在 MCP 服务器中使用 LLM 工具
您已经构建了一个 MCP 服务器，其中包含使用最新汇率进行货币转换的工具。将其与 LLM 集成后，LLM 将能够准确回答有关货币和汇率的问题——这些并非它默认就能做到。
本题的大部分代码已为您提供，重点在于理解工作流而非语法细节。
本练习是课程的一部分
Model Context Protocol (MCP) 入门
练习说明
- 将用户查询（
user_query）和已格式化的工具列表（anthropic_tools）发送给 Claude。 - 使用从工具调用块中提取的名称和参数，调用由 LLM 选择的 MCP 工具。
- 将结果（
result）返回给 Claude，以生成最终回复。
交互式实操练习
通过完成这段示例代码来试试这个练习。
from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client
async def call_anthropic_llm(user_query: str):
"""Call Claude with MCP tools."""
print(f"\nUser: {user_query}\n")
mcp_tools = await get_tools_from_mcp()
anthropic_tools = []
for tool in mcp_tools:
anthropic_tool = {
"name": tool.name,
"description": tool.description or "",
"input_schema": tool.inputSchema,
}
anthropic_tools.append(anthropic_tool)
# Send the user query and formatted tools to the LLM
client = AsyncAnthropic(api_key="")
response = await client.messages.create(
model="claude-sonnet-4-6",
max_tokens=1024,
messages=____,
tools=____,
)
if response.stop_reason == "tool_use":
tool_use = next(block for block in response.content if block.type == "tool_use")
name = tool_use.name
args = tool_use.input
print(f"Model decided to call: {name}")
print(f"Arguments: {args}\n")
# Call the MCP tool
result = await call_mcp_tool(____, ____)
# Send the result back to Claude for the final response
followup = await client.messages.create(
model="claude-sonnet-4-6",
max_tokens=1024,
messages=[
{"role": "user", "content": user_query},
{"role": "assistant", "content": response.content},
{
"role": "user",
"content": [
{
"type": "tool_result",
"tool_use_id": tool_use.id,
"content": ____,
}
],
},
],
tools=anthropic_tools,
)
final_text = next((block.text for block in followup.content if block.type == "text"), None)
if final_text:
print(f"\nAssistant: {final_text}")
return str(final_text)
else:
print("No follow-up message from model.")
else:
text = next((block.text for block in response.content if block.type == "text"), "")
print(f"\nAssistant: {text}")
return str(text)
if __name__ == "__main__":
asyncio.run(call_anthropic_llm("How much is 250 US dollars in euros?"))