File size: 2,079 Bytes
1a9e2c2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 | # -*- coding: utf-8 -*-
"""FastMCP服务器实例"""
from fastmcp import FastMCP
from fastmcp.server.auth.providers.jwt import StaticTokenVerifier
from app.services.mcp.tools import ask_grok_impl
from app.core.config import setting
def create_mcp_server() -> FastMCP:
"""创建MCP服务器实例，如果配置了API密钥则启用认证"""
# 检查是否配置了API密钥
api_key = setting.grok_config.get("api_key")
# 如果配置了API密钥，则启用静态token验证
auth = None
if api_key:
auth = StaticTokenVerifier(
tokens={
api_key: {
"client_id": "grok2api-client",
"scopes": ["read", "write", "admin"]
}
},
required_scopes=["read"]
)
# 创建FastMCP实例
return FastMCP(
name="Grok2API-MCP",
instructions="MCP server providing Grok AI chat capabilities. Use ask_grok tool to interact with Grok AI models.",
auth=auth
)
# 创建全局MCP实例
mcp = create_mcp_server()
# 注册ask_grok工具
@mcp.tool
async def ask_grok(
query: str,
model: str = "grok-3-fast",
system_prompt: str = None
) -> str:
"""
调用Grok AI进行对话，尤其适用于当用户询问最新信息，需要调用搜索功能，或是想了解社交平台动态（如Twitter(X)、Reddit等）时。
Args:
query: 用户的问题或指令
model: Grok模型名称,可选值: grok-3-fast(默认), grok-4-fast, grok-4-fast-expert, grok-4-expert, grok-4-heavy
system_prompt: 可选的系统提示词,用于设定AI的角色或行为约束
Returns:
Grok AI的完整回复内容,可能包括文本和图片链接(Markdown格式)
Examples:
- 简单问答: ask_grok("什么是Python?")
- 指定模型: ask_grok("解释量子计算", model="grok-4-fast")
- 带系统提示: ask_grok("写一首诗", system_prompt="你是一位古典诗人")
"""
return await ask_grok_impl(query, model, system_prompt)
|