Provides installation and distribution capabilities for the TongXiao Common Search MCP Server through the npm package registry, allowing users to easily install and run the server using npm or npx commands.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@TongXiao Common Search MCP Server search for the latest AI developments in quantum computing"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
A Model Context Protocol (MCP) server implementation that integrates with IQS APIs, which delivers clean, accurate, diverse, and high-quality results through multiple data sources. For more Information about TongXiao, please visit our documents.
npm install -g @tongxiao/common-search-mcp-server# run stdio server
env TONGXIAO_API_KEY=your-api-key npx -y @tongxiao/common-search-mcp-server# run sse server
env TONGXIAO_API_KEY=your-api-key SERVER=sse npx -y @tongxiao/common-search-mcp-serverYou can find your apikey from ipaas.console.aliyun.com/api-key
Configure Tongxiao MCP Server directly on mainstream MCP Client.
{
"mcpServers": {
"tongxiao-common-search": {
"command": "npx",
"args": [
"-y",
"@tongxiao/common-search-mcp-server"
],
"env": {
"TONGXIAO_API_KEY": ""
}
}
}
}Related MCP server: DataWorks MCP Server
common_search This tool offers enhanced real-time search capabilities for open domain networks. By utilizing optimization with large models and integrating multiple data sources, it delivers clean, accurate, diverse, and high-quality results.
Best for: When you are unsure where to find information, use this interface to obtain accurate information from the web or multiple vertical data sources.
标准搜索接口提供增强的网络开放域的实时搜索能力，通过大模型优化与多数据源融合的技术，查询干净、准确、多样、高质量的结果。
Inputs: query (required).
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
Scored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool has a clearly defined and distinct purpose focused on enhanced web search.
With only one tool, naming consistency is inherently perfect. The tool name 'common_search' follows a clear and consistent verb_noun pattern, though no comparison with other tools is possible.
A single tool is too few for a server described as providing 'enhanced web open-domain real-time search capabilities' with 'multi-data source fusion'. This scope suggests operations like filtering, refining, or managing searches, which are missing, making the surface feel thin and incomplete.
The server's domain implies a search service, but with only a basic search tool, there are significant gaps. Missing operations likely include advanced filtering, result pagination, source selection, or search history management, which could cause agent failures in complex workflows.
The Remote MCP server acts as a standardized bridge between LLM applications (like Claude, ChatGPT, and Cursor) and external services, enabling AI agents to access external tools and resources. Its primary capability is providing a centralized search tool to discover other MCP servers and their respective tools. Unlike local implementations, it runs remotely with OAuth authentication and permission controls for security.
Remote MCP server for China brand visibility, destination demand, and KOL discovery workflows.
The BigQuery remote MCP server is a fully managed service that uses the Model Context Protocol to connect AI applications and LLMs to BigQuery data sources. It provides secure, standardized tools for AI agents to list datasets and tables, retrieve schemas, generate and execute SQL queries through natural language, and analyze data—enabling direct access to enterprise analytics data without requiring manual SQL coding.
The AWS Knowledge MCP server is a fully managed remote Model Context Protocol server that provides real-time access to official AWS content in an LLM-compatible format. It offers structured access to AWS documentation, code samples, blog posts, What's New announcements, Well-Architected best practices, and regional availability information for AWS APIs and CloudFormation resources. Key capabilities include searching and reading documentation in markdown format, getting content recommendations, listing AWS regions, and checking regional availability for services and features.
