- Python 100%
db6d7a8a61* fix: elicitation scalar return, resource auto-serialization, Client.new() state, prompt errors
- Auto-wrap scalar elicitation responses for ScalarElicitationType schemas
so handlers can return T directly for ctx.elicit("msg", str/int/float)
- Auto-serialize dict/int/float/bool/None resource returns to JSON text
instead of crashing with TypeError
- Reset _task_registry and _submitted_task_ids in Client.new() so cloned
clients have independent task tracking state
- Include original error message in prompt render errors (matching tool
error behavior)
Fixes #3856
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* Fix misleading comment and add list/tuple auto-serialization for resources
The comment said "list/tuple of primitives" but the isinstance check
didn't include list or tuple. Now it does, and the comment matches.
🤖 Generated with Claude Code
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* Fix TaskNotificationHandler binding in Client.new() and meta forwarding for JSON resources
Two review-identified bugs:
1. Client.new() shallow-copies _session_kwargs, so the cloned client's
TaskNotificationHandler still dispatches to the original client.
Fix: create a fresh _session_kwargs dict with a new handler bound
to the new client.
2. convert_result() for dict/int/float/bool/None fell through to
ResourceResult(raw_value) which lost component meta (CSP, permissions).
The str/bytes path correctly wrapped in ResourceContent with meta.
Fix: explicitly serialize JSON-native types and wrap with meta,
matching the str/bytes path. Other types still fall through for
error handling.
🤖 Generated with Claude Code
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* Preserve custom message handlers in Client.new()
Only replace the message handler with a new TaskNotificationHandler
if the current handler IS a TaskNotificationHandler. If the user
provided a custom message_handler, preserve it in the clone.
🤖 Generated with Claude Code
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* fix client.new and add regression tests
* Honor declared MIME type for auto-serialized JSON resources
* Fix static analysis: remove unused StdioTransport import, fix ty:ignore comment
* Exclude list[ResourceContent] from JSON auto-serialization path
A bare list[ResourceContent] would match the isinstance(list) check
and get JSON-serialized instead of passing through to ResourceResult
normalization. Check for ResourceContent items first.
🤖 Generated with Claude Code
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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| .claude | ||
| .cursor/rules | ||
| .github | ||
| docs | ||
| examples | ||
| scripts | ||
| skills/fastmcp-client-cli | ||
| src/fastmcp | ||
| tests | ||
| v3-notes | ||
| .ccignore | ||
| .coderabbit.yaml | ||
| .gitignore | ||
| .pre-commit-config.yaml | ||
| .python-version | ||
| AGENTS.md | ||
| CLAUDE.md | ||
| CODE_OF_CONDUCT.md | ||
| CONTRIBUTING.md | ||
| justfile | ||
| LICENSE | ||
| logo.py | ||
| loq.toml | ||
| pyproject.toml | ||
| README.md | ||
| SECURITY.md | ||
| uv.lock | ||
The Model Context Protocol (MCP) connects LLMs to tools and data. FastMCP gives you everything you need to go from prototype to production:
from fastmcp import FastMCP
mcp = FastMCP("Demo 🚀")
@mcp.tool
def add(a: int, b: int) -> int:
"""Add two numbers"""
return a + b
if __name__ == "__main__":
mcp.run()
Why FastMCP
Building an effective MCP application is harder than it looks. FastMCP handles all of it. Declare a tool with a Python function, and the schema, validation, and documentation are generated automatically. Connect to a server with a URL, and transport negotiation, authentication, and protocol lifecycle are managed for you. You focus on your logic, and the MCP part just works: with FastMCP, best practices are built in.
That's why FastMCP is the standard framework for working with MCP. FastMCP 1.0 was incorporated into the official MCP Python SDK in 2024. Today, the actively maintained standalone project is downloaded a million times a day, and some version of FastMCP powers 70% of MCP servers across all languages.
FastMCP has three pillars:
Servers Expose tools, resources, and prompts to LLMs. |
Apps Give your tools interactive UIs rendered directly in the conversation. |
Clients Connect to any MCP server — local or remote, programmatic or CLI. |
Servers wrap your Python functions into MCP-compliant tools, resources, and prompts. Clients connect to any server with full protocol support. And Apps give your tools interactive UIs rendered directly in the conversation.
Ready to build? Start with the installation guide or jump straight to the quickstart. When you're ready to deploy, Prefect Horizon offers free hosting for FastMCP users.
Installation
We recommend installing FastMCP with uv:
uv pip install fastmcp
For full installation instructions, including verification and upgrading, see the Installation Guide.
Upgrading? We have guides for:
📚 Documentation
FastMCP's complete documentation is available at gofastmcp.com, including detailed guides, API references, and advanced patterns.
Documentation is also available in llms.txt format, which is a simple markdown standard that LLMs can consume easily:
llms.txtis essentially a sitemap, listing all the pages in the documentation.llms-full.txtcontains the entire documentation. Note this may exceed the context window of your LLM.
Community: Join our Discord server to connect with other FastMCP developers and share what you're building.
Contributing
We welcome contributions! See the Contributing Guide for setup instructions, testing requirements, and PR guidelines.