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* feat(python): Add MCP client OTel spans per GenAI semantic conventions Implement MCP client spans per the OTel GenAI Semantic Conventions for MCP (https://opentelemetry.io/docs/specs/semconv/gen-ai/mcp/#client). Operations instrumented: - initialize: CLIENT span capturing MCP session setup - tools/list: CLIENT span for tool listing (per-page) - prompts/list: CLIENT span for prompt listing (per-page) - tools/call: CLIENT span (nested under execute_tool when called via FunctionTool) - prompts/get: CLIENT span Span attributes follow the MCP semantic conventions: - Required: mcp.method.name - Conditional: error.type, gen_ai.tool.name, gen_ai.prompt.name - Recommended: gen_ai.operation.name, mcp.protocol.version, mcp.session.id, network.transport, server.address, server.port Transport-specific attributes per subclass: - MCPStdioTool: network.transport=pipe - MCPStreamableHTTPTool: network.transport=tcp, network.protocol.name=http - MCPWebsocketTool: network.transport=tcp, network.protocol.name=websocket All span creation gated behind OBSERVABILITY_SETTINGS.ENABLED. Closes #3624 Closes #4697 Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * refactor: simplify MCP spans — remove enrichment logic and protocol version caching - Always create nested CLIENT spans for tools/call instead of enriching the parent execute_tool span - Remove _ACTIVE_TOOL_EXECUTION_SPAN contextvar (no longer needed) - Remove enrich_span_with_mcp_attributes() helper - Remove _otel_error_type preservation in FunctionTool.invoke() - Remove _mcp_protocol_version instance variable; protocol version is only set on the initialize span where it is available Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Refine copilot solution * fix: enable automatic exception recording on MCP spans Remove record_exception=False and set_status_on_exception=False from create_mcp_client_span. Let OTel handle exception recording and status setting automatically. The manual set_mcp_span_error calls for tools/call still correctly set error.type (which OTel's automatic handling doesn't touch), so tool_error is preserved. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Reduce number of lines * Add comment to sample * test: address PR review comments on MCP observability tests - Fix initialize test to call mocked session.initialize() and read protocolVersion from the result instead of hardcoding it - Add tools/call McpError error-path test - Add prompts/get McpError error-path test Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Fix export error --------- Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
84 lines
3.0 KiB
Python
84 lines
3.0 KiB
Python
# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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import os
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from agent_framework import Agent
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from agent_framework.openai import OpenAIChatClient
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from dotenv import load_dotenv
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"""
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MCP GitHub Integration with Personal Access Token (PAT)
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This example demonstrates how to connect to GitHub's remote MCP server using a Personal Access
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Token (PAT) for authentication. The agent can use GitHub operations like searching repositories,
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reading files, creating issues, and more depending on how you scope your token.
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Prerequisites:
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1. A GitHub Personal Access Token with appropriate scopes
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- Create one at: https://github.com/settings/tokens
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- For read-only operations, you can use more restrictive scopes
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2. Environment variables:
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- GITHUB_PAT: Your GitHub Personal Access Token (required)
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- OPENAI_API_KEY: Your OpenAI API key (required)
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- OPENAI_MODEL: Your OpenAI model ID (required)
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"""
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async def github_mcp_example() -> None:
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"""Example of using GitHub MCP server with PAT authentication."""
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# 1. Load environment variables from .env file if present
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load_dotenv()
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# 2. Get configuration from environment
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github_pat = os.getenv("GITHUB_PAT")
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if not github_pat:
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raise ValueError(
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"GITHUB_PAT environment variable must be set. Create a token at https://github.com/settings/tokens"
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)
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# 3. Create authentication headers with GitHub PAT
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auth_headers = {
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"Authorization": f"Bearer {github_pat}",
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}
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# 4. Create agent with the GitHub MCP tool using instance method
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# The MCP tool manages the connection to the MCP server and makes its tools available
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# Set approval_mode="never_require" to allow the MCP tool to execute without approval
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client = OpenAIChatClient()
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# Note that the tool created here will be executed remotely by OpenAI, not locally by
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# your application.
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github_mcp_tool = client.get_mcp_tool(
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name="GitHub",
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url="https://api.githubcopilot.com/mcp/",
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headers=auth_headers,
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approval_mode="never_require",
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)
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# 5. Create agent with the GitHub MCP tool
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async with Agent(
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client=client,
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name="GitHubAgent",
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instructions=(
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"You are a helpful assistant that can help users interact with GitHub. "
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"You can search for repositories, read file contents, check issues, and more. "
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"Always be clear about what operations you're performing."
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),
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tools=github_mcp_tool,
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) as agent:
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# Example 1: Get authenticated user information
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query1 = "What is my GitHub username and tell me about my account?"
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print(f"\nUser: {query1}")
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result1 = await agent.run(query1)
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print(f"Agent: {result1.text}")
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# Example 2: List my repositories
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query2 = "List all the repositories I own on GitHub"
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print(f"\nUser: {query2}")
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result2 = await agent.run(query2)
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print(f"Agent: {result2.text}")
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if __name__ == "__main__":
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asyncio.run(github_mcp_example())
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