搭建mcp服务

.gitignore

+# Python-generated files
+__pycache__/
+*.py[oc]
+build/
+dist/
+wheels/
+*.egg-info
+
+# Virtual environments
+.venv
+log

.python-version

+3.13

QueryParam.py

+from pydantic import BaseModel
+
+
+class QueryParam(BaseModel):
+    """查询参数实体"""
+
+    message: str
+    """用户输入的消息内容"""
+
+    customer_token: str
+    """客户token"""
+

api.py

+import uvicorn
+from fastapi import FastAPI
+
+from QueryParam import QueryParam
+from mcp_client import user_query
+
+app = FastAPI(name=["BME MCP服务"])
+
+
+@app.post(path="/mcp/query", description="调用mcp工具查询")
+async def query(query_param: QueryParam):
+    message = await user_query(query_param)
+    return {
+        "message": message
+    }
+
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=8000)

config.yaml

+active: deepseek_v3_bme
+model:
+  # deepseek-v3官方模型
+  deepseek_v3_official:
+    api_key: sk-1fc82f2f3b424668bddc84c89b7131d8
+    base_url: https://api.deepseek.com
+    model_name: deepseek-chat
+  # deepseek-r1官方模型
+  deepseek_r1_official:
+    api_key: sk-1fc82f2f3b424668bddc84c89b7131d8
+    base_url: https://api.deepseek.com
+    model_name: deepseek-reasoner
+  # deepseek-r1公司模型
+  deepseek_r1_bme:
+    api_key: Tc7sY47hiU5d1LNGbJjGBfqfY13IE3khIc0uBvpJ11U
+    base_url: http://10.10.10.12:30070/v1
+    model_name: deepseek-r1
+  # deepseek-v3公司模型
+  deepseek_v3_bme:
+    api_key: Tc7sY47hiU5d1LNGbJjGBfqfY13IE3khIc0uBvpJ11U
+    base_url: http://10.10.10.12:30100/v1
+    model_name: deepseek-v3
+server:
+  # 运行命令
+  command: python
+  args:
+    - mcp_server.py
+tool_calls_deep: 20  # tool_call调用深度
+log:
+  base_path: log
+remote:
+  base_url:
+    bme-screen-service: https://vis.bmetech.com/vis
+customer:
+  01ce2837d453c02f9b0e1828d0134e8e: bme  # 超级管理员，可以查看所有客户资源
+  ef616aad53d3eddfb53ca71980421440: 59  # 连云港华乐合金集团有限公司
\ No newline at end of file

