🔌 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.

You can install the package directly from PyPI:

pip install vectara-mcp

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

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

# 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

• 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

• Streaming: Server-Sent Events for real-time updates
• Authentication: Bearer token support
• Compatibility: Works with legacy MCP clients
• Use Case: Real-time streaming applications

• ⚠️ 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

# 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

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

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

• 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.

• 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.

• 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.

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

{
  "mcpServers": {
    "Vectara": {
      "command": "python",
      "args": ["-m", "vectara_mcp", "--stdio"],
      "env": {
        "VECTARA_API_KEY": "your-api-key"
      }
    }
  }
}

Or using uv:

{
  "mcpServers": {
    "Vectara": {
      "command": "uv",
      "args": ["tool", "run", "vectara-mcp", "--stdio"]
    }
  }
}

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

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.

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

1. Configure your API key securely:

setup-vectara-api-key
API key: [your-vectara-api-key]

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

1. RAG Query with Generation:

ask-vectara
Query: Who is Amr Awadallah?
Corpus keys: ["your-corpus-key"]

2. Semantic Search Only:

search-vectara
Query: events in NYC?
Corpus keys: ["your-corpus-key"]

3. Hallucination Detection & Correction:

correct-hallucinations
Generated text: [text to check]
Documents: ["source1", "source2"]

4. Factual Consistency Evaluation:

eval-factual-consistency
Generated text: [text to evaluate]
Documents: ["reference1", "reference2"]

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.

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