Remove deno dependency in CI

.github/workflows/ci.yml

@@ -163,16 +163,10 @@ jobs:
           enable-cache: true
           cache-suffix: ${{ matrix.install.name }}
 
-      - uses: denoland/setup-deno@v2
-        with:
-          deno-version: v2.5.x
-
       - run: mkdir .coverage
 
       - run: uv sync --only-dev
 
-      - run: uv run mcp-run-python example --deps=numpy
-
       - name: cache HuggingFace models
         uses: actions/cache@v4
         with:
@@ -214,16 +208,10 @@ jobs:
           enable-cache: true
           cache-suffix: lowest-versions
 
-      - uses: denoland/setup-deno@v2
-        with:
-          deno-version: v2.5.x
-
       - run: mkdir .coverage
 
       - run: uv sync --group dev
 
-      - run: uv run mcp-run-python example --deps=numpy
-
       - name: cache HuggingFace models
         uses: actions/cache@v4
         with:

.github/workflows/claude.yml

@@ -39,10 +39,6 @@ jobs:
           enable-cache: true
           cache-suffix: claude-code
 
-      - uses: denoland/setup-deno@v2
-        with:
-          deno-version: v2.5.x
-
       - run: uv tool install pre-commit
 
       - run: make install

docs/contributing.md

@@ -9,26 +9,17 @@ git clone [email protected]:<your username>/pydantic-ai.git
 cd pydantic-ai
 ```
 
-Install `uv` (version 0.4.30 or later), `pre-commit` and `deno`:
+Install `uv` (version 0.4.30 or later) and `pre-commit`:
 
 - [`uv` install docs](https://docs.astral.sh/uv/getting-started/installation/)
 - [`pre-commit` install docs](https://pre-commit.com/#install)
-- [`deno` install docs](https://docs.deno.com/runtime/getting_started/installation/)
 
 To install `pre-commit` you can run the following command:
 
 ```bash
 uv tool install pre-commit
 ```
 
-For `deno`, you can run the following, or check
-[their documentation](https://docs.deno.com/runtime/getting_started/installation/) for alternative
-installation methods:
-
-```bash
-curl -fsSL https://deno.land/install.sh | sh
-```
-
 Install `pydantic-ai`, all dependencies and pre-commit hooks
 
 ```bash

docs/mcp/client.md

@@ -134,26 +134,22 @@ _(This example is complete, it can be run "as is" — you'll need to add `asynci
 
 MCP also offers [stdio transport](https://spec.modelcontextprotocol.io/specification/2024-11-05/basic/transports/#stdio) where the server is run as a subprocess and communicates with the client over `stdin` and `stdout`. In this case, you'd use the [`MCPServerStdio`][pydantic_ai.mcp.MCPServerStdio] class.
 
-In this example [mcp-run-python](https://github.com/pydantic/mcp-run-python) is used as the MCP server.
+In this example we use a simple MCP server that provides weather tools.
 
 ```python {title="mcp_stdio_client.py"}
 from pydantic_ai import Agent
 from pydantic_ai.mcp import MCPServerStdio
 
-server = MCPServerStdio(  # (1)!
-    'uv', args=['run', 'mcp-run-python', 'stdio'], timeout=10
-)
+server = MCPServerStdio('python', args=['mcp_server.py'], timeout=10)
 agent = Agent('openai:gpt-5', toolsets=[server])
 
 
 async def main():
-    result = await agent.run('How many days between 2000-01-01 and 2025-03-18?')
+    result = await agent.run('What is the weather in Paris?')
     print(result.output)
-    #> There are 9,208 days between January 1, 2000, and March 18, 2025.
+    #> The weather in Paris is sunny and 26 degrees Celsius.
 ```
 
-1. See [MCP Run Python](https://github.com/pydantic/mcp-run-python) for more information.
-
 ## Loading MCP Servers from Configuration
 
