Search1API MCP Server

What does “Search1API” MCP Server do?

The Search1API MCP Server is a Model Context Protocol (MCP) server that provides AI assistants with search and web crawling capabilities via the Search1API. By acting as a bridge between AI agents and the powerful Search1API, this server enables seamless integration of internet search functionality into development workflows. This allows AI clients to execute web searches, retrieve live information, and access up-to-date content from the internet directly within their environment. Such capabilities are valuable for tasks like real-time data retrieval, automated research, and enhanced context gathering during code development or content generation. The Search1API MCP Server thus empowers AI-driven applications to perform dynamic, context-aware operations that depend on the latest online information.

List of Prompts

No prompt templates are explicitly documented in the repository at this time.

List of Resources

No resources are explicitly documented as MCP resources in the repository at this time.

List of Tools

No detailed tool definitions are documented in the accessible files (such as a server.py). However, from context, the core tool is likely:

• search: Provides web search results using the Search1API. Enables AI agents to query the internet and retrieve up-to-date information and links.

Use Cases of this MCP Server

• Real-time Web Search Integration: Allows developers to integrate live internet search results into their AI workflows, providing fresh data and answers to user queries.
• Automated Research Assistance: Empowers AI agents to fetch and summarize web content for research tasks, document generation, or content validation.
• Enhanced Coding Support: Provides AI coding assistants with the ability to look up documentation, libraries, or troubleshooting advice from the web.
• Dynamic Content Generation: Enables LLM-powered tools to incorporate current events, statistics, or trending topics directly from the web into generated outputs.

How to set it up

Windsurf

1. Ensure Node.js (>=18.0.0) is installed.
2. Obtain your Search1API API key from Search1API.
3. Edit your configuration file to add the Search1API MCP Server using the following JSON snippet:

   {
     "mcpServers": {
       "search1api": {
         "command": "npx",
         "args": [
           "-y",
           "search1api-mcp"
         ],
         "env": {
           "SEARCH1API_KEY": "YOUR_SEARCH1API_KEY"
         }
       }
     }
   }
   

4. Save the configuration and restart Windsurf.
5. Verify the connection to ensure the MCP server is active.

Claude

1. Install Node.js (>=18.0.0).
2. Acquire a Search1API API key.
3. Add the MCP Server details to your Claude integration config:

   {
     "mcpServers": {
       "search1api": {
         "command": "npx",
         "args": [
           "-y",
           "search1api-mcp"
         ],
         "env": {
           "SEARCH1API_KEY": "YOUR_SEARCH1API_KEY"
         }
       }
     }
   }
   

4. Save and restart Claude.
5. Confirm that the server is working by running a test search.

Cursor

1. Make sure Node.js (>=18.0.0) is installed.
2. Obtain your Search1API API key.
3. Edit the Cursor MCP configuration to include:

   {
     "mcpServers": {
       "search1api": {
         "command": "npx",
         "args": [
           "-y",
           "search1api-mcp"
         ],
         "env": {
           "SEARCH1API_KEY": "YOUR_SEARCH1API_KEY"
         }
       }
     }
   }
   

4. Save your config and restart Cursor.
5. Use Cursor’s interface to verify MCP server access.

Cline

1. Install Node.js (>=18.0.0).
2. Retrieve your API key from Search1API.
3. Add the server to your Cline configuration:

   {
     "mcpServers": {
       "search1api": {
         "command": "npx",
         "args": [
           "-y",
           "search1api-mcp"
         ],
         "env": {
           "SEARCH1API_KEY": "YOUR_SEARCH1API_KEY"
         }
       }
     }
   }
   

4. Save changes and restart Cline.
5. Test the setup to ensure connectivity.

Securing API Keys:
Always store your API keys securely using environment variables. Example configuration:

{
  "mcpServers": {
    "search1api": {
      "command": "npx",
      "args": ["-y", "search1api-mcp"],
      "env": {
        "SEARCH1API_KEY": "YOUR_SEARCH1API_KEY"
      }
    }
  }
}

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "search1api": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “search1api” to the actual name of your MCP server and replace the URL with your own MCP server URL.

Overview

Our opinion

The Search1API MCP Server is easy to set up, offers a well-documented API key management procedure, and provides a useful search tool for AI assistants. However, it lacks extensive prompt, resource, or tool documentation and does not mention advanced MCP features like Roots or Sampling. Its core function—web search—is valuable, but the overall MCP feature completeness is limited at this time.

MCP Score