What tools does the server expose?
It provides tools for collection management, embedding texts/files, querying collections, file management, and direct ArangoDB node retrieval. See the README’s tools list for full input details.
Vectra MCP Server
@dangvu0502
基本信息
关于 Vectra MCP Server
An MCP server providing tools to manage and query a Vectra knowledge base, enabling integration with MCP clients via a backend API.
Vectra MCP Server 配置
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"vectra-mcp": {
"command": "node",
"args": [
"build/index.js"
]
}
}
}Vectra MCP Server 工具
9Create a new Vectra collection.
List existing Vectra collections.
Embeds multiple text items in batch into Vectra.
Reads multiple local files and embeds their content into Vectra.
Add an already embedded file (referenced by its ID) to a specific Vectra collection.
List files within a specific Vectra collection.
Query the knowledge base within a specific Vectra collection.
Delete a file and its associated embeddings from Vectra.
Fetch a specific node directly from the underlying ArangoDB database by its key.
概览
What is Vectra MCP Server?
A Model Context Protocol (MCP) server for managing and querying a Vectra knowledge base. It exposes tools to create collections, embed texts and files, and perform hybrid search (vector + keyword) with graph enhancement. Built in TypeScript for use with MCP-compatible clients.
How to use Vectra MCP Server?
Install dependencies with npm install, build with npm run build, and run the server (listens on stdio) with node build/index.js. For development with auto-rebuild, use npm run watch.
Key features of Vectra MCP Server
- Create and list Vectra collections.
- Embed text items or local files (batch) into Vectra.
- Query collections using hybrid search (vector + keyword) with optional graph enhancement.
- Add already embedded files to collections and list files in a collection.
- Delete files and their associated embeddings.
- Fetch nodes directly from the underlying ArangoDB database by key.
Use cases of Vectra MCP Server
- Build and maintain a searchable knowledge base from documents.
- Automate embedding of local files into Vectra collections.
- Enable LLM-powered retrieval-augmented generation (RAG) workflows via MCP clients.
常见问题
How does querying work?
The `query_collection` tool always uses hybrid search (vector + keyword) with graph search enabled by default. You can optionally limit results, set a max distance, and filter by metadata or graph relationships.
What are the runtime requirements?
A Node.js environment (npm installed). The server listens on stdio; a Vectra backend API must be running separately.
What transport does the server use?
The server uses stdio transport (standard input/output), as is typical for MCP servers.
Where are embeddings and data stored?
Data lives in the Vectra backend and its underlying ArangoDB database. The server does not store data locally beyond configuration.
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