 Instead of creating MCP server instances individually in code, you can load multiple servers from a JSON configuration file using [`load_mcp_servers()`][pydantic_ai.mcp.load_mcp_servers].
@@ -168,8 +164,12 @@ The configuration file should be a JSON file with an `mcpServers` object contain
 {
   "mcpServers": {
     "python-runner": {
-      "command": "uv",
-      "args": ["run", "mcp-run-python", "stdio"]
+        "command": "uv",
+        "args": ["run", "mcp-run-python", "stdio"]
+    },
+    "weather": {
+      "command": "python",
+      "args": ["mcp_server.py"]
     },
     "weather-api": {
       "url": "http://localhost:3001/sse"

docs/mcp/fastmcp-client.md

@@ -20,12 +20,12 @@ A `FastMCPToolset` can then be created from:
 
 - A FastMCP Server: `#!python FastMCPToolset(fastmcp.FastMCP('my_server'))`
 - A FastMCP Client: `#!python FastMCPToolset(fastmcp.Client(...))`
-- A FastMCP Transport: `#!python FastMCPToolset(fastmcp.StdioTransport(command='uvx', args=['mcp-run-python', 'stdio']))`
+- A FastMCP Transport: `#!python FastMCPToolset(fastmcp.StdioTransport(command='python', args=['mcp_server.py']))`
 - A Streamable HTTP URL: `#!python FastMCPToolset('http://localhost:8000/mcp')`
 - An HTTP SSE URL: `#!python FastMCPToolset('http://localhost:8000/sse')`
 - A Python Script: `#!python FastMCPToolset('my_server.py')`
 - A Node.js Script: `#!python FastMCPToolset('my_server.js')`
-- A JSON MCP Configuration: `#!python FastMCPToolset({'mcpServers': {'my_server': {'command': 'uvx', 'args': ['mcp-run-python', 'stdio']}}})`
+- A JSON MCP Configuration: `#!python FastMCPToolset({'mcpServers': {'my_server': {'command': 'python', 'args': ['mcp_server.py']}}})`
 
 If you already have a [FastMCP Server](https://gofastmcp.com/servers) in the same codebase as your Pydantic AI agent, you can create a `FastMCPToolset` directly from it and save agent a network round trip:
 
@@ -76,6 +76,10 @@ mcp_config = {
         'time_mcp_server': {
             'command': 'uvx',
             'args': ['mcp-run-python', 'stdio']
+        },
+        'weather_server': {
+            'command': 'python',
+            'args': ['mcp_server.py']
         }
     }
 }

pydantic_ai_slim/pydantic_ai/models/cerebras.py

@@ -16,7 +16,7 @@
     from openai import AsyncOpenAI
 
     from .openai import OpenAIChatModel, OpenAIChatModelSettings
-except ImportError as _import_error:  # pragma: no cover
+except ImportError as _import_error:
     raise ImportError(
         'Please install the `openai` package to use the Cerebras model, '
         'you can use the `cerebras` optional group — `pip install "pydantic-ai-slim[cerebras]"'

tests/example_modules/mcp_server.py

@@ -7,6 +7,12 @@
 mcp = FastMCP('Pydantic AI MCP Server')
 
 
+@mcp.tool()
+async def get_weather_forecast(location: str) -> str:
+    """Get the weather forecast for a location."""
+    return f'The weather in {location} is sunny and 26 degrees Celsius.'
+
+
 @mcp.tool()
 async def echo_deps(ctx: Context[ServerSessionT, LifespanContextT, RequestT]) -> dict[str, Any]:
     """Echo the run context.

tests/models/anthropic/conftest.py

@@ -18,7 +18,7 @@
     from pydantic_ai.models.anthropic import AnthropicModel
     from pydantic_ai.providers.anthropic import AnthropicProvider
 
-AnthropicModelFactory = Callable[..., AnthropicModel]
+    AnthropicModelFactory = Callable[..., AnthropicModel]
 
 
 # Model factory fixture for live API tests

tests/models/anthropic/test_output.py

@@ -12,7 +12,7 @@
 from __future__ import annotations as _annotations
 
 from collections.abc import Callable
-from typing import Annotated
+from typing import TYPE_CHECKING, Annotated
 
 import httpx
 import pytest
@@ -33,6 +33,11 @@
     from pydantic_ai.models.anthropic import AnthropicModel
     from pydantic_ai.providers.anthropic import AnthropicProvider
 
