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Eliza Huang
d3796f9b33
Close #2893 Co-authored-by: Eliza Huang <heliza@google.com> PiperOrigin-RevId: 826070077
57 lines
2.0 KiB
Python
Executable File
57 lines
2.0 KiB
Python
Executable File
# Copyright 2025 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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from google.adk.agents import LlmAgent
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from google.adk.models.lite_llm import LiteLlm
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from google.adk.tools.mcp_tool import MCPToolset
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from google.adk.tools.mcp_tool.mcp_session_manager import StdioConnectionParams
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from mcp import StdioServerParameters
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# This example uses the OpenAI API for both the agent and the server.
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# Ensure your OPENAI_API_KEY is available as an environment variable.
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api_key = os.getenv('OPENAI_API_KEY')
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if not api_key:
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raise ValueError('The OPENAI_API_KEY environment variable must be set.')
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# Configure the StdioServerParameters to start the mcp_server.py script
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# as a subprocess. The OPENAI_API_KEY is passed to the server's environment.
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server_params = StdioServerParameters(
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command='python',
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args=['mcp_server.py'],
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env={'OPENAI_API_KEY': api_key},
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)
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# Create the ADK MCPToolset, which connects to the FastMCP server.
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# The `tool_filter` ensures that only the 'analyze_sentiment' tool is exposed
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# to the agent.
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mcp_toolset = MCPToolset(
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connection_params=StdioConnectionParams(
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server_params=server_params,
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),
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tool_filter=['analyze_sentiment'],
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)
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# Define the ADK agent that uses the MCP toolset.
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root_agent = LlmAgent(
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model=LiteLlm(model='openai/gpt-4o'),
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name='SentimentAgent',
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instruction=(
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'You are an expert at analyzing text sentiment. Use the'
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' analyze_sentiment tool to classify user input.'
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),
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tools=[mcp_toolset],
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)
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