MCP是什么？

传统Function Calling是串行的：

模型 → 调1个工具 → 等结果 → 调下1个工具...（串行）

MCP让模型一次性看到所有可用工具：

// 服务端暴露工具列表（JSON Schema）
{
  "tools": [
    {"name": "filesystem_read", "params": {"path": "string"}},
    {"name": "database_query", "params": {"sql": "string"}},
    {"name": "github_pr", "params": {"title": "string", "body": "string"}}
  ]
}
// 客户端：Claude Code、Cline、Continue.dev均支持MCP

MonkeyCode集成MCP配置

// ~/.monkeycode/mcp_servers.json
{
  "mcpServers": {
    "filesystem": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-filesystem", "/home/user/workspace"],
      "env": {}
    },
    "database": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-postgres"],
      "env": {"DATABASE_URL": "postgresql://user:pwd@localhost:5432/db"}
    },
    "github": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-github"],
      "env": {"GITHUB_TOKEN": "ghp_your_token"}
    }
  }
}

# 在MonkeyCode中使用
from monkeycode import MonkeyCode

mc = MonkeyCode(
    model="minimax/m3",
    mcp_config="~/.monkeycode/mcp_servers.json"
)

# 列出所有可用工具
print(mc.list_tools())
# 输出: [filesystem/read_file, database/query, github/create_pr, ...]

Agent实战：一条指令完成复杂任务

场景：修复Bug → 提交PR → 部署上线

# 一条指令，M3自动完成：
# 1. 读取CI失败日志
# 2. 分析代码找Bug
# 3. 自动修复
# 4. 写测试验证
# 5. 提交GitHub PR
# 6. 合并后自动部署

result = mc.execute(
    task="修复CI失败的测试，并在GitHub上创建PR然后部署到staging环境",
    context={
        "ci_log_url": "https://github.com/org/repo/actions/runs/12345",
        "deployment_target": "staging"
    },
    mode="thinking"
)

print(result.chain)
# [
#   {"step": 1, "tool": "filesystem/read_file", "input": "logs/ci.log", "output": "Test failed at test_auth.py:23"},
#   {"step": 2, "tool": "analyze", "input": "...", "output": "Fix: add token refresh logic"},
#   {"step": 3, "tool": "filesystem/write_file", "input": "auth.py patch", "output": "OK"},
#   {"step": 4, "tool": "execute", "input": "pytest", "output": "3 tests passed"},
#   {"step": 5, "tool": "github/create_pr", "input": "...", "output": "PR #234 created"},
#   {"step": 6, "tool": "deploy", "input": "...", "output": "Deployed to staging ✅"}
# ]

为什么M3适合做Agent？

常用MCP工具组合

// 添加更多MCP服务器
{
  "mcpServers": {
    "docker": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-docker"]
    },
    "http": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-http"]
    }
  }
}

M3的Tool Calling vs GPT/Claude