What is Vectra Mcp Server

Vectra MCP Server is a Model Context Protocol (MCP) server designed to manage and query a Vectra knowledge base, allowing integration with MCP-compatible clients through a backend API.

Use cases

Use cases for Vectra MCP Server include managing large datasets, enhancing search capabilities in applications, integrating with other MCP-compatible systems, and providing a structured way to handle knowledge bases.

How to use

To use Vectra MCP Server, you can interact with its various tools via API calls. This includes creating collections, embedding texts and files, querying collections, and managing files within the Vectra knowledge base.

Key features

Key features include tools for creating and listing collections, embedding texts and files, querying collections with hybrid search capabilities, and managing files within collections.

Where to use

Vectra MCP Server is suitable for applications in data management, knowledge bases, and any environment requiring efficient querying and integration of information.

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