01
import { McpServer } from '@modelcontextprotocol/sdk/server/mcp.js'
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import { StdioServerTransport } from '@modelcontextprotocol/sdk/server/stdio.js'
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import { z } from 'zod'
04

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const server = new McpServer({
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  name: 'ai-companion',
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  version: '1.0.0',
08
})
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// 声明一个工具
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server.tool(
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  'greet',
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  '根据情绪返回一句问候',
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  {
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    emotion: z.enum(['happy', 'sad', 'neutral']),
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  },
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  async ({ emotion }) => {
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    const greetings = {
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      happy: '今天的你真有活力 ☀️',
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      sad: '没事的，我在这里陪你 🌧️',
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      neutral: '又见面啦，今天过得怎么样？',
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    }
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    return {
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      content: [{ type: 'text', text: greetings[emotion] }],
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    }
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  },
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)
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// 启动
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const transport = new StdioServerTransport()
31
await server.connect(transport)

01
import { Hono } from 'hono'
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import { McpAgent } from '@cloudflare/mcp-agent'
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import { z } from 'zod'
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import type { Env } from './bindings'
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// 定义 MCP Agent（就是一个 Durable Object）
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export class CompanionMCP extends McpAgent<Env> {
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  server = new McpServer({
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    name: 'ai-companion',
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    version: '1.0.0',
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  })
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  async init() {
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    // 工具：检索记忆
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    this.server.tool(
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      'search_memories',
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      '检索指定用户的记忆，支持语义相似度查询',
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      {
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        userId: z.string().describe('用户 ID'),
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        query: z.string().describe('检索关键词'),
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        topK: z.number().int().min(1).max(10).default(5),
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      },
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      async ({ userId, query, topK }) => {
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        const embedding = await embed(this.env, query)
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        const results = await this.env.VECTORIZE.query(embedding, {
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          topK,
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          filter: { userId },
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        })
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        return {
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          content: [
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            { type: 'text', text: JSON.stringify(results.matches, null, 2) },
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          ],
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        }
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      },
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    )
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    // 工具：分析情绪轨迹
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    this.server.tool(
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      'emotion_trend',
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      '分析指定时间范围内用户的情绪变化趋势',
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      {
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        userId: z.string(),
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        days: z.number().int().min(1).max(30).default(7),
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      },
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      async ({ userId, days }) => {
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        const since = Date.now() - days * 86400_000
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        const rows = await this.env.DB.prepare(
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          'SELECT emotion, intensity, created_at FROM emotion_logs WHERE user_id=? AND created_at>? ORDER BY created_at',
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        ).bind(userId, since).all()
50

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        return {
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          content: [
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            { type: 'text', text: JSON.stringify(rows.results, null, 2) },
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          ],
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        }
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      },
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    )
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    // 工具：关系画像
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    this.server.tool(
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      'relationship_profile',
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      '获取用户和伴侣的关系画像：亲密度、话题偏好、性格标签',
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      {
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        userId: z.string(),
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      },
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      async ({ userId }) => {
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        const profile = await this.env.DB.prepare(
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          'SELECT intimacy, tags, topic_preferences FROM user_profiles WHERE id=?',
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        ).bind(userId).first()
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        return {
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          content: [
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            { type: 'text', text: JSON.stringify(profile, null, 2) },
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          ],
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        }
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      },
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    )
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  }
78
}
79

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// HTTP 入口
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const app = new Hono<{ Bindings: Env }>()
82

83
app.route('/mcp', CompanionMCP.serve('/mcp'))
84

85
export default app

01
app.use('/mcp/*', async (c, next) => {
02
  const auth = c.req.header('authorization')
03
  if (!auth?.startsWith('Bearer ')) {
04
    return c.json({ error: 'unauthorized' }, 401)
05
  }
06

07
  const token = auth.slice(7)
08
  const tokenInfo = await c.env.KV.get(`mcp-token:${token}`, 'json')
09
  if (!tokenInfo) {
10
    return c.json({ error: 'invalid token' }, 401)
11
  }
12

13
  c.set('userId', tokenInfo.userId)
14
  await next()
15
})

01
this.server.tool('search_memories',
02
  '检索当前用户的记忆',
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  {
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    query: z.string(),
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    topK: z.number().int().min(1).max(10).default(5),
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  },
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  async ({ query, topK }) => {
08
    const userId = this.ctx.userId  // 从认证上下文拿
09
    // ...
10
  },
11
)

1
export class CompanionMCP extends McpAgent<Env> {
2
  static oAuth = {
3
    authorizeEndpoint: '/oauth/authorize',
4
    tokenEndpoint: '/oauth/token',
5
    scopes: ['memories:read', 'emotions:read'],
6
  }
7
  // ...
8
}

01
import { experimental_createMCPClient as createMCPClient } from 'ai'
02
import { StreamableHTTPClientTransport } from '@modelcontextprotocol/sdk/client/streamableHttp.js'
03

04
const client = await createMCPClient({
05
  transport: new StreamableHTTPClientTransport(
06
    new URL('https://companion-mcp.yourdomain.com/mcp'),
07
    {
08
      fetch: (url, init) =>
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        fetch(url, {
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          ...init,
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          headers: {
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            ...init?.headers,
13
            Authorization: `Bearer ${COMPANION_MCP_TOKEN}`,
14
          },
15
        }),
16
    },
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  ),
18
})
19

20
const tools = await client.tools()
21
// tools 里有 search_memories / emotion_trend / relationship_profile

1
this.server.tool(
2
  'search_memories',
3
  [
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    '检索与指定查询最相关的用户记忆。',
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    '适用场景：用户提到过去的事、想知道自己之前和伴侣说过什么、做情感复盘。',
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    '返回按相似度排序的前 topK 条记忆，每条包含内容、时间戳、重要性评分。',
7
  ].join('\n'),
8
  // ...
9
)

01
app.use('/mcp/*', async (c, next) => {
02
  const start = Date.now()
03
  const userId = c.get('userId')
04

05
  await next()
06

07
  const duration = Date.now() - start
08
  c.executionCtx.waitUntil(
09
    logMcpCall(c.env, {
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      userId,
11
      path: c.req.path,
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      status: c.res.status,
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      duration,
14
      timestamp: Date.now(),
15
    }),
16
  )
17
})

01
// 伴侣主接口
02
app.post('/chat', async (c) => {
03
  const mcpClient = await createMCPClient({
04
    transport: new StreamableHTTPClientTransport(
05
      new URL('https://companion-mcp.yourdomain.com/mcp'),
06
      {
07
        fetch: (url, init) =>
08
          fetch(url, {
09
            ...init,
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            headers: { ...init?.headers, Authorization: `Bearer ${internalMcpToken(c.env)}` },
11
          }),
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      },
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    ),
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  })
15

16
  const mcpTools = await mcpClient.tools()
17

18
  const result = streamText({
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    model: models.chat,
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    messages,
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    tools: mcpTools,  // 自家的 MCP 工具
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    onFinish: () => c.executionCtx.waitUntil(mcpClient.close()),
23
  })
24

25
  return result.toUIMessageStreamResponse()
26
})