How to Use the Verba MCP in Windsurf

Q: How do I search my documents using Verba and Windsurf?

You execute a query with performragquery. This tool searches the entire knowledge base, returning summarized answers plus specific citations. It's perfect for feeding contextual data into your multi-step workflow.

Q: What does Verba do if I need to update my documents? (Windsurf)

If you have new content, use addknowledgedocument. You send the document text and any metadata JSON required. The MCP Server ingests it directly into your knowledge base for future queries.

Q: Can I check if a specific document exists in Verba? (Windsurf)

Yes, you use getdocumentdetails with the necessary identifier. This tool pulls both the full content and any metadata attached to that specific knowledge item.

Connect Verba to Windsurf: Build complex knowledge workflows with multi-step automation.

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Connect Verba MCP to Windsurf

Create your Vinkius account to connect Verba to Windsurf and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Verba with Windsurf

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Manage and Index Knowledge Documents

The `add_knowledge_document` tool lets you feed new content into the Verba knowledge base. You just provide the document text and any associated metadata JSON. You can also check what's already indexed by running `list_knowledge_documents`, or pull all the info for a specific file using `get_document_details`. This handles the entire lifecycle of your source data.

Perform Semantic Searches (RAG)

The core function is `perform_rag_query`. When you use this, Verba searches all indexed documents and returns summarized answers with citations. It doesn't just give keywords; it gives context. Your agent can also manage the backend by running `get_system_config`, ensuring the whole MCP Server stack is configured correctly before launching a complex task.

Maintain Knowledge Base State

If you need to remove data, use `delete_knowledge_document`. Be warned—this action permanently removes documents and can't be undone. The system treats this as a final command. For verification, the `get_system_config` tool lets you pull the current operational state of Verba. It’s quick way to check if the MCP Server is ready for your next cascade step.

Setup guide

Set up Verba MCP in Windsurf

Prerequisites

Windsurf IDE installed (macOS, Windows, or Linux)
Active Vinkius subscription with a valid endpoint token

1

Open MCP configuration

Click the Cascade assistant icon in the sidebar, then click the hammer icon (🔨) at the top of the panel. Select "Configure" to open ~/.codeium/windsurf/mcp_config.json.
2

Add the Verba MCP

Paste the JSON snippet shown on the right into the mcpServers object. Replace [YOUR_TOKEN_HERE] with your endpoint token from cloud.vinkius.com.
3

Refresh MCPs

Go back to the hammer icon (🔨) in Cascade and click "Refresh". Windsurf will detect the new server. No full restart is needed — the connection is hot-reloaded.
4

Verify in Cascade

Start a new Cascade conversation and ask something like "Show my Verba payment history." If connected, Cascade will call the Verba tools directly. You will see a green dot next to the server name in the MCP panel.

mcp_config.json

{
  "mcpServers": {
    "verba-mcp": {
      "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    }
  }
}

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Common questions about Verba MCP in Windsurf

You execute a query with `perform_rag_query`. This tool searches the entire knowledge base, returning summarized answers plus specific citations. It's perfect for feeding contextual data into your multi-step workflow.

If you have new content, use `add_knowledge_document`. You send the document text and any metadata JSON required. The MCP Server ingests it directly into your knowledge base for future queries.

Yes, you use `get_document_details` with the necessary identifier. This tool pulls both the full content and any metadata attached to that specific knowledge item.

The Verba MCP Server handles structured text documents and their associated metadata JSON. It is designed for deep, semantic context retrieval using RAG principles.

Yes. The system manages the persistence of all documents indexed via `add_knowledge_document`. All list and retrieve commands operate on this stored data.

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