* pin pydantic-monty to 0.0.8
* rename tool/prompt/resource base modules to avoid decorator name shadow
* add sys.modules shims for old submodule import paths
* preserve original module paths in deprecation warnings
* clarify when sys.modules shims can be removed
* Replace type: ignore[attr-defined] with isinstance assertions in tests
* Fix isinstance assertions in failing tests
- Fix enum test to check for ResponseEnum instead of str
- Fix binary resource test to check for BlobResourceContents instead of TextResourceContents
- Fix Root type tests to check attributes directly instead of isinstance checks
* Fix type errors without using type: ignore
- Remove execution methods from TransformingProvider (only handles transformations)
- Add execution methods to base Provider class with default implementations
- Fix type narrowing in tests using cast() instead of type: ignore
- Fix PromptResult type handling in prompt render tests
- Fix type narrowing in middleware test for arguments and structured_content
* MCP → SDK (vocab change only)
* WIP: Sampling API with SamplingResult[T] and result_type
* SEP-1577: Sampling with tools
- Add tools and result_type parameters to ctx.sample()
- Update OpenAI handler for tool content types
- Client advertises sampling.tools capability by default
- Collect tool results into single message with list content
* Fix tool result content handling in OpenAI handler
* Remove @sampling_tool decorator - pass functions directly to sample()
Functions passed to ctx.sample(tools=[...]) are now auto-converted
via SamplingTool.from_function(). Users can still use that method
directly for custom name/description overrides.
* Remove auto-conversion of MCP tools to sampling tools
Users want MCP tools passed to ctx.sample() to go through the full MCP
machinery (middleware, native responses) rather than being auto-converted
to direct function calls. Now only SamplingTool and plain callables are
accepted - passing a FastMCP Tool raises a clear TypeError.
Also bumps mcp dependency to >=1.24.0 for required sampling features.
* Refactor sampling API: replace sample_iter() with sample_step()
Replace the mutable SampleRun/sample_iter() pattern with a simpler stateless
sample_step() function. sample_step() makes a single LLM call and returns a
SampleStep with the response and history. sample() now loops sample_step()
internally.
Key changes:
- Add sample_step() for fine-grained control over the sampling loop
- Remove SampleRun class and sample_iter() method
- Structured output uses tool description only (no prompt modification)
- execute_tools parameter controls automatic vs manual tool execution
* Address CodeRabbit nitpicks
* Address CodeRabbit review feedback for sampling tools
- Fix temperature=0.0 being dropped due to falsy evaluation
- Add ToolChoice.name support for forcing specific tools
- Replace assert statements with explicit RuntimeError checks
- Add mask_error_details parameter to sample()/sample_step() with ToolError escape hatch
- Fix hasattr patterns with proper isinstance checks
- Document mask_error_details and add OpenAI prerequisites to docs
* Address additional CodeRabbit review feedback
- Catch ValidationError specifically instead of bare Exception
- Update result_type docs to mention dataclasses and basic types
- Raise ValueError for unknown tool_choice modes
- Validate sampling_handler_behavior to catch typos
- Remove ToolChoice.name handling (not part of MCP spec)
- Validate tool_choice string in sample_step()
* Review fixes for sampling tools PR
- Remove internal functions from sampling __init__.py exports
- Remove fragile is_text property, use not is_tool_use instead
- Inline call_client into context.py, remove from run.py
- Fix SamplingMessage docs to use TextContent
- Handle result.text being None in doc examples
- Simplify client sampling docs to recommend OpenAISamplingHandler
- Add sampling_capabilities override documentation
- Raise iteration limit from 50 to 100
- Remove _parse_model_preferences duplication
- Use AsyncOpenAI in OpenAISamplingHandler
- Fix tool_choice docstring
* Fix OpenAI handler tests to use AsyncOpenAI
* Address remaining CodeRabbit review comments
- Fix message ordering in OpenAI handler: tool results now correctly
follow assistant message with tool_calls
- sample_step() now always includes assistant message in history
- Raise ValueError on JSON parse errors instead of silent {}
- Add has_sampling capability check when behavior is None
- Raise RuntimeError when structured output receives text response
- Wrap primitive result_type schemas in object wrapper
- Fix docs example using invalid SamplingMessage construction
- Add comprehensive client_sampling_test.py example
* Add return type annotation to OpenAISamplingHandler.__init__
* Use explicit 'is not None' check for sampling_capabilities defaulting
---------
Co-authored-by: claude[bot] <41898282+claude[bot]@users.noreply.github.com>
Co-authored-by: Bill Easton <strawgate@users.noreply.github.com>
* feat: make ResourceContent the canonical internal type for resources
Add Resource._read() private method that always returns ResourceContent,
maintaining backwards compatibility for custom resources returning str/bytes
from read(). Includes deprecation warning when str/bytes is returned.
* fix: address review feedback for ResourceContent
- Remove ResourceContent from root exports (import from fastmcp.resources)
- Fix FunctionResource.read() return type to str | bytes | ResourceContent
- Decode base64 blobs in proxy when receiving from remote servers
- Preserve meta in ProxyResource cached content
* fix: add empty result guards in proxy resource reads
* Add compatibility tools contrib module
Implements four standalone tools that expose resources and prompts
as callable tools for clients that only support the tools capability.
Features:
- list_resources: List all available resources
- get_resource: Read a resource by URI
- list_prompts: List all available prompts
- get_prompt: Get a prompt with optional arguments
The tools use Context to access the server instance and can be easily
added to any FastMCP server using the add_compatibility_tools helper
or by adding individual tool instances directly.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-authored-by: William Easton <strawgate@users.noreply.github.com>
* Simplify compatibility tools to return raw MCP protocol objects
Return raw MCP protocol objects (ListResourcesResult, ReadResourceResult,
ListPromptsResult, GetPromptResult) instead of custom dictionaries. This
makes the tools simpler and more predictable by directly exposing what
the client methods return.
Co-authored-by: William Easton <strawgate@users.noreply.github.com>
* Add tool injection middleware
* cleanup contrib module
* More clean-up
* Clean up tool injection middleware.
* Update src/fastmcp/server/middleware/tool_injection.py
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* Add tool injection docs
* Small cleanup of prompt middleware
* PR Feedback
* Fix tool injection tests
---------
Co-authored-by: claude[bot] <41898282+claude[bot]@users.noreply.github.com>
Co-authored-by: William Easton <strawgate@users.noreply.github.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
