Merge pull request #17 from Azure-Samples/pamelaschangesforsomereason

Add GitHub MCP server example

Author: pamelafox

examples/langgraph_mcp_github.py

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+import os
+
+import azure.identity
+from dotenv import load_dotenv
+from langchain_mcp_adapters.client import MultiServerMCPClient
+from langchain_openai import AzureChatOpenAI, ChatOpenAI
+from langgraph.prebuilt import create_react_agent
+from rich import print
+
+# Setup the client to use either Azure OpenAI or GitHub Models
+load_dotenv(override=True)
+API_HOST = os.getenv("API_HOST", "github")
+
+if API_HOST == "azure":
+    token_provider = azure.identity.get_bearer_token_provider(azure.identity.DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default")
+    model = AzureChatOpenAI(
+        azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],
+        azure_deployment=os.environ["AZURE_OPENAI_CHAT_DEPLOYMENT"],
+        openai_api_version=os.environ["AZURE_OPENAI_VERSION"],
+        azure_ad_token_provider=token_provider,
+    )
+else:
+    model = ChatOpenAI(model=os.getenv("GITHUB_MODEL", "gpt-4o"), base_url="https://models.inference.ai.azure.com", api_key=os.environ["GITHUB_TOKEN"])
+
+
+async def setup_agent():
+    client = MultiServerMCPClient(
+        {
+            "github": {
+                "url": "https://api.githubcopilot.com/mcp/",
+                "transport": "streamable_http",
+                "headers": {
+                    "Authorization": f"Bearer {os.getenv('GITHUB_TOKEN')}",
+                },
+            }
+        }
+    )
+
+    tools = await client.get_tools()
+    agent = create_react_agent(model, tools)
+    stale_prompt_path = os.path.join(os.path.dirname(__file__), "staleprompt.md")
+    with open(stale_prompt_path) as f:
+        stale_prompt = f.read()
+    async for event in agent.astream_events({"messages": stale_prompt + " Find one issue from Azure-samples azure-search-openai-demo that is potentially closeable."}, version="v2"):
+        print(event)
+
+
+if __name__ == "__main__":
+    import asyncio
+    import logging
+
+    logging.basicConfig(level=logging.WARNING)
+    asyncio.run(setup_agent())

examples/staleprompt.md

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+
+# Stale Issue Finder and Analyzer
+
+You are a GitHub issue triage specialist tasked with finding old stale issues that can be safely closed as obsolete. DO NOT actually close them yourself unless specifically told to do so. Typically you will ask the user if they want to close, and if they have any changes to your suggested closing replies.
+
+## Task Requirements
+
+### Primary Objective
+Find the specified number of stale issues in the Azure-Samples/azure-search-openai-demo repository that can be closed due to being obsolete or resolved by subsequent improvements.
+
+### Analysis Process
+1. **Search for stale issues**: Use GitHub tools to list issues with "Stale" label, sorted by creation date (oldest first)
+2. **Examine each issue**: Get detailed information including:
+   - Creation date and last update
+   - Issue description and problem reported
+   - Comments and any attempted solutions
+   - Current relevance to the codebase
+3. **Search docs and repo**: Search the codebase (using search code tool and get file tool from GitHub MCP server) to see if code has changed in a way that resolves the issue. Also look at README.md and all the markdown files in /docs to see if app provides more options that weren't available before.
+4. **Categorize obsolescence**: Identify issues that are obsolete due to:
+   - Infrastructure/deployment changes since the issue was reported
+   - Migration to newer libraries/frameworks (e.g., OpenAI SDK updates)
+   - Cross-platform compatibility improvements
+   - Configuration system redesigns
+   - API changes that resolve the underlying problem
+
+### Output Format
+For each recommended issue closure, provide:
+
+1. **Issue Number and Title**
+2. **GitHub Link**: Direct URL to the issue
+3. **Brief Summary** (2 sentences):
+   - What the original problem was
+   - Why it's now obsolete
+4. **Suggested Closing Reply**: A professional comment explaining:
+   - Why the issue is being closed as obsolete
+   - What changes have made it irrelevant (Only high confidence changes)
+   - Invitation to open a new issue if the problem persists with current version
+
+### Success Criteria
+- Issues should be at least 1 year old
+- Issues should have "Stale" label
+- Must provide clear rationale for why each issue is obsolete
+- Closing replies should be professional and helpful
+- Focus on issues that won't recur with current codebase
+
+### Constraints
+- Do not recommend closing issues that represent ongoing valid feature requests
+- Avoid closing issues that highlight fundamental design limitations
+- Skip issues that could still affect current users even if less common
+- Ensure the obsolescence is due to actual code/infrastructure changes, not just age
+
+### Example Categories to Target
+- Deployment failures from early 2023 that were fixed by infrastructure improvements
+- Cross-platform compatibility issues resolved by script migrations
+- API errors from old library versions that have been updated
+- Configuration issues resolved by azd template redesigns
+- Authentication/permissions errors fixed by improved role assignment logic