Ragdoll AI MCP Server

by mesh-hq

167 downloads

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Author: mesh-hq
Downloads: 167
Categories: Other

Setting up with Highlight

This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:

Download and install Highlight from highlightai.com/download
Navigate to the plugins tab and select "Add Custom Plugin"
Configure the plugin with the settings below

Plugin Name Ragdoll AI MCP Server

Command (node, npx, python, etc.)

Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
Enable "Start Automatically" if you want the plugin to start when Highlight launches

Setting up with Cursor

Open Cursor's settings
Navigate to the MCP section
Click "Add new Global MCP server"
Copy and paste the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "ragdoll ai mcp server": {
            "ragdoll-mcp-server": {
                "command": "bun",
                "args": [
                    "run",
                    "index.ts"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "ragdoll ai mcp server": {
            "ragdoll-mcp-server": {
                "command": "bun",
                "args": [
                    "run",
                    "index.ts"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "ragdoll ai mcp server": {
            "ragdoll-mcp-server": {
                "command": "bun",
                "args": [
                    "run",
                    "index.ts"
                ]
            }
        }
    }
}

Setting up with Claude Desktop

Open Claude Desktop's settings
Go to the Integrations tab
Click "Add MCP Server"
Use the configuration below based on your operating system

Note: Make sure Claude Desktop is running before attempting to connect.

JSON

{
    "mcpServers": {
        "ragdoll ai mcp server": {
            "ragdoll-mcp-server": {
                "command": "bun",
                "args": [
                    "run",
                    "index.ts"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "ragdoll ai mcp server": {
            "ragdoll-mcp-server": {
                "command": "bun",
                "args": [
                    "run",
                    "index.ts"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "ragdoll ai mcp server": {
            "ragdoll-mcp-server": {
                "command": "bun",
                "args": [
                    "run",
                    "index.ts"
                ]
            }
        }
    }
}

Setting up with Windsurf

Open Windsurf's preferences
Go to the Integrations section
Click "Add MCP Integration"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "ragdoll ai mcp server": {
            "ragdoll-mcp-server": {
                "command": "bun",
                "args": [
                    "run",
                    "index.ts"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "ragdoll ai mcp server": {
            "ragdoll-mcp-server": {
                "command": "bun",
                "args": [
                    "run",
                    "index.ts"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "ragdoll ai mcp server": {
            "ragdoll-mcp-server": {
                "command": "bun",
                "args": [
                    "run",
                    "index.ts"
                ]
            }
        }
    }
}

Setting up with Cline

Open Cline's settings
Navigate to the Integrations section
Click "Add MCP Server"
Copy and paste the configuration below based on your operating system

Note: Make sure to restart Cline after adding the configuration.

JSON

{
    "mcpServers": {
        "ragdoll ai mcp server": {
            "ragdoll-mcp-server": {
                "command": "bun",
                "args": [
                    "run",
                    "index.ts"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "ragdoll ai mcp server": {
            "ragdoll-mcp-server": {
                "command": "bun",
                "args": [
                    "run",
                    "index.ts"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "ragdoll ai mcp server": {
            "ragdoll-mcp-server": {
                "command": "bun",
                "args": [
                    "run",
                    "index.ts"
                ]
            }
        }
    }
}

Setting up with Goose

Open Goose's preferences
Go to the Integrations tab
Select "MCP Servers"
Click "Add New Server"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "ragdoll ai mcp server": {
            "ragdoll-mcp-server": {
                "command": "bun",
                "args": [
                    "run",
                    "index.ts"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "ragdoll ai mcp server": {
            "ragdoll-mcp-server": {
                "command": "bun",
                "args": [
                    "run",
                    "index.ts"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "ragdoll ai mcp server": {
            "ragdoll-mcp-server": {
                "command": "bun",
                "args": [
                    "run",
                    "index.ts"
                ]
            }
        }
    }
}

From the repository

Install via npx -y ragdoll-mcp-server or clone the repo and run bun run index.ts. Set environment variables RAGDOLL_API_KEY and RAGDOLL_KNOWLEDGE_BASE_ID. Configure the server in your IDE's MCP settings (e.g., mcp.json for Cursor, mcp_config.json for Windsurf, cline_mcp_settings.json for Cline).

