chore: import upstream snapshot with attribution
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wehub-resource-sync
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# MCP Prompt Server Example
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This example uses a local MCP prompt server in [server.py](server.py).
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Run the example via:
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```
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uv run python examples/mcp/prompt_server/main.py
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```
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## Details
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The example uses the `MCPServerStreamableHttp` class from `agents.mcp`. The script auto-selects an open localhost port (or honors `STREAMABLE_HTTP_PORT`) and runs the server at `http://<host>:<port>/mcp`, providing user-controlled prompts that generate agent instructions. If you need a specific address, set `STREAMABLE_HTTP_PORT` and `STREAMABLE_HTTP_HOST`.
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The server exposes prompts like `generate_code_review_instructions` that take parameters such as focus area and programming language. The agent calls these prompts to dynamically generate its system instructions based on user-provided parameters.
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## Workflow
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The example demonstrates two key functions:
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1. **`show_available_prompts`** - Lists all available prompts on the MCP server, showing users what prompts they can select from. This demonstrates the discovery aspect of MCP prompts.
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2. **`demo_code_review`** - Shows the complete user-controlled prompt workflow:
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- Calls `generate_code_review_instructions` with specific parameters (focus: "security vulnerabilities", language: "python")
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- Uses the generated instructions to create an Agent with specialized code review capabilities
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- Runs the agent against vulnerable sample code (command injection via `os.system`)
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- The agent analyzes the code and provides security-focused feedback using available tools
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This pattern allows users to dynamically configure agent behavior through MCP prompts rather than hardcoded instructions.
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import asyncio
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import os
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import shutil
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import socket
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import subprocess
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import time
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from typing import Any, cast
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from agents import Agent, Runner, gen_trace_id, trace
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from agents.mcp import MCPServer, MCPServerStreamableHttp
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from agents.model_settings import ModelSettings
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STREAMABLE_HTTP_HOST = os.getenv("STREAMABLE_HTTP_HOST", "127.0.0.1")
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def _choose_port() -> int:
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env_port = os.getenv("STREAMABLE_HTTP_PORT")
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if env_port:
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return int(env_port)
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with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
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s.bind((STREAMABLE_HTTP_HOST, 0))
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address = cast(tuple[str, int], s.getsockname())
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return address[1]
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STREAMABLE_HTTP_PORT = _choose_port()
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os.environ.setdefault("STREAMABLE_HTTP_PORT", str(STREAMABLE_HTTP_PORT))
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STREAMABLE_HTTP_URL = f"http://{STREAMABLE_HTTP_HOST}:{STREAMABLE_HTTP_PORT}/mcp"
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async def get_instructions_from_prompt(mcp_server: MCPServer, prompt_name: str, **kwargs) -> str:
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"""Get agent instructions by calling MCP prompt endpoint (user-controlled)"""
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print(f"Getting instructions from prompt: {prompt_name}")
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try:
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prompt_result = await mcp_server.get_prompt(prompt_name, kwargs)
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content = prompt_result.messages[0].content
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if hasattr(content, "text"):
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instructions = content.text
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else:
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instructions = str(content)
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print("Generated instructions")
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return instructions
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except Exception as e:
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print(f"Failed to get instructions: {e}")
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return f"You are a helpful assistant. Error: {e}"
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async def demo_code_review(mcp_server: MCPServer):
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"""Demo: Code review with user-selected prompt"""
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print("=== CODE REVIEW DEMO ===")
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# User explicitly selects prompt and parameters
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instructions = await get_instructions_from_prompt(
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mcp_server,
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"generate_code_review_instructions",
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focus="security vulnerabilities",
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language="python",
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)
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agent = Agent(
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name="Code Reviewer Agent",
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instructions=instructions, # Instructions from MCP prompt
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model_settings=ModelSettings(tool_choice="auto"),
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)
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message = """Please review this code:
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def process_user_input(user_input):
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command = f"echo {user_input}"
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os.system(command)
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return "Command executed"
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"""
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print(f"Running: {message[:60]}...")
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result = await Runner.run(starting_agent=agent, input=message)
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print(result.final_output)
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print("\n" + "=" * 50 + "\n")
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async def show_available_prompts(mcp_server: MCPServer):
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"""Show available prompts for user selection"""
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print("=== AVAILABLE PROMPTS ===")
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prompts_result = await mcp_server.list_prompts()
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print("User can select from these prompts:")
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for i, prompt in enumerate(prompts_result.prompts, 1):
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print(f" {i}. {prompt.name} - {prompt.description}")
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print()
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async def main():
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async with MCPServerStreamableHttp(
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name="Simple Prompt Server",
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params={"url": STREAMABLE_HTTP_URL},
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) as server:
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trace_id = gen_trace_id()
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with trace(workflow_name="Simple Prompt Demo", trace_id=trace_id):
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print(f"Trace: https://platform.openai.com/traces/trace?trace_id={trace_id}\n")
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await show_available_prompts(server)
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await demo_code_review(server)
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if __name__ == "__main__":
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if not shutil.which("uv"):
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raise RuntimeError("uv is not installed")
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process: subprocess.Popen[Any] | None = None
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try:
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this_dir = os.path.dirname(os.path.abspath(__file__))
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server_file = os.path.join(this_dir, "server.py")
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print(f"Starting Simple Prompt Server at {STREAMABLE_HTTP_URL} ...")
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env = os.environ.copy()
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env.setdefault("STREAMABLE_HTTP_HOST", STREAMABLE_HTTP_HOST)
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env.setdefault("STREAMABLE_HTTP_PORT", str(STREAMABLE_HTTP_PORT))
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process = subprocess.Popen(["uv", "run", server_file], env=env)
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time.sleep(3)
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print("Server started\n")
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except Exception as e:
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print(f"Error starting server: {e}")
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exit(1)
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try:
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asyncio.run(main())
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finally:
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if process:
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process.terminate()
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print("Server terminated.")
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import os
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from mcp.server.fastmcp import FastMCP
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STREAMABLE_HTTP_HOST = os.getenv("STREAMABLE_HTTP_HOST", "127.0.0.1")
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STREAMABLE_HTTP_PORT = int(os.getenv("STREAMABLE_HTTP_PORT", "18080"))
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# Create server
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mcp = FastMCP("Prompt Server", host=STREAMABLE_HTTP_HOST, port=STREAMABLE_HTTP_PORT)
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# Instruction-generating prompts (user-controlled)
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@mcp.prompt()
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def generate_code_review_instructions(
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focus: str = "general code quality", language: str = "python"
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) -> str:
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"""Generate agent instructions for code review tasks"""
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print(f"[debug-server] generate_code_review_instructions({focus}, {language})")
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return f"""You are a senior {language} code review specialist. Your role is to provide comprehensive code analysis with focus on {focus}.
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INSTRUCTIONS:
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- Analyze code for quality, security, performance, and best practices
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- Provide specific, actionable feedback with examples
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- Identify potential bugs, vulnerabilities, and optimization opportunities
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- Suggest improvements with code examples when applicable
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- Be constructive and educational in your feedback
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- Focus particularly on {focus} aspects
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RESPONSE FORMAT:
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1. Overall Assessment
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2. Specific Issues Found
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3. Security Considerations
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4. Performance Notes
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5. Recommended Improvements
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6. Best Practices Suggestions
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Use the available tools to check current time if you need timestamps for your analysis."""
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if __name__ == "__main__":
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mcp.run(transport="streamable-http")
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