BambooHR MCP Server Integration

What does “BambooHR” MCP Server do?

The BambooHR MCP Server is a Model Context Protocol (MCP) library designed to facilitate seamless integration between AI assistants and the BambooHR API. Built using Node.js and TypeScript, it provides a clean, type-safe interface to access and interact with various BambooHR endpoints. By acting as a bridge between AI systems and HR data, the BambooHR MCP Server enables enhanced workflows for developers and organizations, such as querying employee directories, managing project assignments, submitting work hours, and retrieving information about team availability. This integration empowers AI assistants to automate HR-related tasks, streamline data retrieval, and support more effective human resources management within development environments.

List of Prompts

No explicit prompt templates are mentioned or documented in the repository.

List of Resources

No explicit MCP resources are documented in the repository.

List of Tools

• fetchWhosOut: Retrieves a list of employees who are currently out of the office.
• fetchProjects: Fetches project data associated with an employee.
• submitWorkHours: Allows submission of work hours for a specific project and task.
• getMe: Retrieves the details of the authenticated user.
• fetchEmployeeDirectory: Lists all employees with their names, emails, and job titles.
• fetchTimeEntries: Retrieves time entry records for employees.

Use Cases of this MCP Server

• Employee Directory Retrieval: Quickly access a list of all employees, including names, emails, and job titles, streamlining HR data lookup and reporting.
• Project and Task Management: Fetch projects and tasks assigned to an employee, making it easier to track time and resource allocation for HR and team leads.
• Work Hours Submission: Automate the logging of work hours for specific projects and tasks, simplifying HR processes and reducing manual entry errors.
• Team Availability Tracking: Instantly identify who is out of the office to improve resource planning and reduce scheduling conflicts.
• Personal Information Lookup: Retrieve authenticated user details for personalized HR dashboards or assistant interactions.

How to set it up

Windsurf

1. Ensure Node.js and npm are installed.
2. Clone the repository and install dependencies:

   git clone https://github.com/encoreshao/bamboohr-mcp.git
   cd bamboohr-mcp
   npm install
   

3. Edit your configuration file to add the BambooHR MCP server:

   {
     "mcpServers": {
       "bamboohr-mcp": {
         "command": "npx",
         "args": ["@bamboohr/mcp-server@latest"]
       }
     }
   }
   

4. Set environment variables for API keys (see below).
5. Save and restart Windsurf. Verify the server is running.

Claude

1. Prerequisite: Install Node.js and npm.
2. Clone and set up the BambooHR MCP server as above.
3. In Claude’s config, add:

   {
     "mcpServers": {
       "bamboohr-mcp": {
         "command": "npx",
         "args": ["@bamboohr/mcp-server@latest"]
       }
     }
   }
   

4. Configure required environment variables (see below).
5. Save, restart Claude, and verify the connection.

Cursor

1. Install Node.js and npm.
2. Clone and install bamboohr-mcp:

   git clone https://github.com/encoreshao/bamboohr-mcp.git
   cd bamboohr-mcp
   npm install
   

3. Add to Cursor’s configuration:

   {
     "mcpServers": {
       "bamboohr-mcp": {
         "command": "npx",
         "args": ["@bamboohr/mcp-server@latest"]
       }
     }
   }
   

4. Set environment variables (see below).
5. Save and restart Cursor.

Cline

1. Ensure Node.js and npm are present.
2. Clone and install as described above.
3. Add the following to your Cline config:

   {
     "mcpServers": {
       "bamboohr-mcp": {
         "command": "npx",
         "args": ["@bamboohr/mcp-server@latest"]
       }
     }
   }
   

4. Set environment variables as described.
5. Save and restart Cline.

Securing API Keys using Environment Variables

Set the following in your environment or a .env file:

BAMBOOHR_TOKEN=your_api_token_here
BAMBOOHR_COMPANY_DOMAIN=yourcompany
BAMBOOHR_EMPLOYEE_ID=123

You can also specify environment variables within your JSON config:

{
  "mcpServers": {
    "bamboohr-mcp": {
      "command": "npx",
      "args": ["@bamboohr/mcp-server@latest"],
      "env": {
        "BAMBOOHR_TOKEN": "your_api_token_here",
        "BAMBOOHR_COMPANY_DOMAIN": "yourcompany",
        "BAMBOOHR_EMPLOYEE_ID": "123"
      }
    }
  }
}

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:

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

Overview

| Roots Support | ⛔ | Not mentioned |

Our opinion

BambooHR MCP provides a basic, type-safe integration with BambooHR for developers and MCP-based AI assistants. While well-structured and covering key HR API endpoints, it currently lacks explicit prompt/resource definitions and does not document advanced MCP features like Roots or Sampling. Good for core HR automations but not a full-featured MCP reference implementation.

Based on the above, I would rate this MCP server a 4/10 for general MCP completeness: it covers the core API tools and configuration, but lacks advanced features, prompt templates, and resource exposure.

MCP Score