--api-key=your_api_key_here --api-secret=your_api_secret_here 

EXTEND_API_KEY=your_api_key_here
EXTEND_API_SECRET=your_api_secret_here

npx @modelcontextprotocol/inspector python extend_ai_toolkit/modelcontextprotocol/main.py --tools=all

{
  "extend-mcp": {
    "command": "python",
    "args": [
      "-m",
      "extend_ai_toolkit.modelcontextprotocol.main",
      "--tools=all"
    ],
    "env": {
      "EXTEND_API_KEY": "apik_XXXX",
      "EXTEND_API_SECRET": "XXXXX"
    }
  },
  // optional: if you want to use the create_receipt_attachment tool
  "filesystem": { 
    "command": "npx",
    "args": [
      "-y",
      "@modelcontextprotocol/server-filesystem",
      "/path/to/receipts/folder"
    ]
  }
}

python -m extend_ai_toolkit.modelcontextprotocol.main_sse --tools=all --api-key="apikey" --api-secret="apisecret"

python -m extend_ai_toolkit.modelcontextprotocol.client.mcp_client --mcp-server-host localhost --mcp-server-port 8000 --llm-provider=anthropic --llm-model=claude-3-5-sonnet-20241022

import os
from langchain_openai import ChatOpenAI
from extend_ai_toolkit.openai.toolkit import ExtendOpenAIToolkit
from extend_ai_toolkit.shared import Configuration, Scope, Product, Actions

# Initialize the OpenAI toolkit
extend_openai_toolkit = ExtendOpenAIToolkit.default_instance(
    api_key=os.environ.get("EXTEND_API_KEY"),
    api_secret=os.environ.get("EXTEND_API_SECRET"),
    configuration=Configuration(
        scope=[
            Scope(Product.VIRTUAL_CARDS, actions=Actions(read=True)),
            Scope(Product.CREDIT_CARDS, actions=Actions(read=True)),
            Scope(Product.TRANSACTIONS, actions=Actions(read=True)),
        ]
    )
)

# Create an agent with the tools
extend_agent = Agent(
    name="Extend Agent",
    instructions="You are an expert at integrating with Extend",
    tools=extend_openai_toolkit.get_tools()
)

import os
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
from extend_ai_toolkit.langchain.toolkit import ExtendLangChainToolkit
from extend_ai_toolkit.shared import Configuration, Scope, Product, Actions

# Initialize the LangChain toolkit
extend_langchain_toolkit = ExtendLangChainToolkit.default_instance(
    api_key=os.environ.get("EXTEND_API_KEY"),
    api_secret=os.environ.get("EXTEND_API_SECRET"),
    configuration=Configuration(
        scope=[
            Scope(Product.VIRTUAL_CARDS, actions=Actions(read=True)),
            Scope(Product.CREDIT_CARDS, actions=Actions(read=True)),
            Scope(Product.TRANSACTIONS, actions=Actions(read=True)),
        ]
    )
)

# Create tools for the agent
tools = extend_langchain_toolkit.get_tools()

# Create the agent executor
langgraph_agent_executor = create_react_agent(
    ChatOpenAI(model="gpt-4"),
    tools
)

import os
from extend_ai_toolkit.crewai.toolkit import ExtendCrewAIToolkit
from extend_ai_toolkit.shared import Configuration, Scope, Product, Actions

# Initialize the CrewAI toolkit
toolkit = ExtendCrewAIToolkit.default_instance(
    api_key=os.environ.get("EXTEND_API_KEY"),
    api_secret=os.environ.get("EXTEND_API_SECRET"),
    configuration=Configuration(
        scope=[
            Scope(Product.VIRTUAL_CARDS, actions=Actions(read=True)),
            Scope(Product.CREDIT_CARDS, actions=Actions(read=True)),
            Scope(Product.TRANSACTIONS, actions=Actions(read=True)),
        ]
    )
)

# Configure the LLM (using Claude)
toolkit.configure_llm(
    model="claude-3-opus-20240229",
    api_key=os.environ.get("ANTHROPIC_API_KEY")
)

# Create the Extend agent
extend_agent = toolkit.create_agent(
    role="Extend Integration Expert",
    goal="Help users manage virtual cards, view credit cards, and check transactions efficiently",
    backstory="You are an expert at integrating with Extend, with deep knowledge of virtual cards, credit cards, and transaction management.",
    verbose=True
)

# Create a task for handling user queries
query_task = toolkit.create_task(
    description="Process and respond to user queries about Extend services",
    agent=extend_agent,
    expected_output="A clear and helpful response addressing the user's query",
    async_execution=True
)

# Create a crew with the agent and task
crew = toolkit.create_crew(
    agents=[extend_agent],
    tasks=[query_task],
    verbose=True
)

# Run the crew
result = crew.kickoff()