arcade-mcp/examples/langchain/langgraph_arcade_minimal.py
Eric Gustin 3424ec8219
MCP Local (#563)
Versions:
* arcade-mcp\==1.0.0rc1
* arcade-mcp-server\==1.0.0rc1
* arcade-core\==2.5.0rc1
* arcade-tdk\==2.6.0rc1
* arcade-serve\==2.2.0rc1

### Summary
Adds first-class MCP support across Arcade, introduces a new MCP server
and CLI, unifies the project under the arcade-mcp name, overhauls
templates/scaffolding, and improves developer tooling, secrets
management, and examples.

### Highlights
- **MCP Server & Core**
- New MCP server with stdio and HTTP/SSE transports, session management,
resumability, and lifecycle handling.
- FastAPI-like `MCPApp` for building servers with lazy init; integrated
worker+MCP HTTP app option.
- Middleware system (logging and error handling), robust exception
hierarchy, and Pydantic-based settings.
- Async-safe managers for tools, resources, and prompts backed by
registries and locks.
- Developer-facing, transport-agnostic runtime context interfaces (logs,
tools, prompts, resources, sampling, UI, notifications).
- Conversion from Arcade ToolDefinition to MCP tool schema; OpenAI JSON
tool schema converter.
  - Parser supports `@app.tool`/`@app.tool(...)` decorators.

- **CLI**
  - New `mcp` command to run MCP servers with stdio or HTTP/SSE.
- New `secret` command to set/list/unset tool secrets (supports .env
input, preserves original casing for lookups).
- `new` command refactored; option to create a full toolkit package with
scaffolding.
  - `chat` command removed.
- `serve.py` imports updated to `arcade_serve.fastapi.telemetry`;
version retrieval now uses `arcade-mcp`.
  - `show.py` refactor to use new local catalog utilities.
- `display_tool_details` improved: adds “Default” column and handles
nested properties.

- **Configuration & Discovery**
- New `configure.py` to set up Claude Desktop, Cursor, and VS Code to
connect to local or Arcade Cloud MCP servers.
- Discovery utilities to find/install toolkits, build `ToolCatalog`s,
analyze files for tools, load kits from directories (pyproject parsing),
and build minimal toolkits.
- Better handling of provider API key resolution and evaluation suite
loading.

- **Templates & Scaffolding**
- Reorganized template structure (minimal vs full); moved
`.pre-commit-config.yaml`, `.ruff.toml`, license, Makefile, README,
tests, and tools layout to correct paths.
  - Minimal template adds `.env.example` for runtime secret injection.
- Template pyproject updated for MCP servers; includes sample server
with greeting and secret-reveal tools.
  - Authorization flow in templates simplified.

- **Repo-wide Renaming & Examples**
- Migrates references from `arcade-ai` to `arcade-mcp` across READMEs,
scripts, and package metadata.
- Examples updated (LangChain/LangGraph/AI SDK/TypeScript) and package
name changed to `arcade-mcp-sdk`.

- **Evals & Core Utilities**
- Evals now use OpenAI tooling format (`OpenAIToolList`, `to_openai`);
`tool_eval` takes `provider_api_key`.
- Core utilities: fixed `does_function_return_value` by dedenting before
parse; version bump to `2.5.0rc1` and dependency cleanup.

- **Tooling & CI**
- `setup-uv-env` action splits toolkit vs contrib dependency
installation.
- Pre-commit: excludes `libs/arcade-mcp-server/mkdocs.yml` and
`libs/tests/` from YAML and Ruff hooks; Ruff per-file ignores (e.g.,
C901 in `libs/**/*.py`, TRY400 in server docs paths).
- Makefile updates for uv env setup, quality checks, tests, builds, and
new `shell` target.
  - Added Makefile to MCP server library to streamline dev workflow.

- **Cleanup**
  - Removed `claude.json` config.
- Simplified stdio entrypoint; removed unused imports (`arcade_gmail`,
`arcade_search`).

### Breaking Changes
- **CLI**: `chat` command removed; use `mcp`, `secret`, and updated
`new`.
- **Naming**: All users should update references from `arcade-ai` to
`arcade-mcp`.
- **Templates**: File paths moved; downstream scripts referencing old
template locations may need updates.

### Getting Started
- Run an MCP server:
  - `arcade mcp --stdio --toolkits your_toolkit`
  - `arcade mcp --http --toolkits your_toolkit`
- Manage secrets:
  - `arcade secret set your_toolkit KEY=value`
  - `arcade secret list your_toolkit`
  - `arcade secret unset your_toolkit KEY`
- Configure clients:
- `arcade configure` to set up Claude Desktop, Cursor, and VS Code for
local/Arcade Cloud MCP.

---------

Co-authored-by: Sam Partee <sam@arcade-ai.com>
Co-authored-by: Shub <125150494+shubcodes@users.noreply.github.com>
2025-09-25 15:28:15 -07:00

60 lines
2.1 KiB
Python

import os
from langchain_arcade import ToolManager
from langchain_openai import ChatOpenAI
from langgraph.checkpoint.memory import MemorySaver
from langgraph.prebuilt import create_react_agent
# 1) Set API keys (place your real keys in env variables or directly below)
arcade_api_key = os.environ.get("ARCADE_API_KEY", "YOUR_ARCADE_API_KEY")
openai_api_key = os.environ.get("OPENAI_API_KEY", "YOUR_OPENAI_API_KEY")
# 2) Create an ToolManager and fetch/add tools/toolkits
manager = ToolManager(api_key=arcade_api_key)
# Tool names follow the format "ToolkitName.ToolName"
tools = manager.init_tools(tools=["Firecrawl.ScrapeUrl"])
print(manager.tools)
# Get all tools from a toolkit
tools = manager.init_tools(toolkits=["github"])
print(manager.tools)
# add a tool
manager.add_tool("GoogleSearch.Search")
print(manager.tools)
# add a toolkit
manager.add_toolkit("GoogleSearch")
print(manager.tools)
# 3) Get StructuredTool objects for langchain
lc_tools = manager.to_langchain()
# 4) Create a ChatOpenAI model and bind the Arcade tools.
model = ChatOpenAI(model="gpt-4o", api_key=openai_api_key)
bound_model = model.bind_tools(lc_tools)
# 5) Use MemorySaver for checkpointing.
memory = MemorySaver()
# 5) Create a ReAct-style agent from the prebuilt function.
graph = create_react_agent(model=bound_model, tools=lc_tools, checkpointer=memory)
# 6) Provide basic config and a user query.
# Note: user_id is required for the tool to be authorized
config = {"configurable": {"thread_id": "1", "user_id": "user@example.com"}}
user_input = {"messages": [("user", "star the arcadeai/arcade-mcp repo on github")]}
# 7) Stream the agent's output. If the tool is unauthorized, it may trigger interrupts
for chunk in graph.stream(user_input, config, stream_mode="values"):
chunk["messages"][-1].pretty_print()
# if we were interrupted, we can check for interrupts in state
current_state = graph.get_state(config)
if current_state.tasks:
for task in current_state.tasks:
if hasattr(task, "interrupts"):
for interrupt in task.interrupts:
print(interrupt.value)
# Once you login using the printed link, you can resume the agent