d7107c107d
* Reorganize the examples folder * Add two mcp server examples. A local filesystem server and a simple 'starter' server.
45 lines
1.7 KiB
Python
45 lines
1.7 KiB
Python
"""
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This example shows how to call a tool that requires authorization with an LLM using the OpenAI Python client.
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"""
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import os
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from openai import OpenAI
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def call_tool_with_openai(client: OpenAI) -> dict:
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response = client.chat.completions.create(
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messages=[
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{"role": "user", "content": "Star the ArcadeAI/arcade-mcp repository."},
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],
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model="gpt-4o-mini", # TODO: Try "claude-3-5-sonnet-20240620" or other models from our supported model providers. Checkout out our docs for a full list https://docs.arcade.dev
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user="you@example.com",
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tools=["Github.SetStarred"],
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tool_choice="generate", # TODO: Try "execute" and note any differences
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)
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return response
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if __name__ == "__main__":
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arcade_api_key = os.environ.get(
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"ARCADE_API_KEY"
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) # If you forget your Arcade API key, it is stored at ~/.arcade/credentials.yaml on `arcade login`
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cloud_host = "https://api.arcade.dev" + "/v1"
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openai_client = OpenAI(
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api_key=arcade_api_key,
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base_url=cloud_host, # Alternatively, use http://localhost:9099/v1 if you are running Arcade Engine locally
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)
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chat_result = call_tool_with_openai(openai_client)
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# If the tool call requires authorization, then wait for the user to authorize and then call the tool again
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if (
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chat_result.choices[0].tool_authorizations
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and chat_result.choices[0].tool_authorizations[0].get("status") == "pending"
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):
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print(chat_result.choices[0].message.content)
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input("After you have authorized, press Enter to continue...")
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chat_result = call_tool_with_openai(openai_client)
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print(chat_result.choices[0].message.content)
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