e727af3a21
# PR Description
Consider this PR the result of a full pass through of this repository.
## Add helper for adding tools to an `MCPApp`
You can now add all of the tools in a module to an `MCPApp` via
`app.add_tools_from_module(...)`
## Edit what `arcade new` generates
First, I updated the backend to use hatchling.
Second, the structure generated before this PR was simple, but did not
create a proper Python module.
This hindered developers in the following ways:
1. Difficult to add the tools in your server to an evaluation suite
2. Difficult to add more than one tool to an MCPApp at a time
3. All other niceties that come with being able to import modules
```
# Before
server/
├── .env.example
├── server.py
└── pyproject.toml
```
This PR updates the structure generated such that a valid Python module
is generated:
```
# After
server/
├── pyproject.toml
└── src/
└── server/
├── __init__.py
├── .env.example
└── server.py
```
## Fix Tool Chaining
`self._ctx.server.executor.run(...)` was being called, but `MCPServer`
does not have an instance of `ToolExecutor` (and it's not intended to be
an instance anyways). I updated `Tool.call_raw` to pass the programmatic
tool call through the `MCPServer._handle_call_tool`. This means that the
programmatic tool calls now go through the same steps that a typical
tool call (initiated by the MCP client) would.
This means that **toolA**, which specifies **requirementsA**, is
permitted to call **toolB**, which specifies **requirementsB**, without
needing to explicitly declare or satisfy **requirementsB**. I believe
this is acceptable because the secrets and/or auth token associated with
**toolB's** `Context` are not exposed to **toolA**, and the secrets
and/or auth token associated with **toolA's** `Context` are not exposed
to **toolB**.
## Fix User Elicitation
1. The read & write streams were created with a maximum queue size of 0.
I increased this to 100.
2. I updated `ServerSession`'s run loop to both read messages from the
stream & process them concurrently. This enables server initiated
requests (like user elicitation and progress reporting) to be handled
while tools are being executed. Otherwise, the server initiated requests
would wait for the tool to finish executing and the tool execution would
wait for the server initiated request to finish.
3.
## Fix Progress Reporting
Progress tokens sent by the client were not being stored. Therefore
there was no way to notify a client with progress updates. I am now
storing the `progressToken`, along with other `_meta` sent from the
client, in the `ServerSession`'s `_request_meta`. I am setting
`_request_meta` whenever the `MCPServer` is handling an incoming message
from a client.
## Fix handling of server names with spaces
Before:
Server name: "The simple server name"
Tool name: whisper_secret
Name seen by client: "The_simple_server_name_WhisperSecret"
After
Server name: "The simple server name"
Tool name: whisper_secret
Name seen by client: "TheSimpleServerName_WhisperSecret"
## Add Integration Tests
The stdio integration test is much more comprehensive than the http
integration test. These tests will let me sleep a bit more at night
## Add Example MCP Servers
Example servers for sampling, user-elicitation, progress reporting,
logging, tool chaining, combining prebuilt tools with custom tools, tool
secrets, tool auth, evaluations, and more!
## Add Docker template
Added a Docker template for running an MCP server in Docker (and removed
the old docker stuff)
|
||
|---|---|---|
| .. | ||
| arcade_mcp_server | ||
| Makefile | ||
| pyproject.toml | ||
| README.md | ||
Arcade MCP Server
Arcade MCP (Model Context Protocol) Server enables AI assistants and development tools to interact with your Arcade tools through a standardized protocol. Build, deploy, and integrate MCP servers seamlessly across different AI platforms.
Quick Links
- Quickstart Guide - Get up and running in minutes
- Walkthrough - Learn by example
- API Reference - MCPApp API documentation
Features
- 🚀 FastAPI-like Interface - Simple, intuitive API with
MCPApp - 🔧 Tool Discovery - Automatic discovery of tools in your project
- 🔌 Multiple Transports - Support for stdio and HTTP/SSE
- 🤖 Multi-Client Support - Works with Claude, Cursor, and more
- 📦 Package Integration - Load installed Arcade packages
- 🔐 Built-in Security - Environment-based configuration and secrets
- 🔄 Hot Reload - Development mode with automatic reloading
- 📊 Production Ready - Deploy with Docker, systemd, PM2, or cloud platforms
Getting Started
Installation
pip install arcade-mcp-server
Create Your First Server
from arcade_mcp_server import MCPApp
from typing import Annotated
app = MCPApp(name="my-tools", version="1.0.0")
@app.tool
def greet(name: Annotated[str, "Name to greet"]) -> str:
"""Greet someone by name."""
return f"Hello, {name}!"
if __name__ == "__main__":
app.run()
Run Your Server
# For development
python my_tools.py
# For Claude Desktop
python -m arcade_mcp_server stdio
# For HTTP clients
python -m arcade_mcp_server --host 0.0.0.0 --port 8080
Community
Analytics & Privacy
Arcade MCP Server collects anonymous usage data to help us improve the service and debug issues. We track "MCP server start" events to understand server usage patterns and reliability.
What We Track
When the server starts, we collect the following information:
- Server configuration: transport type (
httporstdio), host, port - Server metadata: tool count, server version
- Runtime environment: Python version, OS type and release
- Timing: device timestamp
- Errors: error messages (if startup fails)
Privacy
- For anonymous users: Events are tracked with an anonymous ID and no user profile is created
- For authenticated users: Events are linked to your account to help us provide better support
- No sensitive data (credentials, tool inputs/outputs, or personal information) is ever collected
Opt Out
To disable usage tracking, set the environment variable ARCADE_USAGE_TRACKING to 0.
License
Arcade MCP Server is open source software licensed under the MIT license.