MCP Server
About
MCP server that provides tools for metadata retrieval from SharePoint and entity retrieval from a Neo4j knowledge graph using semantic similarity search.
Details
- Author
- JanithSilva
- Downloads
- 328
- Categories
- Knowledge Base
Jump to
- Retrieves structured metadata from SharePoint Documents library
- Queries Neo4j knowledge graph with semantic similarity search
- Built on FastMCP framework for tool management
- Configurable via environment variables
- Uses semantic embedding for entity retrieval
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
MCP ServerCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
Clone the repository, create a Python virtual environment, install dependencies from requirements.txt, and configure environment variables in a .env file (including SharePoint credentials, Neo4j URI, and embedding model settings). Start the server with python mcp_server.py, then invoke the metadata_retrieve or entity_retrieve tools.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"mcp server": {
"mcp-server-janithsilva": {
"command": "python",
"args": [
"-m",
"venv",
"venv"
]
}
}
}
}McpServers
{
"mcp-server-janithsilva": {
"command": "python",
"args": [
"-m",
"venv",
"venv"
]
}
}MCP Server
A Python-based server application that provides tools for metadata retrieval from SharePoint and entity retrieval from a Neo4j knowledge graph using semantic similarity search.
Features
- Metadata Retrieval: Fetches structured metadata from SharePoint's 'Documents' library
- Entity Retrieval: Queries a Neo4j knowledge graph using semantic similarity to find relevant entities and their relationships
- FastMCP Integration: Built on top of FastMCP framework for tool management and execution
Prerequisites
- Python 3.x
- Neo4j database
- SharePoint access credentials
- OpenAI API key (for semantic search)
Installation
1. Clone the repository:
git clone <repository-url>
cd mcp-server
2. Create and activate a virtual environment:
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
3. Install dependencies:
pip install -r requirements.txt
4. Configure environment variables:
Create a .env file with the following variables:
SHAREPOINT_URL=
SHAREPOINT_USERNAME=
SHAREPOINT_PASSWORD=
SHAREPOINT_LIBRARY_NAME=
NEO4J_URI=
NEO4J_USER=
NEO4J_PASSWORD=
EMBEDDING_MODEL_ENDPOINT=
EMBEDDING_MODEL_KEY=
EMBEDDING_MODEL_API_VERSION=
EMBEDDING_MODEL_DEPLOYMENT_NAME=
EMBEDDING_MODEL_CHUNK_SIZE=
EMBEDDING_MODEL_DIMENSION=
EMBEDDING_MODEL_CHUNK_OVERLAP=
Usage
1. Start the server:
python mcp_server.py
2. The server provides two main tools:
- metadata_retrieve: Retrieves structured metadata from SharePoint
- entity_retrieve: Queries the knowledge graph using semantic similarity
Project Structure
.
├── mcp_server.py # Main server application
├── settings.py # Configuration management
├── services/ # Service implementations
│ ├── graph_store.py # Neo4j graph operations
│ └── sharepoint.py # SharePoint integration
├── requirements.txt # Project dependencies
└── .env # Environment variables
Dependencies
- langchain-mcp-adapters
- mcp[cli]
- langchain_openai
- pinecone
- langchain
- langchain_neo4j
- office365-rest-python-client
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