Vectara MCP Server

The Vectara MCP server bridges AI assistants with Vectara’s Trusted Retrieval‑Augmented Generation (RAG) platform, providing a secure, low‑hallucination path to external knowledge. By exposing Vectara’s search and retrieval capabilities through the Model Context Protocol, developers can enrich conversational agents with up‑to‑date, domain‑specific data without exposing raw API keys or building custom connectors. This eliminates the need for bespoke integration layers and allows teams to focus on higher‑level dialogue logic.

At its core, the server offers two primary tool families: API Key Management and Query Execution. The key‑management tools let an agent authenticate once, storing the Vectara API key in memory for subsequent calls. The query tool, , accepts a natural‑language prompt and an optional list of corpus identifiers. It forwards the request to Vectara, retrieves ranked documents, and returns a concise answer that blends retrieved evidence with generative language. This workflow guarantees that responses are grounded in real data, thereby reducing hallucinations—a common pain point for generative models.

Key capabilities include:

• Transport Flexibility: HTTP (default, secure with bearer tokens), Server‑Sent Events for real‑time streaming, and STDIO for local development.
• Fine‑Grained Security: Built‑in bearer token authentication, optional API key headers, CORS origin validation, and environment‑driven configuration.
• Rate Limiting & Monitoring: Default limits protect against abuse, while developers can adjust thresholds to match usage patterns.
• Developer‑Friendly Configuration: Environment variables control transport mode, authentication enforcement, and allowed origins without code changes.

Typical use cases span from building knowledge‑base chatbots for enterprise support portals to powering AI‑driven research assistants that pull the latest scientific literature. In a production setting, an MCP client can call whenever the user poses a question; the server handles authentication, query routing, and result formatting, returning a coherent answer with minimal latency. In a local or prototyping scenario, the STDIO mode lets developers iterate quickly on prompt design within Claude Desktop.

Vectara’s Trusted RAG platform distinguishes itself with proven data‑quality pipelines and audit trails, giving AI teams confidence that the assistant’s outputs are both accurate and compliant. By encapsulating these features behind MCP, Vectara enables seamless integration into any agentic workflow that already speaks the protocol—whether it’s Claude Desktop, custom MCP clients, or future extensions.