Overview

Vectara MCP is the official Model Context Protocol server for Vectara, a managed retrieval-augmented generation (RAG) and semantic search platform designed to reduce hallucination in AI responses. It enables AI assistants to perform grounded knowledge queries and semantic searches against Vectara-indexed corpora through a standardized MCP interface.

Built and maintained by Vectara, this connector is available as a Python package on PyPI and can be installed in seconds. It provides two core tools — ask_vectara for RAG queries with generated responses, and search_vectara for pure semantic search — giving AI assistants access to fast, reliable, citation-backed answers from organizational knowledge bases.

Vectara is purpose-built for enterprise knowledge retrieval where accuracy and attribution matter. The platform is used for internal help centers, customer-facing knowledge assistants, and research workflows. By routing AI queries through Vectara rather than relying on general model knowledge, organizations can ensure responses are grounded in their own curated and verified content.

Key Features

RAG Query with Citations

The ask_vectara tool performs retrieval-augmented generation, returning AI-generated answers backed by citations from the indexed corpus to reduce hallucination.

Semantic Search

The search_vectara tool performs pure semantic search without text generation, returning ranked results with relevance scores for raw source material retrieval.

Corpus-Based Knowledge Isolation

Queries are scoped to specific Vectara corpora, enabling organizations to maintain separate knowledge bases for different teams, products, or use cases with clear data boundaries.

Hallucination Reduction

Vectara specializes in factual grounding through its Trusted RAG architecture, which includes built-in mechanisms for detecting and reducing hallucinated content in generated responses.

Simple Integration

Install via pip and configure with API and corpus keys. Compatible with Claude Desktop, Cursor, and any MCP-compatible client for straightforward governed knowledge retrieval.

Capabilities

Vectara MCP exposes 5 tools for AI agents.

3 Read1 Write1 Delete

Tool	Operation	Risk	Description
`search` Semantic search across indexed content	Read	Low Risk	Semantic search across indexed content
`query` RAG query with citation	Read	Low Risk	RAG query with citation
`index_document` Indexes a document for search	Write	Medium Risk	Indexes a document for search
`list_corpora` Lists available corpora	Read	Low Risk	Lists available corpora
`delete_document` Removes document from index	Delete	Medium Risk	Removes document from index

Use Cases

Strategy-Aligned Use Cases

Internal Knowledge Assistant

AI assistants can answer employee questions by searching organizational knowledge bases, returning grounded answers with citations to source documents — reducing time spent searching documentation.

Customer Support Knowledge Retrieval

Route customer inquiries through Vectara to find relevant help articles, product documentation, and troubleshooting guides, enabling AI-assisted support with verified, citation-backed responses.

Research and Due Diligence

Search across indexed research materials, market reports, and competitive intelligence to surface relevant findings for strategic planning and decision-making workflows.

Policy and Compliance Lookup

Enable AI assistants to quickly find and cite specific policy documents, compliance requirements, and procedural guidelines, ensuring organizational decisions reference authoritative sources.

Integrations

Used By

Legal Plugin

Legal document search

Data Plugin

Semantic data search

Enterprise Search Plugin

Semantic search

Considerations

Before You Adopt

Vectara corpora may contain internal documentation, proprietary research, or confidential business knowledge. AI access should be scoped to specific corpora appropriate for each user role and use case.
While Vectara provides citations, organizations should verify that AI assistants surface these citations to end users rather than presenting generated content as original analysis. Proper attribution maintains information integrity.
The content indexed into Vectara corpora determines what AI assistants can retrieve. Organizations must govern what data flows into Vectara to prevent sensitive or outdated information from being surfaced in AI responses.
Vectara is a commercial platform with usage-based pricing. High-volume AI query patterns should be monitored to manage costs and ensure queries are purposeful rather than redundant.

Stratafy Fit

Integration Potential

3/5

Vectara MCP is a moderate-priority governance target for Stratafy. As a read-only knowledge retrieval tool with built-in hallucination reduction, it carries lower inherent risk than tools with write operations or direct data access. However, governance remains valuable for controlling which corpora are accessible to different roles, monitoring query patterns to prevent sensitive knowledge exposure, and ensuring that AI-generated responses maintain proper attribution. Stratafy can add corpus-level access controls, query logging for audit purposes, and cost monitoring for usage-based pricing optimization. Organizations with highly sensitive knowledge bases will benefit most from governance here.