_client)  # type: ignore[arg-type]
+    mock_client.chat.completions.create.return_value = ChatCompletion(
+        id="123",
+        created=123,
+        model="gpt-4o-mini",
+        object="chat.completion",
+        choices=[
+            Choice(
+                message=ChatCompletionMessage(
+                    content="HELPFUL CONTENT FROM A VERY SMART LLM", role="assistant"
+                ),
+                finish_reason="stop",
+                index=0,
+            )
+        ],
+    )
+    result: CreateMessageResult = handler._chat_completion_to_create_message_result(
+        chat_completion=mock_client.chat.completions.create.return_value
+    )
+    assert result == CreateMessageResult(
+        content=TextContent(type="text", text="HELPFUL CONTENT FROM A VERY SMART LLM"),
+        role="assistant",
+        model="gpt-4o-mini",
+    )
--- a/tests/server/test_context.py
+++ b/tests/server/test_context.py
@ -5,7 +5,7 @@ import pytest
 from mcp.types import ModelPreferences
 from starlette.requests import Request

-from fastmcp.server.context import Context
+from fastmcp.server.context import Context, _parse_model_preferences
 from fastmcp.server.server import FastMCP


@ -68,24 +68,24 @@ def context():

 class TestParseModelPreferences:
    def test_parse_model_preferences_string(self, context):
-        mp = context._parse_model_preferences("claude-3-sonnet")
+        mp = _parse_model_preferences("claude-3-sonnet")
        assert isinstance(mp, ModelPreferences)
        assert mp.hints is not None
        assert mp.hints[0].name == "claude-3-sonnet"

    def test_parse_model_preferences_list(self, context):
-        mp = context._parse_model_preferences(["claude-3-sonnet", "claude"])
+        mp = _parse_model_preferences(["claude-3-sonnet", "claude"])
        assert isinstance(mp, ModelPreferences)
        assert mp.hints is not None
        assert [h.name for h in mp.hints] == ["claude-3-sonnet", "claude"]

    def test_parse_model_preferences_object(self, context):
        obj = ModelPreferences(hints=[])
-        assert context._parse_model_preferences(obj) is obj
+        assert _parse_model_preferences(obj) is obj

    def test_parse_model_preferences_invalid_type(self, context):
        with pytest.raises(ValueError):
-            context._parse_model_preferences(123)
+            _parse_model_preferences(model_preferences=123)  # pyright: ignore[reportArgumentType]


 class TestSessionId:
--- a/uv.lock
+++ b/uv.lock
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 ]

+[[package]]
+name = "distro"
+version = "1.9.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/fc/f8/98eea607f65de6527f8a2e8885fc8015d3e6f5775df186e443e0964a11c3/distro-1.9.0.tar.gz", hash = "sha256:2fa77c6fd8940f116ee1d6b94a2f90b13b5ea8d019b98bc8bafdcabcdd9bdbed", size = 60722, upload-time = "2023-12-24T09:54:32.31Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/12/b3/231ffd4ab1fc9d679809f356cebee130ac7daa00d6d6f3206dd4fd137e9e/distro-1.9.0-py3-none-any.whl", hash = "sha256:7bffd925d65168f85027d8da9af6bddab658135b840670a223589bc0c8ef02b2", size = 20277, upload-time = "2023-12-24T09:54:30.421Z" },
+]
+
 [[package]]
 name = "dnspython"
 version = "2.7.0"
@ -533,6 +542,7 @@ dependencies = [
    { name = "exceptiongroup" },
    { name = "httpx" },
    { name = "mcp" },
+    { name = "openai" },
    { name = "openapi-core" },
    { name = "openapi-pydantic" },
    { name = "pydantic", extra = ["email"] },
@ -578,6 +588,7 @@ requires-dist = [
    { name = "exceptiongroup", specifier = ">=1.2.2" },
    { name = "httpx", specifier = ">=0.28.1" },
    { name = "mcp", specifier = ">=1.12.4,<2.0.0" },
+    { name = "openai", specifier = ">=1.95.1" },
    { name = "openapi-core", specifier = ">=0.19.5" },
    { name = "openapi-pydantic", specifier = ">=0.5.1" },
    { name = "pydantic", extras = ["email"], specifier = ">=2.11.7" },
@ -801,6 +812,78 @@ wheels = [
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 ]

+[[package]]
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