Little Green Light (LGL) MCP Server

A direct, secure, and high-fidelity Model Context Protocol (MCP) Server for the Little Green Light CRM database. This server allows AI coding assistants and chat applications (like Claude Desktop, LibreChat, or Open WebUI) to securely interact with your donor database to search constituents, log gifts, categorize taxonomic groups, and generate reports locally without any third-party middleware (like Zapier).

Features

• Constituents & Core Management: Search, retrieve, create, update, and delete constituent records.
• Fundraising & Gifts: Record new gifts, list transactions (with date-range filters), search payments, and view campaigns, funds, appeals, and events.
• Contact Sub-Resources: Fully manage street addresses, phone numbers, email addresses, and web addresses for constituents.
• Activities & Notes: Log notes, write contact reports, track volunteer hours, and schedule reminders.
• Groups & Memberships: Organize constituents into customizable groups and membership levels.
• One-Shot Donor Lookup: get_donor_context returns profile + recent gifts + group memberships + recent notes in a single call (resolves by name or ID).
• Read-Only Safety: LGL_READ_ONLY=true refuses every mutation and hides write tools from tools/list. All tools publish MCP destructive/idempotent annotations.
• Zero-Middleware Architecture: Data transits directly between the local AI client and the LGL API, reducing security risks and third-party fees.

Installation & Setup

1. Prerequisites

• Node.js (v18 or higher recommended)
• A Little Green Light API Key (Generate one in your LGL account under Settings > Integration > API Keys)

2. Install Dependencies

Clone this repository to your local machine, open a terminal in the folder, and run:

npm install

3. Configure Environment Variables

Copy the .env.example template to create your local .env configuration file:

cp .env.example .env

Open .env in a text editor and replace the placeholder with your actual LGL API key and configuration:

LGL_API_KEY=your_lgl_api_key_here
PORT=3000

# Optional: Secure your Streamable HTTP endpoint with Bearer Token Authentication
LGL_MCP_TOKEN=your_secure_bearer_token_here

Optional: Read-Only Mode

Set LGL_READ_ONLY=true to refuse every create_*, update_*, delete_*, record_*, and add_* tool call. Mutation tools are also hidden from tools/list so the AI assistant doesn't try to call them. Recommended whenever you point the server at a live donor database from an exploratory chat session.

LGL_READ_ONLY=true

All tools also publish MCP annotations (readOnlyHint, destructiveHint, idempotentHint) so clients can warn before destructive calls without depending on the server-side guard.

Transport Selection

This server supports two communication transport standards:

• Stdio Transport (Default): The standard input/output process channel. Ideal for local programs like Claude Desktop or local command configurations.
• Streamable HTTP Transport (SSE): Runs a native HTTP server providing modern, stateful Server-Sent Events (SSE) over HTTP. Required for GitHub Copilot, Cursor's SSE mode, cloud containers, and systems that don't natively execute local Node.js processes.

Running in Streamable HTTP Mode

To launch the server in Streamable HTTP mode, use the --http (or --sse) flag:

node index.js --http --port 3000

• Port Selection: Custom ports can be specified using --port <number> or the PORT environment variable (defaults to 3000).
• Secure Token Protection: If you set LGL_MCP_TOKEN in your .env file, Bearer Token Authentication is strictly enforced. All client requests must include the header Authorization: Bearer <your_token>, or they will be rejected with 401 Unauthorized.

Integrating with AI Clients

This server can be integrated into any AI client, editor, or chat interface that supports the Model Context Protocol (MCP).

1. Claude Desktop

To utilize this server in the official Claude Desktop application, add the configuration to your claude_desktop_config.json file.

File Location:

• Windows: %APPDATA%\Claude\claude_desktop_config.json
• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

Configuration:

{
  "mcpServers": {
    "lgl-crm": {
      "command": "node",
      "args": ["C:\\path\\to\\your\\workspace\\folder\\index.js"],
      "env": {
        "LGL_API_KEY": "your_lgl_api_key_here"
      }
    }
  }
}

2. Cursor IDE (AI Code Editor)

Cursor supports custom MCP servers directly in its graphical user interface. You can connect using either Stdio (command) or Streamable HTTP (SSE):

Option A: Local Stdio (Command)

1. Open Cursor and navigate to Settings > Features > MCP.
2. Click + Add New MCP Server.
3. Configure the fields in the popup:
   • Name: lgl-crm
   • Type: command
   • Command: node C:\path\to\your\workspace\folder\index.js
4. Click Save. Note: You must ensure that the LGL_API_KEY is set in your operating system environment variables or shell configuration so Cursor can inherit it.

Option B: Streamable HTTP (SSE Mode)

1. Start the LGL MCP server in HTTP mode: node index.js --http --port 3000
2. Navigate to Settings > Features > MCP.
3. Click + Add New MCP Server.
4. Configure the fields in the popup:
   • Name: lgl-crm-sse
   • Type: sse
   • URL: http://localhost:3000/mcp
5. Click Save. Note: If LGL_MCP_TOKEN is enabled, ensure your editor includes the Bearer authorization header or connection config.

3. Windsurf IDE (AI Code Editor)

Windsurf supports native MCP configurations via its global config file.

File Location:

• Windows: %USERPROFILE%\.codeium\windsurf\mcp_config.json
• macOS/Linux: ~/.codeium/windsurf/mcp_config.json

Configuration:

{
  "mcpServers": {
    "lgl-crm": {
      "command": "node",
      "args": ["C:\\path\\to\\your\\workspace\\folder\\index.js"],
      "env": {
        "LGL_API_KEY": "your_lgl_api_key_here"
      }
    }
  }
}

4. LibreChat (Open-Source Chat UI)

LibreChat allows you to integrate MCP servers directly through its centralized config file librechat.yaml.

Configuration in librechat.yaml:

mcpServers:
  lgl-crm:
    type: "stdio"
    command: "node"
    args: ["C:\\path\\to\\your\\workspace\\folder\\index.js"]
    env:
      LGL_API_KEY: "your_lgl_api_key_here"

5. Open WebUI (Local/Self-Hosted AI UI)

To add this to Open WebUI (commonly used with local Ollama instances):

1. Navigate to Admin Settings > Connections > MCP.
2. Click Add Connection.
3. Name the connection lgl-crm.
4. Enter the command configuration:
   • Command: node C:\path\to\your\workspace\folder\index.js
5. Click Submit. (Make sure the environment variable LGL_API_KEY is loaded on your host machine or docker run statement running the Open WebUI instance).

Security & Data Privacy

Unlike other MCP integrations that route sensitive donor information through third-party services (like Zapier or Make), this server operates on a direct local pipeline:

• Zero Third-Party Storage: All constituent data, physical addresses, emails, phone numbers, and financial donations are sent directly from your computer to the official LGL API over secure, encrypted HTTPS.
• Principle of Least Privilege: You can easily restrict access to database modifications (e.g. deleting records) by commenting out or removing the corresponding tools in the index.js file before deployment.

License

This project is open-source and free to adapt for non-profit organizations under the MIT License.