mcp_client.py

+from contextlib import AsyncExitStack
+from datetime import datetime
+from typing import Optional
+from loguru import logger
+from mcp import ClientSession, StdioServerParameters
+from mcp.client.stdio import stdio_client
+from openai import OpenAI
+from openai.types.chat import ChatCompletionToolParam, ChatCompletionAssistantMessageParam, \
+    ChatCompletionMessageToolCallParam, ChatCompletionToolMessageParam
+from openai.types.chat.chat_completion_message_tool_call_param import Function
+from openai.types.shared_params import FunctionDefinition
+
+import json
+import yaml
+from pydantic import AnyUrl
+
+from QueryParam import QueryParam
+
+with open("config.yaml", "r", encoding="utf-8") as yml_file:
+    config = yaml.safe_load(yml_file)
+
+if config["log"]["base_path"]:
+    logger.add(config["log"]["base_path"] + "/mcp_client/log_{time:%Y-%m-%d}.log", rotation="1 day", encoding="utf-8",
+               level="INFO")
+
+
+class McpClient:
+    def __init__(self):
+        self.customer_resource: [] = None  # 客户资源
+        self.default_system_prompt = None  # mcp_server提供的默认提示词
+        self.available_tools: [] = None  # mcp_server提供的tool
+        self.session: Optional[ClientSession] = None
+        self.exit_stack = AsyncExitStack()
+        active_model = config["model"][config["active"]]
+        self.client = OpenAI(
+            api_key=active_model["api_key"],
+            base_url=active_model["base_url"],
+        )
+        self.model_name = active_model["model_name"]
+        self.connected = False
+
+    async def connect_to_server(self):
+        """
+        连接mcp_server服务
+        """
+        server_params = StdioServerParameters(
+            command=config["server"]["command"],
+            args=config["server"]["args"],
+        )
+        stdio_transport = await self.exit_stack.enter_async_context(stdio_client(server_params))
+        stdio, write = stdio_transport
+        self.session = await self.exit_stack.enter_async_context(ClientSession(stdio, write))
+        await self.session.initialize()
+        self.connected = True
+
+    async def read_mcp(self):
+        """
+        读取mcp服务提供的数据
+        """
+        if self.available_tools is None:
+            await self.get_server_tools()
+        if self.default_system_prompt is None:
+            await self.get_server_prompts()
+        if self.customer_resource is None:
+            await self.get_server_resources()
+
+    async def get_server_tools(self):
+        """
+        获取server提供的tool
+        """
+        mcp_server_tools = await self.session.list_tools()
+        self.available_tools = [ChatCompletionToolParam(
+            type="function",
+            function=FunctionDefinition(
+                name=tool.name,
+                description=tool.description,
+                parameters=tool.inputSchema
+            ),
+        ) for tool in mcp_server_tools.tools]
+        logger.info(f"--> available_tools: {self.available_tools}")
+
+    async def get_server_prompts(self):
+        """
+        获取server提供的prompt
+        """
+        mcp_server_default_system_prompt = await self.session.get_prompt(name="default_system_prompt")
+        self.default_system_prompt = mcp_server_default_system_prompt.messages[0].content.text
+        logger.info(f"--> default_system_prompt: {self.default_system_prompt}")
+
+    async def get_server_resources(self):
+        """
+        获取server提供的resource
+        """
+        # 这里查出所有客户信息，使用时直接取即可，不要通过id去获取客户资源，这样每次都要重新查询
+        mcp_server_customer_resource = await self.session.read_resource(AnyUrl("api://customers"))
+        self.customer_resource = json.loads(mcp_server_customer_resource.contents[0].text)
+        logger.info(f"--> customer_resource: {self.customer_resource}")
+
+    def deal_customer_permission(self, customer_token: str):
+        customer_id = config["customer"].get(customer_token, None)
+        if not customer_id:
+            logger.info(f"Access restricted, customer token[{customer_token}] is not configured!")
+            return "访问受限，客户信息未配置！"
+        if not self.customer_resource:
+            logger.info("No customer resources found!")
+            return "访问受限，客户信息未配置！"
+        if customer_id == "bme":
+            customer_resource = "客户资源如下（如果用户查询没有指定客户，请提示并要求用户传入客户信息）：\n"
