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Model Context Protocol (MCP) is a standardized way to add extra context to LLMs via external data sources and tools. Simply put, with using various MCP servers, you can have your LLM interact with your GitLab, search the web, find places on a map, and more. MCP servers are designed to be easy as possible to set up and use, so you can integrate them into your applications quickly.

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Getting Started

We have set up the MCP server to be accessible through a remote URL, so you can start using it right away, without needing to install anything locally. The server is available at: 
https://customerDomain.ambersearch.de/api/mcp/

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Authentication

The authentication for the MCP server is the same as for the public REST API. You will need to provide your API key in the Authorization header of your requests with the Bearer scheme.

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Configuration

Most of the applications use the same configuration format for handling MCP servers. Here’s a sample configuration that you can use to set up your MCP server in VS Code or other applications: 
{
    "servers": {
        "ambersearch": {
            "type": "http",
            "url": "https://customerDomain.ambersearch.de/api/mcp/",
            "headers": {
                "Authorization": "Bearer ambrs-exampletoken"
            }
        }
    },
    "inputs": []
}

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Tools

The AmberSearch MCP server provides the following tools:
Tool NameDescription
searchSearch for data across multiple data sources.
get_document_metadataRetrieve metadata for a specific document.
Detailed descriptions of the tools and their parameters are as follows:
  • search - Search for data across multiple data sources.
    • query: The search term to look for (string, required)
    • count: The number of results to return (number, optional)
    • data_source: The data source to search in (string, optional)
    • file_type: The type of files to search for (string, optional)
    • last_modified_date: The time frame for the last modified date (string, optional)
    • start_page: The page number to start from (number, optional)
  • get_document_metadata - Retrieve metadata for a specific document.
    • document_id: The unique identifier of the document (string, required)
    • provide_text_attribute: Whether to include the document’s text content in the response (boolean, optional)