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How do I remove outdated context files destroying the accuracy of my RAG model?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.

Vectara MCP Connector for Claude

A+

Empower your agent with Vectara's RAG capabilities. Search corpora natively, execute grounded chats, and manage indexed datasets easily.

7 tools Official Updated Jun 28, 2026 Official Vinkius Partner

More Details Connect to Claude

Connect your Vectara environment to any AI agent to unlock enterprise-grade Retrieval-Augmented Generation (RAG) and semantic search directly inside your conversational IDE or workspace.

What you can do

Semantic Search — Query your indexed private corpora naturally and return highly relevant, grounded documents without traditional keyword matching limitations.
Conversational RAG — Execute fully-fledged interactive chats leveraging Vectara's backend to provide detailed, cited answers strictly based on your secure documents.
Corpus Management — List all available data corpora, retrieve unique keys, and discover the shape of your indexed data environment on the fly.
Document Auditing — Monitor specific document indexes within a corpus, verify correct ingestions, or permanently delete obsolete files avoiding polluted search results.

How it works

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Enter your Vectara API Key and Customer ID
Start retrieving knowledge from Claude, Cursor, or any MCP-compatible client

Your AI agent becomes an elite cognitive search gateway to all your internal data.

Who is this for?

Software Engineers — debug RAG implementation challenges by directly testing query responses via chat instead of writing disposable test scripts.
Data Engineers — securely remove stale database context arrays manually inserted into Vectara via quick conversational text commands.
Product Leads — ask questions against internal product manuals stored as a Vectara corpus without waiting for the frontend UI development.
Technical Writers — locate specific passages traversing across thousands of embedded documents effortlessly leveraging contextual semantic queries.