+            customer_resource += "\n".join(
+                f"客户id：{data.get('customerId')}，客户名：{data.get('customerName')}，客户全称：{data.get('customerFullname')}"
+                for data in self.customer_resource)
+            return customer_resource
+        else:
+            customer = list(filter(lambda c: c["customerId"] == customer_id, self.customer_resource))
+            if not customer:
+                return "访问受限，客户信息未配置！"
+            return f"""请使用下面的客户信息：
+            客户id：{customer[0]['customerId']}
+            客户名：{customer[0]['customerName']}
+            客户全称：{customer[0]['customerFullname']}"""
+
+    async def process_query(self, param: QueryParam):
+        """
+        处理查询逻辑
+        :param param: 请求参数
+        :return: 经过mcp加工后的ai回答
+        """
+        logger.info(f"--> user origin query: {param}")
+        messages = [
+            {"role": "system", "content": self.default_system_prompt},
+            {"role": "system", "content": self.deal_customer_permission(param.customer_token)},
+            {"role": "system", "content": f"如果要使用到当前时间，请使用{datetime.now()}"},
+            {"role": "user", "content": param.message}
+        ]
+        logger.info(f"--> messages: {messages}")
+        logger.info(f"--> model: {self.model_name}")
+        # 调用ai
+        ai_response = self.client.chat.completions.create(
+            model=self.model_name,
+            messages=messages,
+            tools=self.available_tools
+        )
+        logger.info(f"--> ai response: {ai_response}")
+
+        final_text = []
+        chat_completion_message = ai_response.choices[0].message
+        final_text.append(chat_completion_message.content) if chat_completion_message.content else None
+        # 防止死循环
+        loop = 0
+        # 只要还存在工具调用，就循环下去
+        while chat_completion_message.tool_calls and loop < config["tool_calls_deep"]:
+            loop = loop + 1
+            logger.info(f"----> Available tools: {chat_completion_message.tool_calls}")
+            # 可能ai一次选取了多个工具，这里循环处理
+            for tool_call in chat_completion_message.tool_calls:
+                tool_name = tool_call.function.name
+                tool_args = json.loads(tool_call.function.arguments)
+                logger.info(f"------> start to call tool...")
+                logger.info(f"------> tool_name: {tool_name}, tool_args: {tool_args}")
+
+                result = await self.session.call_tool(tool_name, tool_args)
+                logger.info(f"------> call result: {result}")
+
+                messages.append(ChatCompletionAssistantMessageParam(
+                    role="assistant",
+                    tool_calls=[ChatCompletionMessageToolCallParam(
+                        id=tool_call.id,
+                        type="function",
+                        function=Function(
+                            name=tool_name,
+                            arguments=json.dumps(tool_args)
+                        )
+                    )]
+                ))
+                messages.append(ChatCompletionToolMessageParam(
+                    role="tool",
+                    tool_call_id=tool_call.id,
+                    content=str(result.content)
+                ))
+
+            ai_response = self.client.chat.completions.create(
+                model=self.model_name,
+                messages=messages,
+                tools=self.available_tools
+            )
+            logger.info(f"----> ai response: {ai_response}")
+
+            chat_completion_message = ai_response.choices[0].message
+            final_text.append(chat_completion_message.content) if chat_completion_message.content else None
+        return "\n".join(final_text)
+
+    async def handle_query(self, param: QueryParam):
+        try:
+            return await self.process_query(param)
+        except Exception as e:
+            logger.exception(e)
+
+    async def cleanup(self):
+        """Clean up resources."""
+        await self.exit_stack.aclose()
+
+
+client = McpClient()
+
+
+async def user_query(param: QueryParam):
+    try:
+        if not client.connected:
+            await client.connect_to_server()
+        await client.read_mcp()
+        result = await client.handle_query(param)
+        logger.info(f"Final return: {result}")
+        return result
+    except Exception as e:
+        logger.exception(e)

