Knowledge Base MCP Server

This MCP server provides tools for listing and retrieving content from different knowledge bases.

Setup Instructions

These instructions assume you have Node.js and npm installed on your system.

Installing via Smithery

To install Knowledge Base Server for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install @jeanibarz/knowledge-base-mcp-server --client claude

Manual Installation

Prerequisites

• Node.js (version 16 or higher)
• npm (Node Package Manager)

1. Clone the repository:

   git clone <repository_url>
   cd knowledge-base-mcp-server

2. Install dependencies:

   npm install

3. Configure environment variables:

   This server supports two embedding providers: Ollama (recommended for reliability) and HuggingFace (fallback option).

   Option 1: Ollama Configuration (Recommended)

   • Set EMBEDDING_PROVIDER=ollama to use local Ollama embeddings
   • Install Ollama and pull an embedding model: ollama pull dengcao/Qwen3-Embedding-0.6B:Q8_0
   • Configure the following environment variables:

     EMBEDDING_PROVIDER=ollama
     OLLAMA_BASE_URL=http://localhost:11434  # Default Ollama URL
     OLLAMA_MODEL=dengcao/Qwen3-Embedding-0.6B:Q8_0          # Default embedding model
     KNOWLEDGE_BASES_ROOT_DIR=$HOME/knowledge_bases

   Option 2: HuggingFace Configuration (Fallback)

   • Set EMBEDDING_PROVIDER=huggingface or leave unset (default)
   • Obtain a free API key from HuggingFace
   • Configure the following environment variables:

     EMBEDDING_PROVIDER=huggingface          # Optional, this is the default
     HUGGINGFACE_API_KEY=your_api_key_here
     HUGGINGFACE_MODEL_NAME=sentence-transformers/all-MiniLM-L6-v2
     KNOWLEDGE_BASES_ROOT_DIR=$HOME/knowledge_bases

   Additional Configuration

   • The server supports the FAISS_INDEX_PATH environment variable to specify the path to the FAISS index. If not set, it will default to $HOME/knowledge_bases/.faiss.
   • You can set these environment variables in your .bashrc or .zshrc file, or directly in the MCP settings.

4. Build the server:

   npm run build

5. Add the server to the MCP settings:

   • Edit the cline_mcp_settings.json file located at /home/jean/.vscode-server/data/User/globalStorage/saoudrizwan.claude-dev/settings/.

   • Add the following configuration to the mcpServers object:

   • Option 1: Ollama Configuration

   "knowledge-base-mcp-ollama": {
     "command": "node",
     "args": [
       "/path/to/knowledge-base-mcp-server/build/index.js"
     ],
     "disabled": false,
     "autoApprove": [],
     "env": {
       "KNOWLEDGE_BASES_ROOT_DIR": "/path/to/knowledge_bases",
       "EMBEDDING_PROVIDER": "ollama",
       "OLLAMA_BASE_URL": "http://localhost:11434",
       "OLLAMA_MODEL": "dengcao/Qwen3-Embedding-0.6B:Q8_0"
     },
     "description": "Retrieves similar chunks from the knowledge base based on a query using Ollama."
   },

   • Option 2: HuggingFace Configuration

   "knowledge-base-mcp-huggingface": {
     "command": "node",
     "args": [
       "/path/to/knowledge-base-mcp-server/build/index.js"
     ],
     "disabled": false,
     "autoApprove": [],
     "env": {
       "KNOWLEDGE_BASES_ROOT_DIR": "/path/to/knowledge_bases",
       "EMBEDDING_PROVIDER": "huggingface",
       "HUGGINGFACE_API_KEY": "YOUR_HUGGINGFACE_API_KEY",
       "HUGGINGFACE_MODEL_NAME": "sentence-transformers/all-MiniLM-L6-v2"
     },
     "description": "Retrieves similar chunks from the knowledge base based on a query using HuggingFace."
   },

   • Note: You only need to add one of the above configurations (either Ollama or HuggingFace) to your cline_mcp_settings.json file, depending on your preferred embedding provider.

   
   *   Replace `/path/to/knowledge-base-mcp-server` with the actual path to the server directory.
   *   Replace `/path/to/knowledge_bases` with the actual path to the knowledge bases directory.
   
   

6. Create knowledge base directories:

   • Create subdirectories within the KNOWLEDGE_BASES_ROOT_DIR for each knowledge base (e.g., company, it_support, onboarding).
   • Place text files (e.g., .txt, .md) containing the knowledge base content within these subdirectories.

• 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

The server exposes two tools:

• 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.

You can use these tools through the MCP interface.

The retrieve_knowledge tool performs a semantic search using a FAISS index. The index is automatically updated when the server starts or when a file in a knowledge base is modified.

The output of the retrieve_knowledge tool is a markdown formatted string with the following structure:

## Semantic Search Results

**Result 1:**

[Content of the most similar chunk]

**Source:**
```json
{
  "source": "[Path to the file containing the chunk]"
}
```

---

**Result 2:**

[Content of the second most similar chunk]

**Source:**
```json
{
  "source": "[Path to the file containing the chunk]"
}
```

> **Disclaimer:** The provided results might not all be relevant. Please cross-check the relevance of the information.

Each result includes the content of the most similar chunk, the source file, and a similarity score.