* Add poll_interval to TaskConfig
Allow users to configure polling interval per component via
TaskConfig(poll_interval=timedelta(...)). Default is 5 seconds.
* Update snapshots for poll_interval field
* Add version badge to poll_interval docs
* Add defensive handling for Redis data and align default poll intervals
* Add AnthropicSamplingHandler
Adds a sampling handler for the Anthropic API at
fastmcp.client.sampling.handlers.anthropic, alongside the existing
OpenAI handler. Includes full support for tool calling.
Install with: pip install fastmcp[anthropic]
* Update default model
* Update sampling docs to cover both OpenAI and Anthropic handlers
* Use AsyncAnthropic, fix falsy value handling, handle tool_choice none
* Propagate isError to Anthropic, join multiple text blocks, fix docs
* Unify SamplingHandler and promote OpenAI handler
Consolidates ServerSamplingHandler and ClientSamplingHandler into a single
SamplingHandler type alias. Moves OpenAISamplingHandler from experimental
to fastmcp.client.sampling.handlers.openai as the canonical location.
Backwards compatibility maintained for imports from experimental.
* Remove unreachable code paths in OpenAI handler
* Fix docstring and use elif for mutually exclusive branches
* 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: add PromptResult as canonical internal type for prompts
Applies the same pattern as ResourceContent to prompts. PromptResult
wraps messages with description and meta. Public render() can return
either list[PromptMessage] or PromptResult (backwards compatible),
while private _render() always returns PromptResult.
* docs: fix incorrect PromptResult return type in example
* feat: add PromptResult canonical type with meta support
* fix: address PR #2600 review comments
Fixes test failures and code quality issues identified in PR review:
- Update 3 tests in test_server_interactions.py to access PromptResult.messages[0] instead of indexing directly
- Fix ProxyPromptManager to preserve meta field when converting GetPromptResult to PromptResult
- Fix ProxyPrompt.render() to return PromptResult instead of deprecated list[PromptMessage], preventing fastmcp tags from leaking into runtime meta
- Fix mask_error_details initialization to respect explicit False values
- Fix exception re-raising to preserve tracebacks (use bare raise instead of raise e)
- Update testing documentation to use pytest -n auto for parallel execution
* 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
* feat: handle error from the initialize middleware
In some situation, the initialize middleware can check the status of the
server and decide to raise an error.
Example use case: in a FastMCPProxy, an initialization middleware
overrides the on_initialize method and connect to the underlying proxied
client. When client respond with error, I want to pass this error to the
client.
* docs update
* test: use McpError assertions now that exception propagation is fixed
- Update tests to catch McpError specifically instead of generic Exception
- Remove commented-out code in low_level.py
---------
Co-authored-by: Jeremiah Lowin <153965+jlowin@users.noreply.github.com>
* Add comprehensive documentation for read-only tool patterns
Created new patterns guide explaining readOnlyHint annotation usage,
including practical examples, client-specific behavior, and best
practices for marking tools as read-only.
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-authored-by: William Easton <strawgate@users.noreply.github.com>
* Reorganize read-only tools docs into tools section
Moved read-only tools documentation from standalone patterns page into the tools.mdx file as a "Using Annotation Hints" subsection. Condensed from 218 lines to ~50 lines focusing on practical usage while maintaining essential information about readOnlyHint and other annotations.
Changes:
- Added "Using Annotation Hints" subsection in tools.mdx after MCP Annotations
- Removed docs/patterns/read-only-tools.mdx
- Updated docs.json navigation to remove patterns entry
- Content now positioned as core tool feature rather than advanced pattern
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-authored-by: Bill Easton <strawgate@users.noreply.github.com>
---------
Co-authored-by: claude[bot] <41898282+claude[bot]@users.noreply.github.com>
Co-authored-by: William Easton <strawgate@users.noreply.github.com>
* SEP-1330 enum schema support for elicitation
* Add version badges for 2.14.0 elicitation features
* Fix Context.elicit() to handle SEP-1330 enum syntaxes
* Guard against empty list in elicit response_type
* Add guards for empty dict/list edge cases in elicit
* Refactor elicit: extract parsing and response handling to elicitation.py
* Add EventStore and SSE polling support (SEP-1699)
* Add close_sse_stream() method to Context
* Add SSE polling documentation
* Fix missing Context import in docs example
* Remove EventStore from root __init__.py, update docs imports
- Removed EventStore import and export from src/fastmcp/__init__.py
- Updated docs to import EventStore from fastmcp.server.event_store
- Resolves merge conflict by not exporting EventStore from root package
The task protocol (SEP-1686) is now always enabled - server always
registers task handlers and advertises task capabilities. Users still
opt into background execution at the server level (tasks=True) or
component level (task=True on tools, prompts, resources).
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Replace custom VersionBadge implementation with Mintlify's native Badge component while preserving custom color and border styling via CSS. This leverages Mintlify's built-in sizing, typography, and icon support while maintaining the original visual design.
- Lead with concepts instead of code
- Explain MCP background tasks vs general Python concurrency
- Document Docket's Prefect origins and battle-tested infrastructure
- Add sections on graceful degradation and embedded workers
- Fix version badge to 2.14.0
- Link to SEP-1686 spec
Docket provides background task execution and is now always available
for all FastMCP servers. Only `enable_tasks` remains to control the
SEP-1686 task protocol support.
Changes:
- Remove `enable_docket` setting and related validation
- Docket/Worker lifecycle is always active in server lifespan
- CurrentDocket and CurrentWorker dependencies work without config
- Add server readiness signaling via `_started` event
- Fix test timing issues with proper port probing
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
* Implement MCP background tasks (SEP-1686) using Docket
Adds support for background task execution via the MCP task protocol,
powered by Docket for task queue management.
- Tools, resources, and prompts can be marked with `task=True` to run async
- Progress dependency for tracking task progress
- CurrentDocket and CurrentWorker dependencies for advanced use cases
- Client API with `.call_tool(..., task=True)` returns task handles
- Task status notifications via subscriptions
- CLI worker command for distributed task processing
Configuration via environment:
- FASTMCP_ENABLE_DOCKET=true
- FASTMCP_ENABLE_TASKS=true
- FASTMCP_DOCKET_URL=redis://... (or memory:// for single-process)
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
* Fix tasks example import (TaskStatusResponse → GetTaskResult)
The example was using a non-existent TaskStatusResponse type.
Updated to use mcp.types.GetTaskResult which is what the
on_status_change callback actually receives.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
* Fix env var name in Docket error messages
The error messages referenced FASTMCP_EXPERIMENTAL_ENABLE_DOCKET but the
actual setting is FASTMCP_ENABLE_DOCKET.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
* Remove deprecated code re-added from pre-#2329 branch
- Remove ExtendedEnvSettingsSource (FASTMCP_SERVER_ prefix support)
- Remove dependencies parameter from FastMCP.__init__
* Replace fakeredis git pin with PyPI release
* Remove redundant fakeredis dev dep (pulled via pydocket)
---------
Co-authored-by: Claude <noreply@anthropic.com>
Co-authored-by: Jeremiah Lowin <153965+jlowin@users.noreply.github.com>