+if TYPE_CHECKING:
+    from pydantic_ai.models.anthropic import AnthropicModel
+
+    ANTHROPIC_MODEL_FIXTURE = Callable[..., AnthropicModel]
+
 from ..test_anthropic import completion_message
 
 pytestmark = [
@@ -231,9 +236,6 @@ async def verify_headers(request: httpx.Request):
     return verify_headers
 
 
-ANTHROPIC_MODEL_FIXTURE = Callable[..., AnthropicModel]
-
-
 # =============================================================================
 # Supported Model Tests (claude-sonnet-4-5)
 # =============================================================================

tests/models/test_bedrock.py

@@ -5,9 +5,7 @@
 from typing import Any
 
 import pytest
-from botocore.exceptions import ClientError
 from inline_snapshot import snapshot
-from mypy_boto3_bedrock_runtime.type_defs import MessageUnionTypeDef, SystemContentBlockTypeDef, ToolTypeDef
 from typing_extensions import TypedDict
 
 from pydantic_ai import (
@@ -49,6 +47,9 @@
 from ..conftest import IsDatetime, IsInstance, IsStr, try_import
 
 with try_import() as imports_successful:
+    from botocore.exceptions import ClientError
+    from mypy_boto3_bedrock_runtime.type_defs import MessageUnionTypeDef, SystemContentBlockTypeDef, ToolTypeDef
+
     from pydantic_ai.models.bedrock import BedrockConverseModel, BedrockModelName, BedrockModelSettings
     from pydantic_ai.models.openai import OpenAIResponsesModel, OpenAIResponsesModelSettings
     from pydantic_ai.providers.bedrock import BedrockProvider

tests/models/test_google.py

@@ -94,6 +94,12 @@
     from pydantic_ai.providers.google import GoogleProvider
     from pydantic_ai.providers.openai import OpenAIProvider
 
+if not imports_successful():
+    # Define placeholder errors module so parametrize decorators can be parsed
+    from types import SimpleNamespace
+
+    errors = SimpleNamespace(ServerError=Exception, ClientError=Exception, APIError=Exception)
+
 pytestmark = [
     pytest.mark.skipif(not imports_successful(), reason='google-genai not installed'),
     pytest.mark.anyio,
@@ -4660,7 +4666,7 @@ async def test_google_api_errors_are_handled(
     allow_model_requests: None,
     google_provider: GoogleProvider,
     mocker: MockerFixture,
-    error_class: type[errors.APIError],
+    error_class: Any,
     error_response: dict[str, Any],
     expected_status: int,
 ):

tests/models/test_huggingface.py

@@ -8,25 +8,7 @@
 from typing import Any, Literal, cast
 from unittest.mock import Mock
 
-import aiohttp
 import pytest
-from huggingface_hub import (
-    AsyncInferenceClient,
-    ChatCompletionInputMessage,
-    ChatCompletionOutput,
-    ChatCompletionOutputComplete,
-    ChatCompletionOutputFunctionDefinition,
-    ChatCompletionOutputMessage,
-    ChatCompletionOutputToolCall,
-    ChatCompletionOutputUsage,
-    ChatCompletionStreamOutput,
-    ChatCompletionStreamOutputChoice,
-    ChatCompletionStreamOutputDelta,
-    ChatCompletionStreamOutputDeltaToolCall,
-    ChatCompletionStreamOutputFunction,
-    ChatCompletionStreamOutputUsage,
-)
-from huggingface_hub.errors import HfHubHTTPError
 from inline_snapshot import snapshot
 from typing_extensions import TypedDict
 
@@ -50,8 +32,6 @@
     VideoUrl,
 )
 from pydantic_ai.exceptions import ModelHTTPError
-from pydantic_ai.models.huggingface import HuggingFaceModel
-from pydantic_ai.providers.huggingface import HuggingFaceProvider
 from pydantic_ai.result import RunUsage
 from pydantic_ai.run import AgentRunResult, AgentRunResultEvent
 from pydantic_ai.settings import ModelSettings
@@ -62,10 +42,30 @@
 from .mock_async_stream import MockAsyncStream
 
