Knowledge Base MCP Server

Setup Instructions

• Node.js (version 16 or higher)

• npm (Node Package Manager)

1. Clone the repository:

2. Install dependencies:

3. Configure environment variables:

4. Build the server:

5. Add the server to the MCP settings:

6. Create knowledge base directories:

• The server recursively reads all text files (e.g., .txt, .md) within the specified knowledge base subdirectories.

• The server skips hidden files and directories (those starting with a .).

• For each file, the server calculates the SHA256 hash and stores it in a file with the same name in a hidden .index subdirectory. This hash is used to determine if the file has been modified since the last indexing.

• The file content is splitted into chunks using the MarkdownTextSplitter from langchain/text_splitter.

• The content of each chunk is then added to a FAISS index, which is used for similarity search.

• The FAISS index is automatically initialized when the server starts. It checks for changes in the knowledge base files and updates the index accordingly.

Usage

• list_knowledge_bases: Lists the available knowledge bases.

• retrieve_knowledge: Retrieves similar chunks from the knowledge base based on a query. Optionally, if a knowledge base is specified, only that one is searched; otherwise, all available knowledge bases are considered. By default, at most 10 document chunks are returned with a score below a threshold of 2. A different threshold can optionally be provided using the threshold parameter.

Knowledge Base MCP Server

Setup Instructions

• Node.js (version 16 or higher)

• npm (Node Package Manager)

1. Clone the repository:

2. Install dependencies:

3. Configure environment variables:

4. Build the server:

5. Add the server to the MCP settings:

6. Create knowledge base directories:

• The server recursively reads all text files (e.g., .txt, .md) within the specified knowledge base subdirectories.

• The server skips hidden files and directories (those starting with a .).

• For each file, the server calculates the SHA256 hash and stores it in a file with the same name in a hidden .index subdirectory. This hash is used to determine if the file has been modified since the last indexing.

• The file content is splitted into chunks using the MarkdownTextSplitter from langchain/text_splitter.

• The content of each chunk is then added to a FAISS index, which is used for similarity search.

• The FAISS index is automatically initialized when the server starts. It checks for changes in the knowledge base files and updates the index accordingly.

Usage

• list_knowledge_bases: Lists the available knowledge bases.

• retrieve_knowledge: Retrieves similar chunks from the knowledge base based on a query. Optionally, if a knowledge base is specified, only that one is searched; otherwise, all available knowledge bases are considered. By default, at most 10 document chunks are returned with a score below a threshold of 2. A different threshold can optionally be provided using the threshold parameter.