Claude Desktop / Cursor

Paste into your MCP client config file to install this server.

{
    "mcpServers": {
        "ragdoll ai mcp server": {
            "ragdoll-mcp-server": {
                "command": "bun",
                "args": [
                    "run",
                    "index.ts"
                ]
            }
        }
    }
}

McpServers

{
    "ragdoll-mcp-server": {
        "command": "bun",
        "args": [
            "run",
            "index.ts"
        ]
    }
}

# Ragdoll AI MCP Server A Model Context Protocol (MCP) server for Ragdoll AI knowledge base queries. ## Overview This MCP server provides a simple interface to query Ragdoll AI knowledge bases through the Model Context Protocol. It allows seamless integration with various LLM client applications including Cursor, Windsurf, and Cline. ## Prerequisites - [Bun](https://bun.sh) runtime (v1.2.1 or later) - Ragdoll AI API key - Ragdoll AI knowledge base ID ## Installation Clone the repository and install dependencies: ```bash git clone <repository-url> cd mcp-ragdoll-server bun install ``` ## Configuration Set up your environment variables: ```bash export RAGDOLL_API_KEY="your-ragdoll-api-key" export RAGDOLL_KNOWLEDGE_BASE_ID="your-knowledge-base-id" ``` For persistent configuration, add these to your `.bashrc`, `.zshrc`, or create a `.env` file in the project root. ## Running the Server Start the server: ```bash bun run index.ts ``` ## Client Setup ### NPX Installation (Recommended) The simplest way to use this server is via NPX: ```bash npx -y ragdoll-mcp-server ``` ### Cursor To install the Ragdoll MCP server in Cursor IDE: 1. Open Cursor IDE 2. Go to Settings > Extensions > AI Settings 3. Create a file named `mcp.json` with the following configuration: ```json { "mcpServers": { "ragdoll-mcp-server": { "command": "npx", "args": ["-y", "ragdoll-mcp-server"], "env": { "RAGDOLL_API_KEY": "your-ragdoll-api-key", "RAGDOLL_KNOWLEDGE_BASE_ID": "your-knowledge-base-id" } } } } ``` Alternatively, you can run the server locally: ```json { "mcpServers": { "ragdoll-mcp-server": { "command": "bun", "args": ["run", "/path/to/mcp-ragdoll-server/index.ts"], "env": { "RAGDOLL_API_KEY": "your-ragdoll-api-key", "RAGDOLL_KNOWLEDGE_BASE_ID": "your-knowledge-base-id" } } } } ``` ### Windsurf To install the Ragdoll MCP server in Windsurf IDE: Create or edit your `mcp_config.json` file with the following configuration: ```json { "mcpServers": { "ragdoll-mcp-server": { "command": "npx", "args": ["-y", "ragdoll-mcp-server"], "env": { "RAGDOLL_API_KEY": "your-ragdoll-api-key", "RAGDOLL_KNOWLEDGE_BASE_ID": "your-knowledge-base-id" } } } } ``` ### Cline To install the Ragdoll MCP server in Cline: Create or edit your `cline_mcp_settings.json` file with the following configuration: ```json { "mcpServers": { "ragdoll-mcp-server": { "command": "npx", "args": ["-y", "ragdoll-mcp-server"], "env": { "RAGDOLL_API_KEY": "your-ragdoll-api-key", "RAGDOLL_KNOWLEDGE_BASE_ID": "your-knowledge-base-id" } } } } ``` ## Usage Once connected, you can query your Ragdoll knowledge base with the following parameters: - `query` (string, required): The search query to find relevant information - `topK` (number, optional): Number of results to return (1-10) - `rerank` (boolean, optional): Whether to rerank results Example usage in your LLM client: ``` You can ask questions about your knowledge base content. ``` ## Development This project uses the Model Context Protocol SDK. For more information, refer to the [MCP documentation](https://modelcontext.protocol.ai/). ## Support For issues or questions about this MCP server, please submit an issue on GitHub. ## License [MIT](LICENSE)

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Description

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Details

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Setting up with Highlight

From the repository

Claude Desktop / Cursor

McpServers

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