 with try_import() as imports_successful:
-    pass
+    import aiohttp
+    from huggingface_hub import (
+        AsyncInferenceClient,
+        ChatCompletionInputMessage,
+        ChatCompletionOutput,
+        ChatCompletionOutputComplete,
+        ChatCompletionOutputFunctionDefinition,
+        ChatCompletionOutputMessage,
+        ChatCompletionOutputToolCall,
+        ChatCompletionOutputUsage,
+        ChatCompletionStreamOutput,
+        ChatCompletionStreamOutputChoice,
+        ChatCompletionStreamOutputDelta,
+        ChatCompletionStreamOutputDeltaToolCall,
+        ChatCompletionStreamOutputFunction,
+        ChatCompletionStreamOutputUsage,
+    )
+    from huggingface_hub.errors import HfHubHTTPError
+
+    from pydantic_ai.models.huggingface import HuggingFaceModel
+    from pydantic_ai.providers.huggingface import HuggingFaceProvider
 
-MockChatCompletion = ChatCompletionOutput | Exception
-MockStreamEvent = ChatCompletionStreamOutput | Exception
+    MockChatCompletion = ChatCompletionOutput | Exception
+    MockStreamEvent = ChatCompletionStreamOutput | Exception
 
 pytestmark = [
     pytest.mark.skipif(not imports_successful(), reason='huggingface_hub not installed'),

tests/models/test_model_names.py

@@ -25,6 +25,12 @@
     from pydantic_ai.providers.grok import GrokModelName
     from pydantic_ai.providers.moonshotai import MoonshotAIModelName
 
+if not imports_successful():
+    # Define placeholders so the module can be loaded for test collection
+    AnthropicModelName = BedrockModelName = CohereModelName = GoogleModelName = None
+    GroqModelName = HuggingFaceModelName = MistralModelName = OpenAIModelName = None
+    DeepSeekModelName = GrokModelName = MoonshotAIModelName = None
+
 pytestmark = [
     pytest.mark.skipif(not imports_successful(), reason='some model package was not installed'),
     pytest.mark.vcr,

tests/models/test_openai_responses.py

@@ -45,10 +45,6 @@
     BuiltinToolResultEvent,  # pyright: ignore[reportDeprecated]
 )
 from pydantic_ai.models import ModelRequestParameters
-from pydantic_ai.models.openai import (
-    OpenAIResponsesModelSettings,
-    _resolve_openai_image_generation_size,  # pyright: ignore[reportPrivateUsage]
-)
 from pydantic_ai.output import NativeOutput, PromptedOutput, TextOutput, ToolOutput
 from pydantic_ai.profiles.openai import openai_model_profile
 from pydantic_ai.tools import ToolDefinition
@@ -68,7 +64,11 @@
     from openai.types.responses.response_usage import ResponseUsage
 
     from pydantic_ai.models.anthropic import AnthropicModel, AnthropicModelSettings
-    from pydantic_ai.models.openai import OpenAIResponsesModel, OpenAIResponsesModelSettings
+    from pydantic_ai.models.openai import (
+        OpenAIResponsesModel,
+        OpenAIResponsesModelSettings,
+        _resolve_openai_image_generation_size,  # pyright: ignore[reportPrivateUsage]
+    )
     from pydantic_ai.providers.anthropic import AnthropicProvider
     from pydantic_ai.providers.openai import OpenAIProvider
 

tests/profiles/test_anthropic.py

@@ -22,12 +22,11 @@
 from inline_snapshot import snapshot
 from pydantic import BaseModel, Field
 
-from pydantic_ai.providers.anthropic import AnthropicJsonSchemaTransformer
-
 from ..conftest import try_import
 
 with try_import() as imports_successful:
     from pydantic_ai.profiles.anthropic import anthropic_model_profile
+    from pydantic_ai.providers.anthropic import AnthropicJsonSchemaTransformer
 
 pytestmark = [
     pytest.mark.skipif(not imports_successful(), reason='anthropic not installed'),

tests/providers/test_bedrock.py

@@ -19,6 +19,8 @@
     from pydantic_ai.models.bedrock import LatestBedrockModelNames
     from pydantic_ai.providers.bedrock import BEDROCK_GEO_PREFIXES, BedrockModelProfile, BedrockProvider
 
+if not imports_successful():
+    BEDROCK_GEO_PREFIXES: tuple[str, ...] = ()  # type: ignore[no-redef]
 
 pytestmark = pytest.mark.skipif(not imports_successful(), reason='bedrock not installed')