Vectara MCP Server Integration

What does “Vectara” MCP Server do?

Vectara MCP Server is an open source implementation of the Model Context Protocol (MCP) designed to bridge AI assistants with Vectara’s Trusted RAG (Retrieval-Augmented Generation) platform. By acting as an MCP server, it enables AI systems to securely and efficiently perform sophisticated search and retrieval tasks against Vectara’s reliable retrieval engine. This facilitates seamless, two-way connections between AI clients and external data sources, empowering developers to augment their workflows with advanced RAG capabilities, minimize hallucination, and streamline access to relevant information for generative AI applications.

List of Prompts

No specific prompt templates are mentioned in the available documentation or repository files.

List of Resources

No explicit MCP resources are listed in the available documentation or repository files.

List of Tools

• ask_vectara: Executes a RAG (Retrieval-Augmented Generation) query using Vectara. Returns search results accompanied by a generated response. It requires a user query, Vectara corpus keys, and API key, and supports several configurable parameters such as the number of context sentences and generation preset.

Use Cases of this MCP Server

• Retrieval-Augmented Generation (RAG): Developers can enhance AI models by integrating Vectara’s trusted RAG platform, providing factual, up-to-date information from external corpora to minimize hallucinations in outputs.
• Enterprise Search Integration: Teams can enable AI assistants to query internal or external document repositories, making it easier to extract relevant insights for decision-making or support.
• Knowledge Management: Leverage Vectara MCP to automate knowledge base queries, surfacing contextual answers from large data stores.
• Secure AI Data Access: Facilitate secure, API-key-protected access to sensitive or proprietary data through MCP, ensuring compliance and privacy.

How to set it up

Windsurf

1. Ensure Python is installed and install Vectara MCP via pip install vectara-mcp.
2. Locate the Windsurf configuration file.
3. Add the Vectara MCP Server to your mcpServers object:

   {
     "mcpServers": {
       "vectara-mcp": {
         "command": "vectara-mcp",
         "args": []
       }
     }
   }
   

4. Save changes and restart Windsurf.
5. Verify that the Vectara MCP Server appears in the interface.

Claude

1. Install Python and Vectara MCP (pip install vectara-mcp).
2. Open the Claude Desktop configuration.
3. Insert the Vectara MCP Server into the mcpServers section:

   {
     "mcpServers": {
       "vectara-mcp": {
         "command": "vectara-mcp",
         "args": []
       }
     }
   }
   

4. Save the file and relaunch Claude Desktop.
5. Confirm connectivity to the MCP server.

Cursor

1. Install Vectara MCP with pip install vectara-mcp.
2. Edit the Cursor config file.
3. Add the server under mcpServers:

   {
     "mcpServers": {
       "vectara-mcp": {
         "command": "vectara-mcp",
         "args": []
       }
     }
   }
   

4. Save and restart Cursor.
5. Check that Vectara MCP is active in Cursor.

Cline

1. Install Vectara MCP using pip install vectara-mcp.
2. Find and edit the Cline configuration.
3. Add the MCP server in JSON:

   {
     "mcpServers": {
       "vectara-mcp": {
         "command": "vectara-mcp",
         "args": []
       }
     }
   }
   

4. Save the configuration and restart Cline.
5. Ensure the MCP server is listed and accessible.

Securing API Keys

It is strongly recommended to store sensitive API keys in environment variables rather than configuration files. Example:

{
  "mcpServers": {
    "vectara-mcp": {
      "command": "vectara-mcp",
      "args": [],
      "env": {
        "VECTARA_API_KEY": "${VECTARA_API_KEY}"
      },
      "inputs": {
        "api_key": "${VECTARA_API_KEY}"
      }
    }
  }
}

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "vectara-mcp": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “vectara-mcp” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Our opinion

Vectara MCP provides a clear, focused integration for RAG with strong documentation for setup and API key security, but lacks details on prompts, resources, or sampling/roots. It’s great for enabling RAG in agentic workflows, but the absence of richer MCP features limits its versatility.

MCP Score

Rating: 5/10 — It is solid and production-ready for its RAG use case, but covers only a minimal MCP feature set and lacks documentation on prompts, resources, and advanced MCP concepts.