Vectra MCP Server

Overview

The Vectra MCP Server bridges AI assistants with a Vectra knowledge‑base, turning the rich graph and vector search capabilities of Vectra into a set of declarative tools that can be invoked by any MCP‑compatible client. By exposing operations such as collection management, bulk embedding, and hybrid query through a standard protocol, the server eliminates the need for developers to write custom API wrappers or handle authentication logic. Instead, they can focus on designing conversational flows that leverage structured knowledge retrieval and enrichment.

At its core, the server offers a suite of tools that mirror common data‑science workflows. Developers can create collections to logically group documents, embed plain text or file contents in bulk, and then query those embeddings with hybrid search that blends vector similarity and keyword matching. The built‑in graph‑search enhancement allows queries to traverse relationships defined in the underlying ArangoDB, giving assistants context that goes beyond simple similarity. Tools for listing and managing files, as well as direct access to ArangoDB nodes, give fine‑grained control over the data lifecycle and enable advanced debugging or custom analytics.

The value proposition lies in its seamless integration with AI workflows. A conversation can trigger to set up a new knowledge area, then to ingest relevant PDFs or logs. When the user asks a question, the assistant calls , automatically leveraging graph depth and relationship filters to surface not only the most similar passages but also related entities. Because all these actions are expressed as JSON payloads over MCP, the same client can be used across multiple knowledge bases or even swapped out for a different backend without changing the conversational logic.

Real‑world use cases include security operations centers that need to ingest threat intelligence reports, research teams that maintain a corpus of academic papers, or support desks that store product manuals. In each scenario, the server lets an AI assistant act as a knowledge navigator: it can pull up the most relevant documents, explain how they connect to other artifacts, and even delete outdated files. The ability to query ArangoDB nodes directly is particularly useful for audit trails or custom visualizations that require low‑level access to the graph structure.

Unique advantages of this MCP server stem from its hybrid search default, which ensures that keyword constraints are respected while still benefiting from semantic embeddings. The graph‑search enhancement is also a standout feature, allowing assistants to reason over relationships such as “who authored this” or “which system is affected by this vulnerability.” By exposing these capabilities through a clean, tool‑based interface, the Vectra MCP Server empowers developers to build sophisticated, data‑driven conversational experiences without wrestling with the intricacies of Vectra’s API or graph database.