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Verba MCP Server

Name: Verba
Availability: InStock
Author: Vinkius

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Connect your Verba RAG platform to your AI agent. Search your documents, retrieve semantic answers, and manage your Weaviate knowledge base directly.

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What is the Verba MCP Server?

The Verba MCP Server gives AI agents like Claude, ChatGPT, and Cursor direct access to Verba via 6 tools. Connect your Verba RAG platform to your AI agent. Search your documents, retrieve semantic answers, and manage your Weaviate knowledge base directly. Powered by the Vinkius - no API keys, no infrastructure, connect in under 2 minutes.

Built-in capabilities (6)

add_knowledge_documentdelete_knowledge_documentget_document_detailsget_system_configlist_knowledge_documentsperform_rag_query

Tools for your AI Agents to operate Verba

Ask your AI agent "Check Verba's configuration to see which embedding model it is currently using." and get the answer without opening a single dashboard. With 6 tools connected to real Verba data, your agents reason over live information, cross-reference it with other MCP servers, and deliver insights you would spend hours assembling manually.

Works with Claude, ChatGPT, Cursor, and any MCP-compatible client. Powered by the Vinkius - your credentials never touch the AI model, every request is auditable. Connect in under two minutes.

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One subscription gives you access to thousands of MCP servers - and you can deploy your own to the Vinkius Edge. Your AI agents only access the data you authorize, with DLP that blocks sensitive information from ever reaching the model, kill switch for instant shutdown, and up to 60% token savings. Enterprise-grade infrastructure and security, zero maintenance.

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Verba MCP Server capabilities

6 tools

add_knowledge_document

Provide the document content and optional metadata JSON. Ingests a new document into the Verba knowledge base

delete_knowledge_document

This action is irreversible. Permanently removes a document from the knowledge base

get_document_details

Retrieves the full content and metadata of a specific document

get_system_config

Retrieves the current Verba system configuration

list_knowledge_documents

Lists all documents indexed in the Verba knowledge base

perform_rag_query

Returns summarized answers with citations. Executes a RAG (Retrieval Augmented Generation) query against the Verba knowledge base

What the Verba MCP Server unlocks

Intertwine the open-source Verba (by Weaviate) ecosystem natively into your conversational AI IDE. Execute powerful Retrieval-Augmented Generation processes and manage your localized knowledge bases simply by chatting.

What you can do

Augmented Queries — Cast a question to your agent and have it retrieve fully synthesized answers from the Verba engine completely backed up by exact document citations.
Knowledge Management — Insert new context text, list all ingested documents, retrieve the deeply embedded raw data of any ID, or remove dead knowledge dynamically without Web UIs.
Health Checks — Request system configurations directly via chat to ensure your local LLM connections, embedding models, and cluster health are firing effectively.

How it works

1. Ensure your local or cloud Verba instance is running
2. Supply your Verba API URL and API Key (if authenticated)
3. Ask Claude or Cursor to query, retrieve, or insert document structures intuitively.

Who is this for?

RAG Developers — quickly add or delete text chunks to evaluate changes in embedding fidelity directly inside your IDE coding session.
Knowledge Managers — query your dense technical manuals using semantic search and receive the verified text snippets instantly.
Open Source Hobbyists — orchestrate your personal Weaviate/Verba RAG stacks strictly through the programmatic conversational layer.

Frequently asked questions about the Verba MCP Server

Can I query my local Verba instance directly through Cursor?

Yes! Once you configure VERBA_API_URL to point to http://localhost:8000 (or your host port), you can prompt your AI assistant to execute rigorous perform_rag_query instructions without ever breaking your developer focus.

How do I insert fresh text data into Verba completely using conversational chat?

Provide the agent with your desired context directly. For example: Add this chunk of markdown as a new document to Verba: '# Title Content...'. The agent leverages addDocumentTool, serializes the payload, and commits it into Verba's vector store immutably.

Are the query answers backed by citations from its embedded documents?

Absolutely. That's the primary benefit of the integration. When you run perform_rag_query, Verba utilizes Weaviate's hybrid search mechanics. The output explicitly includes natural language synthesis backed by the unique document IDs and snippet texts it referenced.