1
{
2
  "mcpServers": {
3
    "filesystem": {
4
      "command": "npx",
5
      "args": [
6
        "-y",
7
        "@modelcontextprotocol/server-filesystem",
8
        "/Users/yourusername/Documents",
9
        "/Users/yourusername/Desktop"
10
      ]
11
    }
12
  }
13
}

1
请读取我桌面上 todo.txt 的内容，并告诉我今天要做什么。

1
<dependency>
2
    <groupId>io.openliberty.beta</groupId>
3
    <artifactId>openliberty-runtime</artifactId>
4
    <version>26.0.0.3-beta</version>
5
    <type>zip</type>
6
</dependency>

1
import io.openliberty.mcp.server.Tool;
2
import io.openliberty.mcp.server.ToolArg;
3
import io.openliberty.mcp.server.RequestId;
4

5
@ApplicationScoped
6
public class SearchService {
7

8
    @Tool(description = "搜索数据存储")
9
    public SearchResult search(
10
            @ToolArg(name = "query", description = "搜索关键词") String query,
11
            RequestId requestId) {  // 自动注入请求ID，用于日志审计
12

13
        // 日志记录，便于追踪
14
        logger.info("Search request " + requestId.asString() + ": " + query);
15

16
        SearchResult result = datastore.runQuery(query);
17
        return result;
18
    }
19
}

1
@ApplicationScoped
2
public class SearchResultEncoder implements ToolResponseEncoder<SearchResult> {
3

4
    @Override
5
    public boolean supports(Class<?> runtimeType) {
6
        return SearchResult.class.isAssignableFrom(runtimeType);
7
    }
8

9
    @Override
10
    public ToolResponse encode(SearchResult searchResult) {
11
        if (searchResult.results().isEmpty()) {
12
            return ToolResponse.error("未找到结果");
13
        }
14

15
        List<TextContent> contents = new ArrayList<>();
16
        for (var result : searchResult.results()) {
17
            // 根据相关性设置优先级注释
18
            Annotations annotations = new Annotations(null, null, result.relevance());
19
            contents.add(new TextContent(result.summary(), null, annotations));
20
        }
21

22
        return ToolResponse.success(contents);
23
    }
24
}

1
<featureManager>
2
    <feature>mcpServer-1.0</feature>
3
</featureManager>
4

5
<mcpServer endpoint="/mcp"/>

1
# 安装 Claude Code
2
npm install -g @anthropic-ai/claude-code
3

4
# 安装 pptx skill
5
claude skills add pptx
6

7
# 使用 skill 创建 PPT
8
claude "用 pptx skill 创建一个关于 MCP 的演示文稿"

1
using Microsoft.SemanticKernel;
2

3
public class EmailPlugin
4
{
5
    [KernelFunction]
6
    [Description("发送电子邮件")]
7
    public async Task<string> SendEmailAsync(
8
        [Description("收件人地址")] string to,
9
        [Description("邮件主题")] string subject,
10
        [Description("邮件正文")] string body)
11
    {
12
        // 可在此调用 MCP 邮件服务器
13
        await emailService.SendAsync(to, subject, body);
14
        return $"邮件已发送至 {to}";
15
    }
16
}
17

18
// 注册并使用
19
var kernel = Kernel.CreateBuilder()
20
    .AddOpenAIChatCompletion("gpt-4", apiKey)
21
    .Build();
22

23
kernel.Plugins.AddFromType<EmailPlugin>("Email");
24

25
// 自然语言触发
26
await kernel.InvokePromptAsync("请给 zhangsan@example.com 发送问候邮件");

1
from langchain.tools import tool
2
from langchain.agents import create_react_agent, AgentExecutor
3

4
@tool
5
def send_email(to: str, subject: str, body: str) -> str:
6
    """发送电子邮件。收件人、主题、正文必填。"""
7
    # 调用 MCP Server
8
    return f"邮件已发送至 {to}"
9

10
@tool
11
def get_weather(city: str) -> str:
12
    """获取城市天气。"""
13
    return f"{city}：晴朗 25°C"
14

15
tools = [send_email, get_weather]
16
agent = create_react_agent(llm, tools, prompt)
17
agent_executor = AgentExecutor(agent=agent, tools=tools)
18

19
agent_executor.invoke({"input": "查北京天气并发邮件告诉老板"})

1
from mcp.server import Server
2
import mcp.server.stdio
3
import mcp.types as types
4
import asyncio
5
import json
6

