Vectra MCP Server
About
An MCP server providing tools to manage and query a Vectra knowledge base, enabling integration with MCP clients via a backend API.
Details
- Author
- theVuArena
- GitHub stars
- 1
- Downloads
- 280
- Categories
- Other, Knowledge Base
Jump to
- Create and list Vectra collections.
- Embed texts in batch with optional metadata.
- Embed local files into Vectra.
- Query collections using hybrid and graph search.
- Add, list, and delete files in collections.
- Fetch ArangoDB nodes directly by key.
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
Vectra 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
Install dependencies with npm install, build with npm run build, then run the server with node build/index.js. For development, use npm run watch for auto-rebuild. The server communicates over stdio.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"vectra mcp server": {
"vectra-mcp-server": {
"command": "node",
"args": [
"build/index.js"
]
}
}
}
}McpServers
{
"vectra-mcp-server": {
"command": "node",
"args": [
"build/index.js"
]
}
}Vectra MCP Server
A Model Context Protocol (MCP) server for interacting with a Vectra knowledge base.
This TypeScript-based MCP server provides tools to manage and query a Vectra instance, enabling integration with MCP-compatible clients. It interacts with a backend Vectra API (presumably running separately).
Features
Tools
This server exposes the following tools for interacting with Vectra:
- create_collection: Create a new Vectra collection.
- Input: name (string, required), description (string, optional)
- list_collections: List existing Vectra collections.
- Input: None
- embed_texts: Embeds multiple text items in batch into Vectra.
- Input: items (array of objects with text (required) and optional metadata), collectionId (string, optional)
- embed_files: Reads multiple local files and embeds their content into Vectra.
- Input: sources (array of local file paths, required), collectionId (string, optional), metadata (object, optional - applies to all items)
- add_file_to_collection: Add an already embedded file (referenced by its ID) to a specific Vectra collection.
- Input: collectionId (string, required), fileId (string, required)
- list_files_in_collection: List files within a specific Vectra collection.
- Input: collectionId (string, required)
- query_collection: Query the knowledge base within a specific Vectra collection.
- Note: This tool always uses hybrid search (vector + keyword) and enables graph search enhancement by default.
- Input: collectionId (string, required), queryText (string, required), limit (number, optional), maxDistance (number, optional), graphDepth (number, optional), graphRelationshipTypes (array of strings, optional), includeMetadataFilters (array of objects, optional), excludeMetadataFilters (array of objects, optional)
- delete_file: Delete a file and its associated embeddings from Vectra.
- Input: fileId (string, required)
- get_arangodb_node: Fetch a specific node directly from the underlying ArangoDB database by its key.
- Input: nodeKey (string, required - e.g., chunk_xyz or doc_abc)
(Refer to src/tools.ts for detailed input schemas)
Development
Install dependencies:
npm install
Build the server:
npm run build
Run the server (listens on stdio):
node build/index.js
For development with auto-rebuild:
npm run watch
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