Vectara Connector for AI agents.
7 live capabilities
Connect your private data to your agent for grounded RAG and semantic search.
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Why people use Vectara
Vectara for Semantic Search in Enterprise Knowledge Bases
Vectara changes this by letting your agent pull the right info automatically. You ask a question, it searches your corpus, and gives you a cited answer in seconds.
What Vinkius changes
Your agent gets immediate access to your private knowledge without you having to copy and paste anything.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 5,900+ Connectors
- Real-world use case 01
Debugging RAG responses
A developer uses execute_rag_chat to see how the agent handles a specific query and verify citations without writing a test script.
- Real-world use case 02
Cleaning stale data indices
A data engineer uses delete_corpus_document to remove a stale database schema that's causing the agent to hallucinate.
- Real-world use case 03
Querying internal manuals
A product lead asks questions about the latest manual using perform_semantic_search to get instant answers on internal specs.
Complete set · 7capabilities
The complete Vectara capability set.
These are the exact actions your AI can choose when you ask it to work with Vectara.
01—04
4 capabilities in this set.
Part of 7 available through Vectara.
- 01 Capability
List corpora
View all searchable datasets in your Vectara account. It's the quickest way to see what's indexed.
- 02 Capability
Perform semantic search
Run a query across one or more corpora. This returns relevant documents based on meaning rather than keywords.
- 03 Capability
List chat sessions
See a history of previous RAG chat sessions. This helps track past interactions.
- 04 Capability
List corpus documents
See every document inside a specific corpus. Use this to audit your current data.
05—07
3 capabilities in this set.
Part of 7 available through Vectara.
- 05 Capability
Execute rag chat
Get a summarized response with citations from a specific corpus. It's perfect for grounded conversations.
- 06 Capability
Delete corpus document
Permanently remove a document from a corpus. This helps keep your search results clean.
- 07 Capability
Get corpus details
Pull metadata and configuration for a specific corpus. Use this to check your data setup.
Set up in minutes
One URL. Then ask Vectara to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Vectara from the conversation.
Choose your client
Live previewAdvanced clients IDE · CLI
Claude · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_P55Xdk6UU5XMN3cdVPORquufduaGCfpEWSemsNQg/mcp - Step 01
Open Connectors
In Claude Web or Claude Desktop, open Settings and choose Connectors.
- Step 02
Add the URL
Choose Add custom connector, name it Vectara, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable Vectara for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_P55Xdk6UU5XMN3cdVPORquufduaGCfpEWSemsNQg/mcp - Step 01
Open MCP settings
On desktop, open Settings and MCP servers. On web, open your workspace app or connector settings.
- Step 02
Add the URL
Choose Add server with Streamable HTTP, or create a custom MCP app, then paste the Vectara URL.
- Step 03
Save and start
Save the connection and enable Vectara in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"vectara": {
"url": "https://edge.vinkius.com/vk_preview_P55Xdk6UU5XMN3cdVPORquufduaGCfpEWSemsNQg/mcp"
}
}
}- Step 01
Open MCP Settings
Press Cmd+Shift+P (macOS) or Ctrl+Shift+P (Windows/Linux) → search "MCP Settings"
- Step 02
Add the server config
Paste the JSON configuration above into the mcp.json file that opens
- Step 03
Save the file
Cursor will automatically detect the new Connector
- Step 04
Start using Vectara
Open Agent mode in chat and ask: "Using Vectara, help me...". 7 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"vectara": {
"url": "https://edge.vinkius.com/vk_preview_P55Xdk6UU5XMN3cdVPORquufduaGCfpEWSemsNQg/mcp"
}
}
}- Step 01
Create MCP config
Create a .vscode/mcp.json file in your project root
- Step 02
Add the server config
Paste the JSON configuration above
- Step 03
Enable Agent mode
Open GitHub Copilot Chat and switch to Agent mode using the dropdown
- Step 04
Start using Vectara
Ask Copilot: "Using Vectara, help me...". 7 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"vectara": {
"url": "https://edge.vinkius.com/vk_preview_P55Xdk6UU5XMN3cdVPORquufduaGCfpEWSemsNQg/mcp"
}
}
}- Step 01
Open MCP Settings
Go to Settings → MCP Configuration or press Cmd+Shift+P and search "MCP"
- Step 02
Add the server
Paste the JSON configuration above into mcp_config.json
- Step 03
Save and reload
Windsurf will detect the new server automatically
- Step 04
Start using Vectara
Open Cascade and ask: "Using Vectara, help me...". 7 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"vectara": {
"url": "https://edge.vinkius.com/vk_preview_P55Xdk6UU5XMN3cdVPORquufduaGCfpEWSemsNQg/mcp"
}
}
}- Step 01
Open Cline MCP Settings
Click the Connectors icon in the Cline sidebar panel
- Step 02
Add remote server
Click "Add Connector" and paste the configuration above
- Step 03
Enable the server
Toggle the server switch to ON
- Step 04
Start using Vectara
Ask Cline: "Using Vectara, help me...". 7 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add vectara --transport http "https://edge.vinkius.com/vk_preview_P55Xdk6UU5XMN3cdVPORquufduaGCfpEWSemsNQg/mcp"- Step 01
Install Claude Code
Run npm install -g @anthropic-ai/claude-code if not already installed
- Step 02
Add the Connector
Run the command above in your terminal
- Step 03
Verify the connection
Run claude mcp to list connected servers, or type /mcp inside a session
- Step 04
Start using Vectara
Ask Claude: "Using Vectara, show me...". 7 tools are ready
Where the request belongs
Work Vectara can move forward.
