open-ai-work-prompts-mcp-servers

joshuaberkowitzus/open-ai-work-prompts-mcp-servers

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The Workplace Prompts MCP Server provides a structured way to access and utilize workplace productivity prompts through HTTP and stdio implementations.

Tools
5
Resources
0
Prompts
0

Workplace Prompts MCP Server

A Model Context Protocol (MCP) server that exposes workplace productivity prompts as resources, prompts, and tools. Available in both HTTP and stdio implementations. Prompts : https://academy.openai.com/public/clubs/work-users-ynjqu/resources/chatgpt-for-any-role

Features

  • 20 workplace prompts organized into 4 categories:

    • Communication & Writing
    • Meetings & Collaboration
    • Problem Solving & Decision Making
    • Organization & Productivity

  • Three MCP primitives:

    • Resources: Access prompts as readable resources
    • Prompts: Use prompts with parameter substitution
    • Tools: Execute prompts with user input

Installation

pip install -r requirements.txt

Usage

HTTP Server

Start the HTTP server:

python mcp_http_server.py

The server will run on http://localhost:3000

Available endpoints:

  • POST /mcp/v1/initialize - Initialize connection
  • POST /mcp/v1/resources/list - List all prompt resources
  • POST /mcp/v1/resources/read - Read a specific prompt
  • POST /mcp/v1/prompts/list - List all available prompts
  • POST /mcp/v1/prompts/get - Get a prompt with arguments
  • POST /mcp/v1/tools/list - List all available tools
  • POST /mcp/v1/tools/call - Execute a tool

Example request:

curl -X POST http://localhost:3000/mcp/v1/resources/list \
  -H "Content-Type: application/json" \
  -d '{}'

stdio Server

Run the stdio server:

python mcp_stdio_server.py

The server reads JSON-RPC requests from stdin and writes responses to stdout.

Example request (via stdin):

{"jsonrpc": "2.0", "id": 1, "method": "resources/list", "params": {}}

MCP Methods

Resources

List resources:

{"method": "resources/list", "params": {}}

Read resource:

{
  "method": "resources/read",
  "params": {
    "uri": "prompt://communication-writing/write-professional-email"
  }
}

Prompts

List prompts:

{"method": "prompts/list", "params": {}}

Get prompt with arguments:

{
  "method": "prompts/get",
  "params": {
    "name": "communication-writing/write-professional-email",
    "arguments": {
      "recipient": "Sarah Johnson",
      "topic": "Q4 budget review"
    }
  }
}

Tools

List tools:

{"method": "tools/list", "params": {}}

Call tool:

{
  "method": "tools/call",
  "params": {
    "name": "meetings-collaboration/create-meeting-agenda",
    "arguments": {
      "input": "Sprint planning meeting for next week"
    }
  }
}

Prompt Categories

Communication & Writing

  • write-professional-email - Draft professional emails
  • rewrite-for-clarity - Improve text clarity
  • adapt-message-for-audience - Adjust tone for different audiences
  • draft-meeting-invite - Create meeting invitations
  • summarize-long-email - Summarize email threads

Meetings & Collaboration

  • create-meeting-agenda - Structure meeting agendas
  • summarize-meeting-notes - Organize meeting notes
  • create-action-items-list - Extract tasks from notes
  • prep-questions-for-meeting - Generate thoughtful questions
  • draft-follow-up-email - Write meeting follow-ups

Problem Solving & Decision Making

  • identify-root-cause - Analyze workplace issues
  • compare-options - Evaluate multiple solutions
  • decision-criteria - Define decision frameworks
  • risk-assessment - Assess plan risks
  • recommend-best-option - Provide recommendations

Organization & Productivity

  • document-daily-priorities - Prioritize daily tasks
  • create-weekly-plan - Build weekly schedules
  • summarize-long-document - Create document summaries
  • brainstorm-solutions - Generate solution ideas
  • write-project-update - Draft status updates

Configuration

Claude Desktop

To use with Claude Desktop, add to your config file:

For stdio version (claude_desktop_config.json):

{
  "mcpServers": {
    "workplace-prompts": {
      "command": "python",
      "args": ["/path/to/mcp_stdio_server.py"]
    }
  }
}

Config file locations:

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

For HTTP version: Start the HTTP server separately, then configure Claude Desktop to connect to http://localhost:3000

VS Code with GitHub Copilot

GitHub Copilot in VS Code supports MCP servers through the Copilot Extensions API.

Setup with stdio version:

  1. Create or edit .vscode/settings.json in your workspace:
{
  "github.copilot.advanced": {
    "mcp.servers": {
      "workplace-prompts": {
        "command": "python",
        "args": ["-u", "/absolute/path/to/mcp_stdio_server.py"],
        "env": {}
      }
    }
  }
}

  1. Reload VS Code window (Cmd/Ctrl + Shift + P → "Developer: Reload Window")

  2. Access prompts through Copilot Chat using @workplace-prompts

Setup with HTTP version:

  1. Start the HTTP server:
python mcp_http_server.py
  1. Configure in .vscode/settings.json:
{
  "github.copilot.advanced": {
    "mcp.servers": {
      "workplace-prompts": {
        "url": "http://localhost:3000/mcp/v1"
      }
    }
  }
}

Usage in VS Code:

@workplace-prompts /communication-writing/write-professional-email

Gemini CLI

Google's Gemini CLI (experimental) supports MCP through configuration files.

Setup with stdio version:

  1. Create or edit ~/.gemini/config.json:
{
  "mcpServers": {
    "workplace-prompts": {
      "command": "python3",
      "args": ["/absolute/path/to/mcp_stdio_server.py"],
      "env": {
        "PYTHONUNBUFFERED": "1"
      }
    }
  }
}
  1. Verify the server is registered:
gemini mcp list

Usage with Gemini CLI:

# List available prompts
gemini mcp resources list workplace-prompts

# Use a prompt
gemini chat --mcp workplace-prompts \
  "Use the write-professional-email prompt for a budget update to my manager"

# Call a tool directly
gemini mcp tools call workplace-prompts \
  communication-writing/write-professional-email \
  --input "Recipient: John Smith, Topic: Q4 Budget Review"

Setup with HTTP version:

  1. Start the HTTP server first:
python mcp_http_server.py
  1. Configure in ~/.gemini/config.json:
{
  "mcpServers": {
    "workplace-prompts": {
      "transport": "http",
      "url": "http://localhost:3000"
    }
  }
}

Environment Variables for Gemini:

export GEMINI_MCP_WORKPLACE_PROMPTS_ENABLED=true
export GEMINI_MCP_LOG_LEVEL=debug  # For troubleshooting

Architecture

Both implementations follow the MCP specification and provide the same functionality through different transport layers:

  • HTTP: REST-style endpoints for easy integration and testing
  • stdio: JSON-RPC over stdin/stdout for direct process communication

License

MIT