geekbot-mcp 0.2.1

Model Context Protocol (MCP) server integrating Geekbot data and tools to Claude AI

Geekbot MCP

Unlock your Geekbot data within your LLM applications 🚀

Geekbot MCP (Model Context Protocol) server acts as a bridge, connecting LLM client applications (like Claude) directly to your Geekbot workspace. This allows you to interact with your standups, reports, and team members seamlessly within your conversations using natural language.

Key Features ✨

• Access Standup Information: List all standups in your Geekbot workspace. 📊
• Retrieve Standup Reports: Fetch reports with filters for specific standups, users, or date ranges. 📄
• View Team Members: Get a list of members you collaborate with in Geekbot. 👥

Installation 💻

Requires Python 3.10+ and uv.

1. Install uv (if you haven't already):

   curl -LsSf https://astral.sh/uv/install.sh | sh
   

   (See uv installation docs for more options.)

2. Install Geekbot MCP:

   uv tool install geekbot-mcp
   

Upgrading ⬆️

To update to the latest version:

  uv tool install --upgrade geekbot-mcp

Configuration ⚙️

Connect Geekbot MCP to your LLM (e.g., Claude Desktop):

1. Get your Geekbot API Key: Find it in your Geekbot API/Webhooks settings 🔑.

2. Find your uv executable path:

     which uv
   

3. Configure your LLM client application: Edit your claude_desktop_config.json (or equivalent configuration file for other MCP clients) to add Geekbot MCP server

   {
     "globalShortcut": "",
     "mcpServers": {
       // Add or update this section
       "geekbot-mcp": {
   
         "command": "<path-returned-by-which-uv>", // Replace with your actual uv path
         "args": [
           "tool",
           "run",
           "geekbot-mcp"
         ],
         // Environment variables needed by the server
         "env": {
           "GB_API_KEY": "<your-geekbot-api-key>" // Replace with your actual API key
         }
       }
       // ... other MCP servers if any
     }
     // ... other configurations
   }
   

   (Refer to the MCP Quickstart for more details on client configuration.)

Usage 💡

Once configured, your LLM client application will have access to the following tools and prompts to interact with your Geekbot data:

Tools 🛠️

• list_standups

Purpose: Lists all the standups accessible via your API key. Useful for getting an overview or finding a specific standup ID.

Example Prompt: "Hey, can you list my Geekbot standups?"

Data Fields Returned:

• id: Unique standup identifier.

• name: Name of the standup.

• channel: Associated communication channel (e.g., Slack channel).

• time: Scheduled time for the standup report.

• timezone: Timezone for the scheduled time.

• questions: List of questions asked in the standup.

• participants: List of users participating in the standup.

• owner_id: ID of the standup owner.

• list_polls

Purpose: Lists all the polls accessible via your API key. Useful for getting an overview or finding a specific poll ID.

Example Prompt: "Hey, can you list my Geekbot polls?"

Data Fields Returned:

• id: Unique poll identifier.

• name: Name of the poll.

• time: Scheduled time for the poll.

• timezone: Timezone for the scheduled time.

• questions: List of questions asked in the poll.

• participants: List of users participating in the poll.

• creator: The poll creator.

• fetch_reports

Purpose: Retrieves specific standup reports. You can filter by standup, user, and date range.

Example Prompts:

• "Fetch the reports for submitted yesterday in the Retrospective."
• "Show me reports from user John Doe for the 'Weekly Sync' standup."
• "Get all reports submitted to the Daily Standup standup after June 1st, 2024."

Available Filters:

• standup_id: Filter by a specific standup ID.
• user_id: Filter reports by a specific user ID.
• after: Retrieve reports submitted after this date (YYYY-MM-DD) 🗓️.
• before: Retrieve reports submitted before this date (YYYY-MM-DD) 🗓️.

Data Fields Returned:

• id: Unique report identifier.

• reporter_name: Name of the user who submitted the report.

• reporter_id: ID of the user who submitted the report.

• standup_id: ID of the standup the report belongs to.

• created_at: Timestamp when the report was submitted.

• content: The actual answers/content of the report.

• list_members

Purpose: Lists all team members you share standups with in your Geekbot workspace.

Example Prompt: "Who are the members in my Geekbot workspace?"

Data Fields Returned:

• id: Unique member identifier.

• name: Member's full name.

• email: Member's email address.

• role: Member's role within Geekbot (e.g., Admin, Member).

• fetch_poll_results

Purpose: Retrieves specific poll results. Requires a poll id and optionally a date range.

Example Prompt: "Hey, what was decided about the new logo in Geekbot polls?"

Data Fields Returned:

• total_results: Total number of results.
• question_results: List of question results.

Prompts 💬

• weekly_rollup_report

Purpose: Generates a comprehensive weekly rollup report that summarizes team standup responses, highlights key updates, identifies risks and mitigation strategies, outlines next steps, and tracks upcoming launches.

Arguments:

• standup_id: ID of the standup to include in the rollup report.

Development 🧑‍💻

Interested in contributing or running the server locally?

Setup Development Environment

# 1. Clone the repository
git clone https://github.com/geekbot-com/geekbot-mcp.git
cd geekbot-mcp

# 2. Install uv (if needed)
# curl -LsSf https://astral.sh/uv/install.sh | sh

# 3. Create a virtual environment and install dependencies
uv sync

Running Tests ✅

# Ensure dependencies are installed (uv sync)
pytest

Contributing 🤝

Contributions are welcome! Please fork the repository and submit a Pull Request with your changes.

License 📜

This project is licensed under the MIT License.

Acknowledgements 🙏

• Built upon the Anthropic Model Context Protocol framework.
• Leverages the official Geekbot API.