LlamaCloud MCP Server

What does “LlamaCloud” MCP Server do?

The LlamaCloud MCP Server is a TypeScript-based Model Context Protocol (MCP) server that connects AI assistants to multiple managed indexes on LlamaCloud . By exposing each LlamaCloud index as a dedicated tool, it empowers AI agents to perform search and retrieval tasks across a range of structured document sets—such as SEC filings or company-specific data—directly via the MCP interface. This setup enhances development workflows by enabling easy access to external data, facilitating tasks like contextual data retrieval, document search, and knowledge augmentation for AI-powered applications. With configurable command-line arguments, developers can quickly set up and manage multiple indexes as MCP tools, making LlamaCloud a flexible bridge between LLMs and enterprise-scale document repositories.

List of Prompts

No explicit prompt templates are mentioned in the available documentation or code for the LlamaCloud MCP Server.

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List of Resources

No specific resources are listed or described in the available documentation or code for the LlamaCloud MCP Server.

List of Tools

• get_information_index_name
  Each LlamaCloud index defined in the configuration becomes a tool (e.g., get_information_10k-SEC-Tesla). Each tool exposes a query parameter that allows searching within its associated managed index.

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Use Cases of this MCP Server

• Enterprise Document Search
  Developers can configure tools for different company document indexes (e.g., SEC filings for Tesla or Apple), allowing AI agents to retrieve and summarize relevant corporate information on demand.
• Knowledge Augmentation in AI Agents
  LLM-powered assistants can tap into authoritative data sources (like 10k SEC documents) for more accurate, context-aware responses.
• Multi-Index Information Retrieval
  By connecting to multiple indexes at once, the server enables cross-repository search scenarios for research or compliance tasks.
• Custom Data Pipelines
  Teams can plug proprietary document sets into LlamaCloud indexes and expose them securely to AI workflows for internal analytics or reporting.

How to set it up

Windsurf

1. Ensure you have Node.js and npx installed.
2. Open your Windsurf MCP client configuration file.
3. Add the LlamaCloud MCP Server under the mcpServers object as shown below.
4. Insert your LlamaCloud project name and API key into the env section.
5. Save the configuration and restart Windsurf.

{
  "mcpServers": {
    "llamacloud": {
      "command": "npx",
      "args": [
        "-y",
        "@llamaindex/mcp-server-llamacloud",
        "--index",
        "10k-SEC-Tesla",
        "--description",
        "10k SEC documents from 2023 for Tesla",
        "--index",
        "10k-SEC-Apple",
        "--description",
        "10k SEC documents from 2023 for Apple"
      ],
      "env": {
        "LLAMA_CLOUD_PROJECT_NAME": "<YOUR_PROJECT_NAME>",
        "LLAMA_CLOUD_API_KEY": "<YOUR_API_KEY>"
      }
    }
  }
}

Claude

1. Ensure Node.js and npx are installed.
2. Locate Claude’s MCP config:
   • Mac: ~/Library/Application Support/Claude/claude_desktop_config.json
   • Windows: %APPDATA%/Claude/claude_desktop_config.json
3. Add the LlamaCloud MCP Server configuration in the mcpServers object (see Windsurf example above).
4. Place your API credentials in the env section.
5. Save changes and restart Claude.

Cursor

1. Install Node.js and npx if not already present.
2. Open Cursor’s MCP client configuration file.
3. Insert the LlamaCloud MCP Server config as shown in the Windsurf example.
4. Supply your API credentials.
5. Save and restart Cursor.

Cline

1. Make sure Node.js and npx are available.
2. Find or create your Cline MCP client configuration file.
3. Add the LlamaCloud MCP Server config under mcpServers, using the example above.
4. Input your LlamaCloud API credentials.
5. Save and restart Cline.

Securing API Keys

Use environment variables in the env section of your config. Example:

"env": {
  "LLAMA_CLOUD_PROJECT_NAME": "<YOUR_PROJECT_NAME>",
  "LLAMA_CLOUD_API_KEY": "<YOUR_API_KEY>"
}

Never expose secrets in plaintext where possible.

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:

{
  "llamacloud": {
    "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 “llamacloud” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Our opinion

LlamaCloud MCP Server is focused and easy to set up for connecting LLMs to managed document indexes. It lacks advanced resources and prompt templates, but its tool-based approach for each index is clean and well-documented. Based on the tables, it’s a solid, straightforward choice for developers needing robust document retrieval, but not for those seeking advanced MCP features like resources, roots, or sampling.

RATING: 6/10

MCP Score