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。
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | 搜索问题（长度：>=2 and <=100） |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It mentions 'enhanced capabilities' and 'optimized by large models,' but fails to disclose critical behavioral traits such as rate limits, authentication needs, response format, or error handling. This leaves significant gaps for an agent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core functionality ('standard search interface provides enhanced real-time search capabilities'). It avoids redundancy but could be slightly more structured for clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete. It omits details on return values, error cases, and operational constraints like rate limits or data sources, which are essential for a search tool with real-time claims.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with the parameter 'query' documented as a search question with length constraints. The description adds no additional parameter semantics beyond what the schema provides, such as query formatting examples or supported languages, meeting the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose as providing 'enhanced real-time search capabilities for the open web domain' with 'clean, accurate, diverse, high-quality results.' It specifies the verb (search) and resource (open web), but since there are no sibling tools, it cannot demonstrate differentiation from alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions general capabilities like 'real-time search' and 'multi-source fusion,' but provides no explicit guidance on when to use this tool versus alternatives, prerequisites, or exclusions. Without sibling tools, it lacks comparative context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.0.0common_searchScored 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.
One MCP server that gives your AI agent search, AI answer, social, ad and lead data.
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.
