It is a Model Context Protocol (MCP) server that provides search capabilities using a Google Custom Search Engine (CSE). It allows large language models (LLMs) to perform regular Google searches and returns search results containing titles, links, and snippets. The tool only provides search results and needs to be combined with other servers like mcp-server-fetch for content extraction.
This server enables LLMs to perform Google searches programmatically and integrate the results into AI workflows. It is useful for developers who want to incorporate search functionality into their applications or tools without directly interacting with Google's API. Additionally, it supports customization through environment variables and can be used in combination with other tools for advanced functionalities like 'deep search'.
Developers and organizations looking to integrate Google search capabilities into their applications or AI workflows should use this server. It is particularly beneficial for those working with LLMs and needing external data sources to enhance their models' capabilities.
You can set up and use this server locally by cloning the repository from GitHub (Richard-Weiss/mcp-google-cse), installing it via PIP, or using Smithery for automatic installation. It requires configuration of environment variables such as API_KEY and ENGINE_ID, which are obtained by setting up a custom search engine on Google Programmable Search Engine.
Use this server when you need to programmatically perform Google searches within your applications, especially if you're working with LLMs that require external data sources. It is suitable for scenarios where you need to chain multiple tools together or extract specific information from search results.
You can install it using uv (recommended), pip, or Smithery. For uv, run the server directly using uvx. For pip, install via `pip install mcp-google-cse` and run it using `python -m mcp-google-cse`. For Smithery, use `npx -y @smithery/cli install @Richard-Weiss/mcp-google-cse --client claude`.
The primary tool is `google_search`, which performs searches using a given search term and returns a list of results containing titles, links, and snippets. Additional customization can be achieved using environment variables like COUNTRY_REGION, GEOLOCATION, and RESULT_LANGUAGE.
The free quota is 100 searches per day. If this is insufficient for your use case and you don't want to set up billing, consider using another server.
Go to Google Programmable Search Engine, create a new search engine, and obtain the engine ID. Enable the Custom Search API in Google Cloud Console, create an API key, and configure these credentials in the server's environment variables.
MCP (Model Context Protocol) is an open protocol designed to standardize how applications provide context information to large language models (LLMs). Like a 'USB-C port' for AI applications, MCP ensures AI models can seamlessly connect with various data sources and tools.
An MCP Server is a server that supports the MCP protocol, enabling the exchange of contextual information between applications and AI models in a standardized way. It provides developers with an easy way to integrate AI models with databases, APIs, or other data sources.
An MCP Server eliminates the complexity of developing custom adapters by unifying the connection between AI models and various data sources. Whether you're a developer, data scientist, or AI app builder, an MCP Server simplifies the integration process, saving time and resources.
An MCP Server acts as an intermediary bridge, converting contextual information from various data sources into a format that AI models can understand. By adhering to the MCP protocol, it ensures data is transmitted between applications and AI models in a standardized manner.
At mcpserver.shop, you can browse our MCP Server Directory. The directory is categorized by industry (e.g., finance, healthcare, education), and each server comes with detailed descriptions and tags to help you quickly find the option that suits your needs.
The MCP Server Directory on mcpserver.shop is free to browse. However, some servers are hosted by third-party providers and may involve usage fees. Check the detailed page of each server for specific information.
MCP Servers support a wide range of data sources, including databases, APIs, cloud services, and custom tools. The flexibility of the MCP protocol allows it to connect almost any type of data source to AI models.
MCP Servers are primarily designed for developers, data scientists, and AI app builders. However, mcpserver.shop provides detailed documentation and guides to help users of varying technical levels get started easily.
Yes, MCP is an open-source protocol that encourages community participation and collaboration. For more details or to contribute, visit the official MCP documentation.
On mcpserver.shop, each MCP Server’s detailed page includes the provider’s contact information or a link. You can directly reach out to the provider for more details or technical support.