Vectara MCP Server

🔌 Compatible with Claude Desktop, and any other MCP Client!

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:

pip install vectara-mcp

Quick Start

Secure by Default (HTTP/SSE with Authentication)

# Start server with secure HTTP transport (DEFAULT)
python -m vectara_mcp
# Server running at http://127.0.0.1:8000 with authentication enabled

Local Development Mode (STDIO)

# For Claude Desktop or local development (less secure)
python -m vectara_mcp --stdio
# ⚠️ Warning: STDIO transport is less secure. Use only for local development.

Configuration Options

# Custom host and port
python -m vectara_mcp --host 0.0.0.0 --port 8080

# SSE transport mode
python -m vectara_mcp --transport sse --path /sse

# Disable authentication (DANGEROUS - dev only)
python -m vectara_mcp --no-auth

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

# Required
export VECTARA_API_KEY="your-api-key"

# Optional
export VECTARA_AUTHORIZED_TOKENS="token1,token2"  # Additional auth tokens
export VECTARA_ALLOWED_ORIGINS="http://localhost:*,https://app.example.com"
export VECTARA_TRANSPORT="http"  # Default transport mode
export VECTARA_AUTH_REQUIRED="true"  # Enforce authentication

Authentication

HTTP/SSE Transport

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

# Using curl with bearer token
curl -H "Authorization: Bearer $VECTARA_API_KEY" \
     -H "Content-Type: application/json" \
     -X POST http://localhost:8000/call/ask_vectara \
     -d '{"query": "What is Vectara?", "corpus_keys": ["my-corpus"]}'

# Using X-API-Key header (alternative)
curl -H "X-API-Key: $VECTARA_API_KEY" \
     http://localhost:8000/sse

Disabling Authentication (Development Only)

# ⚠️ NEVER use in production
python -m vectara_mcp --no-auth

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

... View full README on GitHub