Merge branch 'main' into add-cerebras-support

Author: sebastiand-cerebras

.github/workflows/ci.yml

@@ -157,6 +157,9 @@ jobs:
     env:
       CI: true
       COVERAGE_PROCESS_START: ./pyproject.toml
+      # We only run the llama_cpp tests on the latest Python as they have been regularly failing in CI with `Fatal Python error: Illegal instruction`:
+      # https://github.com/pydantic/pydantic-ai/actions/runs/19547773220/job/55970947389
+      RUN_LLAMA_CPP_TESTS: ${{ matrix.python-version == '3.13' && matrix.install.name == 'all-extras' }}
     steps:
       - uses: actions/checkout@v4
 
@@ -207,6 +210,7 @@ jobs:
     env:
       CI: true
       COVERAGE_PROCESS_START: ./pyproject.toml
+      RUN_LLAMA_CPP_TESTS: false
     steps:
       - uses: actions/checkout@v4
 

docs/mcp/client.md

@@ -365,6 +365,67 @@ async def main():
 
 MCP tools can include metadata that provides additional information about the tool's characteristics, which can be useful when [filtering tools][pydantic_ai.toolsets.FilteredToolset]. The `meta`, `annotations`, and `output_schema` fields can be found on the `metadata` dict on the [`ToolDefinition`][pydantic_ai.tools.ToolDefinition] object that's passed to filter functions.
 
+## Resources
+
+MCP servers can provide [resources](https://modelcontextprotocol.io/docs/concepts/resources) - files, data, or content that can be accessed by the client. Resources in MCP are application-driven, with host applications determining how to incorporate context manually, based on their needs. This means they will _not_ be exposed to the LLM automatically (unless a tool returns a `ResourceLink` or `EmbeddedResource`).
+
+Pydantic AI provides methods to discover and read resources from MCP servers:
+
+- [`list_resources()`][pydantic_ai.mcp.MCPServer.list_resources] - List all available resources on the server
+- [`list_resource_templates()`][pydantic_ai.mcp.MCPServer.list_resource_templates] - List resource templates with parameter placeholders
+- [`read_resource(uri)`][pydantic_ai.mcp.MCPServer.read_resource] - Read the contents of a specific resource by URI
+
+Resources are automatically converted: text content is returned as `str`, and binary content is returned as [`BinaryContent`][pydantic_ai.messages.BinaryContent].
+
+Before consuming resources, we need to run a server that exposes some:
+
+```python {title="mcp_resource_server.py"}
+from mcp.server.fastmcp import FastMCP
+
+mcp = FastMCP('Pydantic AI MCP Server')
+log_level = 'unset'
+
+
+@mcp.resource('resource://user_name.txt', mime_type='text/plain')
+async def user_name_resource() -> str:
+    return 'Alice'
+
+
+if __name__ == '__main__':
+    mcp.run()
+```
+
+Then we can create the client:
+
+```python {title="mcp_resources.py", requires="mcp_resource_server.py"}
+import asyncio
+
+from pydantic_ai.mcp import MCPServerStdio
+
+
+async def main():
+    server = MCPServerStdio('python', args=['-m', 'mcp_resource_server'])
+
+    async with server:
+        # List all available resources
+        resources = await server.list_resources()
+        for resource in resources:
+            print(f' - {resource.name}: {resource.uri} ({resource.mime_type})')
+            #>  - user_name_resource: resource://user_name.txt (text/plain)
+
+        # Read a text resource
+        user_name = await server.read_resource('resource://user_name.txt')
+        print(f'Text content: {user_name}')
+        #> Text content: Alice
+
+
+if __name__ == '__main__':
+    asyncio.run(main())
+```
+
+_(This example is complete, it can be run "as is")_
+
+
 ## Custom TLS / SSL configuration
 
 In some environments you need to tweak how HTTPS connections are established –

pydantic_ai_slim/pydantic_ai/_mcp.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import base64
 from collections.abc import Sequence
 from typing import Literal