Connect Vectara MCP for AI Agents
Grounding AI Answers in Internal Corporate Knowledge Bases
We take care of the infrastructure, maintenance, security, and governance. Works with:
Claude 
ChatGPT 
Cursor 
Gemini 
Windsurf 
VS Code 
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What AI agents can do with 7 Vectara Tools for Enterprise Knowledge Search
Use these tools to list corpora, perform advanced semantic searches, execute RAG chats, or clean up outdated files in your company's data.
Execute rag chat
Runs a conversational chat using your private documents, providing an AI response that includes citations for accuracy.
Delete corpus document
Permanently removes a specific document from a corpus; this action cannot be undone.
Get corpus details
Retrieves the configuration and metadata for any given knowledge corpus.
List chat sessions
Shows a history of previous AI chat sessions run against your data sources.
List corpora
Generates a full list of all searchable knowledge datasets (corpora) in the account.
List corpus documents
Lists every individual document indexed within a specified corpus for review.
Perform semantic search
Runs an advanced search across one or more corpora using meaning, not just keywords, to find relevant documents.
Frequently Asked Questions
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How do I remove outdated context files destroying the accuracy of my RAG model?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 tool 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.
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