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@mcpilotx/core

v0.8.0

Published

Intent-Driven MCP Orchestration Toolkit - Transform natural language into executable workflows with AI-powered intent parsing and MCP tool orchestration

Readme

@mcpilotx/core

npm version License TypeScript Coverage

English | [简体中文]

@mcpilotx/core 是一款为 Model Context Protocol (MCP) 生态系统量身定制的高性能、意图驱动的编排引擎。它能够将模糊的自然语言指令转化为精确、可执行且具高鲁棒性的工具调用流。


🚀 为什么选择 IntentOrch?

当前的 LLM 工具调用往往面临两个痛点:“过度分解”(在冗余步骤上浪费 Token)和“参数契约不匹配”(不同 MCP 服务器之间的命名冲突)。IntentOrch 通过专业的编排层完美解决了这些问题。

💎 核心价值

  • 最小化分解原则 (Minimal Decomposition Principle):智能前置分析,确保生成最简执行路径。有效拒绝 LLM 的“过度思考”,大幅降低 Token 损耗和执行延迟。
  • 自适应参数映射 (Adaptive Parameter Mapping):基于语义的参数对齐引擎。自动桥接 LLM 推理结果与异构 MCP 工具 Schema 之间的鸿沟(例如:自动将 filename 映射为 path)。
  • 鲁棒性 DAG 引擎:基于有向无环图 (DAG) 的任务执行模型,内置拓扑排序、依赖分析和多级错误恢复机制。
  • 混合意图解析架构:结合超快速的正则启发式逻辑与深度 LLM 推理,实现性能与精准度的最优平衡。

📦 安装

npm install @mcpilotx/core

⚡ 快速上手

仅需数行代码,即可实现跨工具的复杂意图编排。

import { createSDK } from '@mcpilotx/core';

const sdk = createSDK();

// 1. 配置您的 AI 大模型(支持 DeepSeek, OpenAI, Ollama 等)
await sdk.configureAI({
  provider: 'deepseek', 
  apiKey: process.env.DEEPSEEK_API_KEY,
  model: 'deepseek-chat'
});

// 2. 连接多个 MCP 服务器
await sdk.connectMCPServer({
  name: 'github',
  transport: { type: 'stdio', command: 'npx', args: ['-y', '@modelcontextprotocol/server-github'] }
});

await sdk.connectMCPServer({
  name: 'dingtalk', // 钉钉通知
  transport: { type: 'stdio', command: 'npx', args: ['-y', 'mcp-server-dingtalk'] }
});

// 3. 初始化 CloudIntentEngine
await sdk.initCloudIntentEngine();

// 4. 执行复杂意图编排
const result = await sdk.executeWorkflowWithTracking(
  "分析 mcpilotx/intentorch 仓库的最新 PR,并生成一份报告通过钉钉发送给开发群"
);

console.log('工作流执行结果:', result.success);

🛠 架构概览

IntentOrch 作为智能中间件,位于您的应用与 MCP 生态系统之间:

graph TD
    User([用户意图]) --> Selector{解析策略选择}
    Selector -->|简单意图| Rule[正则解析器]
    Selector -->|复杂任务| Cloud[云端 LLM 引擎]
    
    Cloud --> MinDec[最小化分解过滤]
    MinDec --> DAG[DAG 工作流生成]
    
    DAG --> ParamMap[自适应参数映射]
    ParamMap --> Validator[执行前置校验]
    
    Validator --> MCP[MCP 工具执行]
    MCP --> Result([最终输出结果])

🌟 进阶特性

🧩 交互式反馈

当系统对意图解析的置信度较低时,IntentOrch 会自动暂停并请求用户确认,确保高风险操作的安全性。

📝 @intentorch 指令系统

支持在自然语言中通过指令直接干预编排逻辑:

  • 查询日志 @intentorch summary -> 自动在工作流末尾追加一个 AI 摘要生成步骤。

🛡️ 生产级保障

内置 RetryManager(重试管理)、FallbackManager(降级管理)和 PerformanceMonitor(性能监控),确保您的 AI 工作流具备生产级的稳定性。


📄 许可证

基于 Apache-2.0 许可证。详见 LICENSE


🤝 参与贡献

我们非常欢迎社区贡献!如有任何想法或建议,请提交 Pull Request。


MCPilotX 倾心打造 ❤️