Vectara MCP

What is Vectara MCP about?

Vectara MCP exposes two primary tools – ask_vectara and search_vectara – that let AI agents perform RAG queries or plain semantic searches against Vectara indexes. The server implements the Model Context Protocol (MCP), so any MCP‑compatible client (e.g., Claude Desktop) can call these tools without additional glue code.

How to use Vectara MCP?

1. Installation: pip install vectara-mcp.
2. Run the server (as an MCP server) – the package registers its tools automatically when invoked via the MCP runtime (e.g., uv tool run vectara-mcp).
3. Configure the client – add the following to claude_desktop_config.json:

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

4. Restart the client application. The hammer icon will reveal the two Vectara tools.
5. When a tool is first invoked, the client prompts for the Vectara API key and one or more corpus keys.

Key features of Vectara MCP

• ask_vectara: RAG query that returns a generated answer plus the underlying search results.
• search_vectara: Pure semantic search returning matching passages without generation.
• Configurable parameters such as number of context sentences, lexical interpolation, result limits, and generation preset selection.
• Works with any MCP‑compatible client, not limited to Claude Desktop.
• Open‑source and distributed via PyPI.

Use cases of Vectara MCP

• Enabling autonomous agents to retrieve up‑to‑date factual information from Vectara‑hosted knowledge bases.
• Providing chat‑based assistants with low‑hallucination RAG capabilities for internal documentation, support tickets, or product FAQs.
• Building custom search‑oriented plugins for AI chat interfaces that require source citations.
• Integrating Vectara search into workflow automation tools that rely on MCP for tool orchestration.

FAQ from Vectara MCP

Q: Do I need a Vectara account? A: Yes, you must have a Vectara API key and at least one corpus key to use the tools.

Q: Can I run multiple corpora simultaneously? A: Provide a list of corpus keys in the corpus_keys argument; the server will search across all supplied corpora.

Q: What language models are used for generation? A: The default generation preset (vectara-summary-table-md-query-ext-jan-2025-gpt-4o) leverages a Vectara‑hosted LLM; you can specify a different preset via the generation_preset_name argument.

Q: How do I change the number of context sentences? A: Use the optional n_sentences_before and n_sentences_after parameters when calling the tool.

Q: Is there a limit on the number of returned search results? A: The max_used_search_results argument caps the number of results used for generation (default 10).

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], 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:

{
  "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.

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:

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

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

Vectara Tool Examples

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"]

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