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Metaso MCP Server

Metaso MCP Server

by csrts

Metaso MCP Server

基于Metaso AI搜索引擎的MCP (Model Context Protocol) Server,为大语言模型提供AI搜索、网页内容读取和AI问答功能。

功能特性

  • 🔍 AI搜索: 支持网页、文档、学术、图片、视频、播客等多种搜索范围

  • 📖 网页读取: 提取网页完整内容,支持Markdown和JSON格式输出

  • 💬 AI问答: 基于搜索增强的智能问答,支持多种AI模型

  • 🔒 安全验证: URL安全检查、API密钥验证、输入长度限制

  • 🚀 高性能: 内置重试机制、连接池、错误处理

Related MCP server: ORZ MCP

安装

从源码构建

# 克隆项目
git clone https://github.com/csrts/metaso-mcp
cd metaso-mcp

# 安装依赖
npm install

# 构建项目
npm run build

配置

环境变量

变量名

必需

默认值

说明

METASO_API_KEY

✅

-

Metaso API密钥(格式:mk-[32位字符])

METASO_BASE_URL

❌

https://metaso.cn

API基础URL

METASO_TIMEOUT

❌

30000

请求超时时间(毫秒)

METASO_DEBUG

❌

false

启用调试日志

获取API密钥

  1. 访问 Metaso官网

  2. 注册账户并获取API密钥

  3. 设置环境变量:

    export METASO_API_KEY="mk-YOUR_API_KEY_HERE"

MCP客户端配置

详情请看->MCP-CONFIG

Claude Desktop

在Claude Desktop的配置文件中添加:

{
  "mcpServers": {
    "metaso": {
      "command": "node",
      "args": ["/path/to/metaso-mcp-server/dist/index.js"],
      "env": {
        "METASO_API_KEY": "mk-YOUR_API_KEY_HERE"
      }
    }
  }
}

开发模式

{
  "mcpServers": {
    "metaso": {
      "command": "npx",
      "args": ["-y", "tsx", "/path/to/metaso-mcp-server/src/index.ts"],
      "env": {
        "METASO_API_KEY": "mk-YOUR_API_KEY_HERE",
        "METASO_DEBUG": "true"
      }
    }
  }
}

可用工具

1. metaso_search - AI搜索

搜索互联网内容,支持多种搜索范围。

参数:

  • query (必需): 搜索查询内容

  • scope (可选): 搜索范围,支持 webpage、document、scholar、image、video、podcast

  • page (可选): 页数(与size互斥)

  • size (可选): 结果数量(与page互斥)

  • include_summary (可选): 是否包含AI摘要(默认true)

  • include_row_content (可选): 是否包含原文内容(默认false)

示例:

{
  "query": "人工智能最新发展",
  "scope": "scholar",
  "size": 10,
  "include_summary": true
}

2. metaso_reader - 网页读取

读取指定网页的完整内容。

参数:

  • url (必需): 要读取的网页URL

  • format (可选): 返回格式,支持 markdown、json(默认markdown)

示例:

{
  "url": "https://example.com/article",
  "format": "markdown"
}

3. metaso_chat - AI问答

与Metaso AI助手对话,获得基于搜索增强的智能回答。

参数:

  • query (必需): 问题或提示内容

  • model (可选): AI模型,支持 fast、fast_thinking、ds-r1(默认fast)

  • scope (可选): 搜索范围,支持 document、scholar、video、podcast

  • format (可选): 响应格式,支持 chat_completions、simple(默认chat_completions)

  • stream (可选): 启用流式输出(默认false)

示例:

{
  "query": "解释量子计算的基本原理",
  "model": "ds-r1",
  "scope": "scholar"
}

开发

项目结构

metaso-mcp/
├── src/
│   ├── index.ts                 # 主入口文件
│   ├── server.ts                # MCP服务器实现
│   ├── tools/                   # 工具实现
│   │   ├── search.ts            # 搜索工具
│   │   ├── reader.ts            # 读取工具
│   │   └── chat.ts              # 问答工具
│   ├── types/                   # 类型定义
│   │   └── requests.ts          # 请求和响应类型
│   └── utils/                   # 工具函数
│       ├── config.ts            # 配置管理
│       └── http-client.ts       # HTTP客户端
├── tests/                       # 测试文件
└── dist/                        # 编译输出

开发命令

# 开发模式运行
npm run dev

# 构建项目
npm run build

运行测试

# 运行所有测试
npm test

# 运行特定测试
npm test -- search.test.ts

# 监视模式
npm run test:watch

故障排除

常见错误

  1. 配置验证失败

    Configuration validation failed: apiKey: Required

    解决方案: 确保设置了METASO_API_KEY环境变量

  2. API密钥格式错误

    Invalid API key format. Expected format: mk-[32 alphanumeric characters]

    解决方案: 检查API密钥格式是否正确

  3. 网络连接错误

    Network error: Unable to connect to Metaso API

    解决方案: 检查网络连接和API基础URL设置

调试模式

启用调试日志以获取更多信息:

export METASO_DEBUG=true

安全性

  • 🔐 API密钥通过环境变量安全管理

  • 🚫 阻止访问私有IP和localhost

  • 📏 查询内容长度限制

  • 🔍 URL安全性验证

  • 🚨 敏感信息日志屏蔽

许可证

MIT License

贡献

欢迎提交Issue和Pull Request!

更新日志

v1.0.0

  • 初始版本发布

  • 支持AI搜索、网页读取、AI问答功能

  • 完整的MCP协议兼容性

  • 安全性和错误处理

Available Tools

3 tools
metaso_chatC

Chat with Metaso AI assistant. Get intelligent responses based on search-enhanced AI models. Supports multiple models and output formats.

Inputs: query (required), model, scope, format, +1 more.

metaso_readerA

Read and extract content from web pages using Metaso API. Returns the full text content of the specified webpage in markdown or JSON format.

Inputs: url (required), format.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    TDQS

    A3.7/5.0

    Scored across 3 tools

    Disambiguation5/5

    Each tool has a clearly distinct purpose: chat for conversational AI, reader for extracting web content, and search for internet search. No overlap or ambiguity.

    Naming Consistency5/5

    All tools follow a consistent 'metaso_<action>' pattern with clear verb/noun naming: chat, reader, search. Perfectly predictable.

    Tool Count5/5

    Three tools is well-scoped for an AI assistant server, each serving a core function without unnecessary bloat or gaps.

    Completeness5/5

    The tool surface covers the essential operations: conversational query, web reading, and search. No obvious missing functionality for its domain.

    Maintenance

    ActivityInactive
    ResponsivenessNo recent community issues

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