7
server = Server("db-server")
8

9
# 模拟数据库
10
MOCK_TASKS = [
11
    {"id": 1, "title": "完成需求文档", "status": "done", "date": "2026-03-27"},
12
    {"id": 2, "title": "修复登录 Bug", "status": "done", "date": "2026-03-26"},
13
    {"id": 3, "title": "性能优化", "status": "in_progress", "date": "2026-03-28"},
14
    {"id": 4, "title": "部署上线", "status": "pending", "date": "2026-03-29"},
15
]
16

17
@server.list_tools()
18
async def list_tools():
19
    return [
20
        types.Tool(
21
            name="get_weekly_tasks",
22
            description="获取本周（最近7天）的任务列表",
23
            inputSchema={
24
                "type": "object",
25
                "properties": {
26
                    "start_date": {"type": "string", "description": "开始日期"},
27
                    "end_date": {"type": "string", "description": "结束日期"}
28
                },
29
            },
30
        )
31
    ]
32

33
@server.call_tool()
34
async def call_tool(name: str, arguments: dict | None):
35
    if name == "get_weekly_tasks":
36
        # 可添加日期过滤逻辑
37
        return [types.TextContent(type="text", text=json.dumps(MOCK_TASKS))]
38
    raise ValueError(f"Unknown tool: {name}")
39

40
async def main():
41
    async with mcp.server.stdio.stdio_server() as (read_stream, write_stream):
42
        await server.run(read_stream, write_stream, ...)

1
# chart_server.py - 简化示例
2
@server.list_tools()
3
async def list_tools():
4
    return [
5
        types.Tool(
6
            name="create_bar_chart",
7
            description="根据数据创建柱状图",
8
            inputSchema={
9
                "type": "object",
10
                "properties": {
11
                    "data": {"type": "object", "description": "图表数据"},
12
                    "title": {"type": "string", "description": "图表标题"}
13
                },
14
                "required": ["data"]
15
            },
16
        )
17
    ]
18

19
@server.call_tool()
20
async def call_tool(name: str, arguments: dict | None):
21
    if name == "create_bar_chart":
22
        # 生成图表并返回 base64 图片
23
        chart_base64 = generate_chart(arguments["data"], arguments.get("title"))
24
        return [types.ImageContent(type="image", data=chart_base64, mimeType="image/png")]

1
<!DOCTYPE html>
2
<html>
3
<head>
4
    <title>周报预览</title>
5
    <script src="https://cdn.jsdelivr.net/npm/chart.js"></script>
6
</head>
7
<body>
8
    <div id="app">
9
        <h2>周报预览</h2>
10

11
        <div id="summary-section">
12
            <h3>摘要</h3>
13
            <div id="summary" contenteditable="true"></div>
14
        </div>
15

16
        <div id="chart-section">
17
            <canvas id="statusChart"></canvas>
18
        </div>
19

20
        <div id="tasks-section">
21
            <h3>任务列表</h3>
22
            <div id="tasks"></div>
23
        </div>
24

25
        <div class="actions">
26
            <button id="edit-btn">编辑</button>
27
            <button id="send-btn">发送</button>
28
            <button id="cancel-btn">取消</button>
29
        </div>
30
    </div>
31

32
    <script type="module">
33
        import { App } from '@modelcontextprotocol/ext-apps';
34

35
        const app = new App();
36

37
        // 监听工具结果
38
        app.on('toolresult', async (data) => {
39
            // 渲染预览界面
40
            renderPreview(data.result);
41
        });
42

43
        // 发送按钮
44
        document.getElementById('send-btn').onclick = async () => {
45
            // 调用 MCP 工具发送邮件
46
            const result = await app.callServerTool('email-server', 'send_email', {
47
                to: document.getElementById('recipient').value,
48
                body: document.getElementById('summary').innerText
49
            });
50
            app.updateContext('邮件已发送');
51
        };
52

53
        function renderPreview(reportData) {
54
            document.getElementById('summary').innerText = reportData.summary;
55
            // 渲染图表
56
            new Chart(document.getElementById('statusChart'), {
57
                type: 'bar',
58
                data: reportData.chartData
59
            });
60
        }
61
    </script>
62
</body>
63
</html>

1
@server.call_tool()
2
async def call_tool(name: str, arguments: dict):
3
    if name == "generate_report_preview":
4
        # 生成周报数据
5
        report_data = await generate_report_data()
6

7
        # 返回带有 UI 资源的结果
8
        return [
9
            types.TextContent(
10
                type="text",
11
                text=json.dumps(report_data),
12
                _meta={
13
                    "ui": {
14
                        "resourceUri": "ui://weekly-report/preview",
15
                        "data": report_data
16
                    }
17
                }
18
            )
19
        ]