mcp_server.py

+import requests
+import yaml
+from mcp.server.fastmcp import FastMCP
+
+mcp = FastMCP("BME-MCP")
+
+with open("config.yaml", "r", encoding="utf-8") as yml_file:
+    config = yaml.safe_load(yml_file)
+
+base_url = config["remote"]["base_url"]
+
+bme_screen_service = base_url["bme-screen-service"]
+
+
+def deal_request_exception(response):
+    """
+    处理response的异常信息
+    :param response: 响应体
+    :return: json数据
+    """
+    response.raise_for_status()
+    response_json = response.json()
+    if isinstance(response_json, dict) and "success" in response_json and not response_json["success"]:
+        raise Exception(response_json.get("msg"))
+    return response_json
+
+
+def get_size_limit(size: int, max_size=20, min_size=0) -> int:
+    """
+    处理数据量边界
+    :param size: 数据量
+    :param max_size: 最大边界
+    :param min_size: 最小边界
+    :return:
+    """
+    size = max_size if size > max_size else size
+    size = min_size if size < min_size else size
+    return size
+
+
+@mcp.tool()
+def get_air_pm_10_month_focus(customer_id: int) -> []:
+    """
+    根据客户id获取本月重点关注的空气质量微站（PM10标准）
+    :param customer_id: 客户id
+    :return: 返回结构中的字段名解释：
+        rank：排行,
+        deviceNo：设备编号，
+        deviceName：设备名称
+    """
+    response = requests.get(bme_screen_service + "/monitor/getMonitorRankingTitle", {
+        "customerId": customer_id,
+        "type": "1",  # 随便传，该参数当前无用
+        "order": "1",  # 随便传，该参数当前无用
+    })
+    response_data = deal_request_exception(response).get("data", {}).get("title", [])
+    return [{
+        "rank": data.get("rank"),
+        "deviceNo": data.get("deviceNo"),
+        "deviceName": data.get("deviceName")
+    } for data in response_data]
+
+
+@mcp.tool()
+def get_air_pm_rank(customer_id: int, order: int=1, statistic_type: int=1) -> []:
+    """
+    根据客户id获取空气质量微站排行（包含PM2.5和PM10标准）
+    :param customer_id: 客户id
+    :param order: 排序规则：按统计区间的类型进行排序，升序排序传1，降序排序传2，默认为1
+    :param statistic_type: 统计区间：按小时统计传1，按日统计传2，要按月统计传3，默认为1
+    :return: 返回结构中的字段名解释：
+        PM10：PM10的数据集，
+        PM2.5：PM2.5的数据集，
+        avgValue：平均值 单位为μg/m³，
+        deviceNo：设备编号，
+        deviceName：设备名称，
+    """
+    response = requests.get(bme_screen_service + "/monitor/getMonitorRanking", {
+        "customerId": customer_id,
+        "type": statistic_type,
+        "order": order,
+    })
+    response_data = deal_request_exception(response).get("data", {})
+    result = {
+        "PM10": [{
+            "avgValue": pm10.get("avg_value"),
+            "deviceNo": pm10.get("deviceNo"),
+            "deviceName": pm10.get("deviceName")
+        } for pm10 in response_data.get("pm10lists")],
+        "PM2.5": [{
+            "avgValue": pm2_5.get("avg_value"),
+            "deviceNo": pm2_5.get("deviceNo"),
+            "deviceName": pm2_5.get("deviceName")
+        } for pm2_5 in response_data.get("pm25lists")],
+    }
+    return result
+
+
+@mcp.tool()
+def get_tsp_rank(customer_id: int, order: int=1, statistic_type: int=1, size: int=10) -> []:
+    """
+    根据客户id获取TSP监测排行
+    :param customer_id: 客户id
+    :param order: 排序规则：按统计区间的类型进行排序，升序排序传1，降序排序传2，默认为1
+    :param statistic_type: 统计区间：按小时统计传1，按日统计传2，要按月统计传3，默认为1
+    :param size: 要取多少条数据，比如按小时升序取前两条，则传2，默认为10
+    :return: 返回结构中的字段名解释：
+            total: 区域总数
+            data: 详细数据
+            data->area: 区域名称
+            data->devicecount: 关联设备数
+            data->realtimePollutionIndex: 实时污染指数
+            data->hourRank: 小时排名
+            data->dayRank: 日排名
+            data->monthRank: 月排名
+    """
+    response = requests.get(bme_screen_service + "/monitor/getMonitorTSPRanking", {
+        "customerId": customer_id,
+        "order": order,
+        "pageNo": 1,
+        "pageSize": get_size_limit(size),
+        "type": statistic_type,
+    })
+    response_data = deal_request_exception(response).get("data", {})
+    result = {
+        "total": response_data.get("areaCount", 0),
+        "data": [{
+            "area": data.get("area"),
+            "devicecount": data.get("devicecount"),
