* Update repository references from jlowin/fastmcp to prefecthq/fastmcp
* Retrigger CI after repo transfer
* chore: Update SDK documentation
* Only run deep triage on bug issues for jlowin
---------
Co-authored-by: marvin-context-protocol[bot] <225465937+marvin-context-protocol[bot]@users.noreply.github.com>
* Reorganize docs navigation and add Apps documentation
Collapse Providers, Transforms, and Deployment under Servers. Add Apps
section with overview and low-level API pages. Add card images to welcome
page and README. Add NEW tags to recent features.
* Fix missing imports in Apps low-level API code examples
* generate-cli: auto-generate SKILL.md alongside CLI script
generate-cli now produces a SKILL.md agent skill file next to the CLI
script, documenting every tool's exact invocation syntax, parameter
flags, and types. Agents can use the CLI immediately without discovery.
* Use uv run --with fastmcp in generated SKILL.md invocations
* Fix skill generation issues from review
- Escape pipe chars in union type labels so markdown tables render
- Boolean params omit <value> placeholder in example invocations
- Quote YAML frontmatter values to handle special chars in names
- Match cyclopts camelCase→snake_case in flag derivation
- Use four-backtick fence for nested code block in docs
* Replace --skill/--no-skill with just --no-skill
* Escape quotes in YAML frontmatter description
* Strip newlines from param descriptions in skill table rows
* Detect boolean union types for flag placeholder
* Add `fastmcp generate-cli` command
Connects to any MCP server, reads its tool/resource/prompt schemas,
and writes a standalone Python CLI script with typed subcommands.
* docs: add generate-cli documentation
* docs: add generate-cli documentation; skip Windows executable test
* fix: address PR review feedback
- Sanitize tool and parameter names to valid Python identifiers
- Replace bare except Exception with specific exception types
- Escape server name in generated string literals
- Handle trailing colon edge case in _derive_server_name
- Clarify in docs that generated CLI is a client, not a bundled server
* Fix string escaping issues in generate-cli
- Use single-quoted docstrings to avoid triple-quote escaping issues
- Escape quotes in app_name derived from server_name
- Add tests for descriptions with quotes and server names with quotes
Addresses CodeRabbit review comments about insufficient escaping.
* Implement smart parameter handling for generate-cli
- Simple types (str, int, float, bool): Direct typed flags
- Arrays of simple types (list[str], list[int]): Repeatable flags via cyclopts
- Complex types (objects, nested arrays): Accept JSON strings with parsing
- JSON schema shown in help text for complex parameters
- Proper escaping of newlines and quotes in help text
- Filter out None and empty list defaults when calling tools
This gives typed, discoverable CLIs for common cases while handling
complex schemas via JSON input.
* Update generate-cli docs to explain smart parameter handling
- Document simple types as direct typed flags
- Document arrays of simple types as repeatable flags
- Document complex types as JSON strings with schema in help
- Add examples showing all three patterns
* Fix Codex review issues in generate-cli
High priority fixes:
- Complex type defaults: Serialize dict/list defaults to JSON strings
- List params: Preserve help metadata with Annotated wrapper
- Name collisions: Detect and error on sanitized name conflicts
- JSON parsing: Use isinstance check for safety with defaults
Added tests for:
- Complex types with default values
- Parameter name collision detection
- Updated existing tests to match new format
* Use pydantic_core.to_json for consistency
- Generator now uses pydantic_core.to_json() instead of json.dumps()
- Consistent with rest of fastmcp codebase
- Generated CLI still uses plain json module (standalone script)
* Move local imports to module level in generate-cli
* Handle union item types and Python keyword collisions in generate-cli
* 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>
- 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
* 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>
* Add manual initialization control to Client
- Add auto_initialize parameter (default True) to control automatic initialization
- Make initialize() method public with idempotent caching
- Add comprehensive test suite for initialization behavior
* Document client initialization control and server instructions
- Expand documentation to cover auto_initialize parameter
- Show manual initialization for advanced use cases
- Document accessing server instructions via initialize_result
* Update client.mdx
Add warning notes to documentation directing users to review
py-key-value documentation for backend maturity and limitations
before production use.
Co-authored-by: William Easton <strawgate@users.noreply.github.com>
* Update docs for required scopes
* add scopes
* Fix Azure scope validation
Azure returns unprefixed scopes in JWT tokens but requires prefixed scopes in authorization requests. The previous implementation incorrectly validated tokens against prefixed scopes, causing "invalid_token" errors.
Simplified AzureProvider to use standard JWTVerifier with unprefixed scopes for validation. Scopes are only prefixed when building the Azure authorization URL via _build_upstream_authorize_url() override.
Closes#2263
* Improve OAuth client token storage security documentation
Updated warning message and documentation to address security concerns
around storing OAuth credentials for multiple MCP servers.
Implements comprehensive notification system for tools, resources, and prompts with automatic client updates and flexible message handlers.
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
