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This MCP document Server with an integrated Faiss vectorstore works with popular MCP clients like GitHub Copilot and VSCode’s Continue extension — all running smoothly on Ubuntu WSL for Windows users.
Configuration
View docs{
"mcpServers": {
"bierschneideremanuel-mcpdocserver": {
"command": "python",
"args": [
"/home/em/McpDocServer/mcp_vectorstore_server.py"
],
"env": {
"PYTHONPATH": "/home/em/McpDocServer/McpDocServer",
"USER_AGENT": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36",
"VECTORSTORE_DIR": "/path/to/your/documents",
"LLMSHERPA_API_URL": "http://localhost:5001/api/parseDocument?renderFormat=all",
"CUDA_VISIBLE_DEVICES": "0"
}
}
}
}You run an MCP VectorStore Server to manage document vector stores, process PDFs, and perform semantic searches. It exposes a standardized MCP interface so you can search, create, and manage vector stores, read local files, perform web searches, and even calculate mathematical expressions from a unified client workflow.
Start by launching the local MCP VectorStore Server. Use a Python environment and run the server script so your MCP clients can connect. The server provides tools to search, create, inspect, and clear vector stores, as well as read local files and perform web searches. You can integrate it with clients that support MCP to issue tool calls like vectorstore_search, vectorstore_create, and vectorstore_info. When you query, you receive relevant document snippets from indexed stores and can perform additional operations such as reading files or calculating expressions.
Prerequisites: ensure you have Python 3.8 or higher installed on your system and a supported operating system (Linux, macOS, or Windows). You should have at least 4 GB RAM (8 GB+ recommended for large collections) and several gigabytes of free storage for models and vector stores.
Step 1: Create a working directory for the server and its data, then navigate into it.
Step 2: Create and activate a virtual environment.
Step 3: Install dependencies from the requirements file.
Step 4: (Optional but recommended) Install LLMSherpa for PDF processing and start its server if you will index and extract content from PDFs.
Step 5: Prepare your document directory with subfolders for PDFs and text files that you plan to index.
To run the server locally, use the following startup command as shown in the example usage. This starts the MCP VectorStore Server in the local environment.
python /home/em/McpDocServer/mcp_vectorstore_server.pyConfigure your MCP client to connect to the local server using a stdio connection. The following example shows how to wire the server into a client configuration. This enables you to issue tool calls to create, search, and manage vector stores directly from your MCP-enabled client.
{
"mcpServers": {
"vectorstore": {
"command": "python",
"args": ["/home/em/McpDocServer/mcp_vectorstore_server.py"],
"env": {
"PYTHONPATH": "/home/em/McpDocServer/McpDocServer"
}
}
}
}The server exposes a set of tools to interact with the vector store, read files, perform web searches, and compute calculations.
Search the vector store for relevant documents using a query and a result count.
Create a new vector store from documents found in a specified directory.
Retrieve information about the current vector store, such as document count and model details.
Remove all documents from the current vector store.
Read and return the contents of a file on the local system.
Perform a Google search for a given query and return results.
Search Wikipedia for information related to a query.
Perform a DuckDuckGo search for information.
Evaluate a mathematical expression and return the result.