+            "realtimePollutionIndex": f"{data.get('tdc')} {data.get('tspUnit')}",
+            "hourRank": data.get("hourRank"),
+            "dayRank": data.get("dayRank"),
+            "monthRank": data.get("monthRank"),
+        } for data in response_data.get("page", {}).get("records")],
+    }
+    return result
+
+
+@mcp.tool()
+def get_governance_process_statistics(customer_id: int) -> {}:
+    """
+    根据客户id获取今天治理过程记录统计数据
+    :param customer_id: 客户id
+    :return: 返回结构中的字段名解释：
+        timeCount: 今日雾炮平均开启时长（小时）
+        allCount: 今日污染发生次数（次）
+        openCount: 今日雾炮开启次数（次）
+    """
+    response = requests.get(bme_screen_service + "/eq/selectEquCount", {"customerId": customer_id})
+    response_data = deal_request_exception(response).get("data", {})
+    result = {
+        "timeCount": response_data.get("timeCount", 0),
+        "allCount": response_data.get("allCount", 0),
+        "openCount": response_data.get("openCount", 0),
+    }
+    return result
+
+
+@mcp.tool()
+def get_governance_process_records(customer_id: int, instruct_type: str, device_name: str, start_time: str,
+                                   end_time: str, size: int) -> []:
+    """
+    根据客户id查询治理过程全记录列表
+    :param customer_id: 客户id
+    :param instruct_type: 触发机制：智能联动为0，手动开启为1，全部为空
+    :param device_name: 设备名称
+    :param start_time: 开始时间，格式为yyyy-MM-dd HH:mm:ss，如：2025-04-27 17:30:00，默认为七天前的时间
+    :param end_time: 结束时间，格式为yyyy-MM-dd HH:mm:ss，如：2025-04-27 17:30:00，默认为当前时间
+    :param size: 查询数量，默认10
+    :return: 返回结构中的字段名解释：
+        total：总数（由于该接口是分页查询，所以这里返回总数）
+        data：数据集
+        data->createTime: 时间
+        data->deviceName: 设备
+        data->position: 位置
+        data->instructType: 触发机制，0为智能联动，1为手动开启
+        data->describe: 治理过程描述
+    """
+    response = requests.get(bme_screen_service + "/eq/selectEquPage", {
+        "customerId": customer_id,
+        "InstructType": instruct_type,
+        "deviceName": device_name,
+        "StatcDate": start_time,
+        "StopDate": end_time,
+        "pageNo": 1,
+        "pageSize": get_size_limit(size),
+    })
+    response_data = deal_request_exception(response).get("data", {})
+    result = {
+        "total": response_data.get("total", 0),
+        "data": [{
+            "createTime": data.get("createTime"),
+            "deviceName": data.get("deviceName"),
+            "position": data.get("position"),
+            "instructType": data.get("instructType"),
+            "describe": data.get("describe"),
+        } for data in response_data.get("records")],
+    }
+    return result
+
+
+@mcp.resource("api://customers")
+def get_all_available_customer() -> []:
+    """
+    获取所有的客户信息
+    :return: 客户信息数据
+    """
+    response = requests.get(bme_screen_service + "/reportgen/customer")
+    response_data = deal_request_exception(response).get("data", [])
+    return [{
+        "customerId": data.get("customerId"),
+        "customerName": data.get("customerName"),
+        "customerFullname": data.get("customerFullname")
+    } for data in response_data]
+
+
+@mcp.prompt(name="default_system_prompt")
+def get_default_system_prompt() -> str:
+    """
+    默认的系统提示词
+    :return: 默认的系统提示词
+    """
+    return (
+        "你可以结合一系列的工具（tool）来回答用户的问题。\n"
+        "以下是你应该始终遵循的规则：\n"
+        "1.始终传入类型正确的参数，如果从输入中没有解析到参数，则取工具对应参数描述的默认值，如果也没有默认值，则结束流程并告知用户信息不全。\n"
+        "2.只在需要时调用工具，如果你不需要额外信息，不要调用搜索代理，尽量自己解决任务。\n"
+        "3.如果不需要调用工具，直接回答问题即可。\n"
+        "4.永远不要用完全相同的参数重新进行之前的工具调用。\n"
+        "5.如果要使用到客户信息，当用名字查找时，优先匹配客户全称，其次匹配客户名，再考虑使用部分匹配，最后考虑读音相近的名字，都没找到则结束流程并告知用户客户不存在。\n"
+    )
+
+
+if __name__ == '__main__':
+    mcp.run(transport="stdio")

pyproject.toml

+[project]
+name = "bme-mcp"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.13"
+dependencies = [
+    "mcp[cli]>=1.6.0",
+]

uv.lock

+version = 1
+revision = 2
+requires-python = ">=3.13"
+
+[[package]]
+name = "annotated-types"
+version = "0.7.0"
+source = { registry = "https://pypi.org/simple" }
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