chore: import upstream snapshot with attribution

2026-07-13 13:31:35 +08:00
commit c275ba2868
13613 changed files with 2980806 additions and 0 deletions
@@ -0,0 +1,121 @@
+"""
+cd to the `examples/snippets/clients` directory and run:
+    uv run client
+"""
+
+import asyncio
+import os
+
+from pydantic import AnyUrl
+
+from mcp import ClientSession, StdioServerParameters, types
+from mcp.client.stdio import stdio_client
+from mcp.shared.context import RequestContext
+
+import os
+from openai import OpenAI
+
+# Create server parameters for stdio connection
+server_params = StdioServerParameters(
+    command="python",  # Using python to run the server
+    args=["server.py"]
+)
+
+async def call_llm(prompt: str, system_prompt: str) -> str:
+    client = OpenAI(
+    base_url="https://models.github.ai/inference",
+    api_key=os.environ["GITHUB_TOKEN"],
+)
+
+    response = client.chat.completions.create(
+        messages=[
+            {
+                "role": "system",
+                "content": system_prompt,
+            },
+            {
+                "role": "user",
+                "content": prompt,
+            }
+        ],
+        model="openai/gpt-4o-mini",
+        temperature=1,
+        max_tokens=200,
+        top_p=1
+    )
+
+    return response.choices[0].message.content
+
+
+# Optional: create a sampling callback
+async def handle_sampling_message(
+    context: RequestContext[ClientSession, None], params: types.CreateMessageRequestParams
+) -> types.CreateMessageResult:
+    print(f"Sampling request: {params.messages}")
+
+    message = params.messages[0].content.text
+
+    # todo, call an actual llm and change below
+    response = await call_llm(message, "You're a helpful assistant, keep to the topic, don't make things up too much but definitely create a compelling product description")
+
+    return types.CreateMessageResult(
+        role="assistant",
+        content=types.TextContent(
+            type="text",
+            text=response,
+        ),
+        model="gpt-3.5-turbo",
+        stopReason="endTurn",
+    )
+
+
+async def run():
+    async with stdio_client(server_params) as (read, write):
+        async with ClientSession(read, write, sampling_callback=handle_sampling_message) as session:
+            # Initialize the connection
+            await session.initialize()
+
+            # List available prompts
+            # prompts = await session.list_prompts()
+            # print(f"Available prompts: {[p.name for p in prompts.prompts]}")
+
+            # # Get a prompt (greet_user prompt from fastmcp_quickstart)
+            # if prompts.prompts:
+            #     prompt = await session.get_prompt("greet_user", arguments={"name": "Alice", "style": "friendly"})
+            #     print(f"Prompt result: {prompt.messages[0].content}")
+
+            # # List available resources
+            # resources = await session.list_resources()
+            # print(f"Available resources: {[r.uri for r in resources.resources]}")
+
+            # List available tools
+            # tools = await session.list_tools()
+            # print(f"Available tools: {[t.name for t in tools.tools]}")
+
+            # # Read a resource (greeting resource from fastmcp_quickstart)
+            # resource_content = await session.read_resource(AnyUrl("greeting://World"))
+            # content_block = resource_content.contents[0]
+            # if isinstance(content_block, types.TextContent):
+            #     print(f"Resource content: {content_block.text}")
+
+            # Call a tool (create_product tool from fastmcp_quickstart)
+            result = await session.call_tool("create_product", arguments={"product_name": "paprika", "keywords": "red, juicy, vegetable"})
+            print("result:", result.content[0].text)
+
+            result = await session.call_tool("get_products", arguments={})
+            print("result:", result.content[0].text)
+
+            # result_unstructured = result.content[0]
+            # if isinstance(result_unstructured, types.TextContent):
+            #     print(f"Tool result: {result_unstructured.text}")
+            # result_structured = result.structuredContent
+            # print(f"Structured tool result: {result_structured}")
+
+
+def main():
+    """Entry point for the client script."""
+    asyncio.run(run())
+
+
+if __name__ == "__main__":
+    main()