Vectara MCP Server

🔌 Compatible with

Vectara MCP is also compatible with any MCP client

The Model Context Protocol (MCP) is an open standard that enables AI systems to interact seamlessly with various data sources and tools, facilitating secure, two-way connections.

Vectara-MCP provides any agentic application with access to fast, reliable RAG with reduced hallucination, powered by Vectara's Trusted RAG platform, through the MCP protocol.

Installation

You can install the package directly from PyPI:

Related MCP server: Azure MCP Server

Quick Start

Secure by Default (HTTP/SSE with Authentication)

Local Development Mode (STDIO)

Configuration Options

Transport Modes

HTTP Transport (Default - Recommended)

• Security: Built-in authentication via bearer tokens

• Encryption: HTTPS ready

• Rate Limiting: 100 requests/minute by default

• CORS Protection: Configurable origin validation

• Use Case: Production deployments, cloud environments

SSE Transport

• Streaming: Server-Sent Events for real-time updates

• Authentication: Bearer token support

• Compatibility: Works with legacy MCP clients

• Use Case: Real-time streaming applications

STDIO Transport

• ⚠️ Security Warning: No transport-layer security

• Performance: Low latency for local communication

• Use Case: Local development, Claude Desktop

• Requirement: Must be explicitly enabled with --stdio flag

Environment Variables

Authentication

HTTP/SSE Transport

When using HTTP or SSE transport, authentication is required by default:

Disabling Authentication (Development Only)

Available Tools

API Key Management

• setup_vectara_api_key: Configure and validate your Vectara API key for the session (one-time setup).

  Args:

  • api_key: str, Your Vectara API key - required.

  Returns:

  • Success confirmation with masked API key or validation error.

• clear_vectara_api_key: Clear the stored API key from server memory.

  Returns:

  • Confirmation message.

Query Tools

• ask_vectara: Run a RAG query using Vectara, returning search results with a generated response.

  Args:

  • query: str, The user query to run - required.

  • corpus_keys: list[str], List of Vectara corpus keys to use for the search - required.

  • n_sentences_before: int, Number of sentences before the answer to include in the context - optional, default is 2.

  • n_sentences_after: int, Number of sentences after the answer to include in the context - optional, default is 2.

  • lexical_interpolation: float, The amount of lexical interpolation to use - optional, default is 0.005.

  • max_used_search_results: int, The maximum number of search results to use - optional, default is 10.

  • generation_preset_name: str, The name of the generation preset to use - optional, default is "vectara-summary-table-md-query-ext-jan-2025-gpt-4o".

  • response_language: str, The language of the response - optional, default is "eng".

  Returns:

  • The response from Vectara, including the generated answer and the search results.

• search_vectara: Run a semantic search query using Vectara, without generation.

  Args:

  • query: str, The user query to run - required.

  • corpus_keys: list[str], List of Vectara corpus keys to use for the search - required.

  • n_sentences_before: int, Number of sentences before the answer to include in the context - optional, default is 2.

  • n_sentences_after: int, Number of sentences after the answer to include in the context - optional, default is 2.

  • lexical_interpolation: float, The amount of lexical interpolation to use - optional, default is 0.005.

  Returns:

  • The response from Vectara, including the matching search results.

Analysis Tools

• correct_hallucinations: Identify and correct hallucinations in generated text using Vectara's VHC (Vectara Hallucination Correction) API.

  Args:

  • generated_text: str, The generated text to analyze for hallucinations - required.

  • documents: list[str], List of source documents to compare against - required.

  • query: str, The original user query that led to the generated text - optional.

  Returns:

  • JSON-formatted string containing corrected text and detailed correction information.

• eval_factual_consistency: Evaluate the factual consistency of generated text against source documents using Vectara's dedicated factual consistency evaluation API.

  Args:

  • generated_text: str, The generated text to evaluate for factual consistency - required.

  • documents: list[str], List of source documents to compare against - required.

  • query: str, The original user query that led to the generated text - optional.

  Returns:

  • JSON-formatted string containing factual consistency evaluation results and scoring.

Note: API key must be configured first using setup_vectara_api_key tool or VECTARA_API_KEY environment variable.

Configuration with Claude Desktop

To use with Claude Desktop, update your configuration to use STDIO transport:

Or using uv:

Note: Claude Desktop requires STDIO transport. While less secure than HTTP, it's acceptable for local desktop use.

Usage in Claude Desktop App

Once the installation is complete, and the Claude desktop app is configured, you must completely close and re-open the Claude desktop app to see the Vectara-mcp server. You should see a hammer icon in the bottom left of the app, indicating available MCP tools, you can click on the hammer icon to see more detail on the Vectara-search and Vectara-extract tools.

Now claude will have complete access to the Vectara-mcp server, including all six Vectara tools.

Secure Setup Workflow

First-time setup (one-time per session):

1. Configure your API key securely:

After setup, use any tools without exposing your API key:

Vectara Tool Examples

1. RAG Query with Generation:

2. Semantic Search Only:

3. Hallucination Detection & Correction:

4. Factual Consistency Evaluation:

Security Best Practices

1. Always use HTTP transport for production - Never expose STDIO transport to the network

2. Keep authentication enabled - Only disable with --no-auth for local testing

3. Use HTTPS in production - Deploy behind a reverse proxy with TLS termination

4. Configure CORS properly - Set VECTARA_ALLOWED_ORIGINS to restrict access

5. Rotate API keys regularly - Update VECTARA_API_KEY and VECTARA_AUTHORIZED_TOKENS

6. Monitor rate limits - Default 100 req/min, adjust based on your needs

See SECURITY.md for detailed security guidelines.

Support

For issues, questions, or contributions, please visit: https://github.com/vectara/vectara-mcp