This is for the engineer building RAG systems who needs to test results fast, or the product lead who needs to query internal wikis without a custom UI.
Software Engineer
Tests RAG responses and debugs query results via chat instead of writing disposable test scripts.
Data Engineer
Manages and cleans up stale database context arrays by removing old documents from the corpus.
Product Lead
Queries internal product manuals and wikis to get answers without waiting for a frontend UI.
Technical Writer
Locates specific passages across thousands of embedded documents using contextual semantic queries.
Build the capability set
Add more capabilities.
Each Connector adds new actions and data without changing how you work.
Browse ConnectorsR2R
Equip your AI with direct access to your R2R engine. execute vector searches, run precise RAG queries, and manage your documents.
GroundX
Data search and RAG optimization platform.
Glean
Search across all your company apps and docs with AI that understands your organization and surfaces the right answer instantly.
Lucidworks Fusion (AI Search & Discovery)
Manage AI-powered search via Lucidworks Fusion. execute semantic queries, index documents, and monitor ML training jobs.
LlamaIndex (AI Data Framework & RAG)
Query and manage RAG pipelines via LlamaIndex. execute natural language searches, audit indexed files, and monitor data pipelines.
Vertex AI Search
Search across your enterprise data using Google's semantic search and generative AI grounding.
Bring your own AI
Change the model, client or framework. Keep Vectara connected.
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Claude -
ChatGPT -
Gemini -
Cursor -
VS Code -
Windsurf -
ZCode -
Cline -
Zed -
Continue -
Kiro -
Roo Code -
Zencoder -
Goose -
Void -
Augment Code -
Amp -
Qodo -
Tabnine -
Pieces -
Sourcegraph Cody -
JetBrains -
Warp -
Amazon Q -
Antigravity -
BoltAI -
Raycast -
Jan -
LM Studio -
AnythingLLM -
Open WebUI -
Msty -
Cherry Studio -
LibreChat -
TypingMind -
Chorus -
5ire -
n8n -
LangChain -
LlamaIndex -
CrewAI -
Vercel AI SDK
Before you connect
Questions about Vectara.
The practical details behind the request, access and result.
How does Vectara MCP help with RAG?
Vectara MCP provides a direct connection for your AI agent to query your private data. It enables grounded responses where the agent only answers based on the documents you've indexed.
Can I use Vectara MCP to search my private files?
Yes, that is the primary use case. It allows your agent to perform semantic searches across your private PDFs, text files, and other documents stored in your Vectara environment.
How do I connect Vectara to my AI agent?
You connect it by adding the Vectara MCP to your supported client like Claude or Cursor. You'll just need your Vectara API Key and Customer ID to get started.
Can Vectara MCP delete old files from my index?
Yes, it includes capabilities to manage your data. You can ask your agent to delete specific documents from a corpus to ensure your search results stay clean and relevant.
Does Vectara MCP support semantic search?
Yes, it uses semantic search to find information based on the meaning of your query rather than just looking for exact word matches.
How do I see my chat history with Vectara?
The Connector includes a capability to list previous RAG chat sessions, allowing you to review past interactions and queries performed by your agent.
Can I query my internal documents directly using just conversational chat?
Yes. If your data is indexed in a Vectara corpus, simply ask your agent: search the 'employee-handbook' corpus for remote work policies. The agent uses the queryTool to pass your question to Vectara's semantic engine, effortlessly bringing back precisely matching paragraph citations instantly.
How do I remove outdated context files destroying the accuracy of my RAG model?
You don't need to rebuild APIs or use cURL. Tell your AI: delete document ID 'doc-992a' from my Sales corpus. It automatically formats the mutation and wipes the poisoned embedding from Vectara's nodes permanently, restoring high accuracy.
Will the RAG Chat capability provide accurate source citations?
Yes. When you instruct the agent to run execute_rag_chat, Vectara processes the query against its internal LLM and index, returning a synthesized natural language answer appended solidly with exact document citations, proving the AI isn't hallucinating facts.
One connection away
Give your agent a direct line to Vectara.
Connect Vectara once. Keep it beside 5,900+ managed Connectors when the next task needs more.
Explore every Connector No credit card required · Free tier available