Welcome to the documentation series for the MCP playbook server. This collection of documents provides a comprehensive understanding of the mcp-playbook, from its conceptual motivations and high-level data flows to detailed setup instructions and internal code architecture.
The mcp-playbook is a specialized server designed to empower AI assistants (like those in Claude Desktop, Cursor, Zed, and other LLM environments) with tools to systematically manage project documentation, capture valuable AI interaction data, and interact with our collective knowledge bases.
Why we built this playbook
In the rapidly evolving landscape of AI-assisted software development, a wealth of knowledge is generated through interactions with Large Language Models (LLMs). This includes novel solutions, effective prompts, design rationales, and troubleshooting steps. However, this knowledge often remains siloed within individual chat sessions or local developer environments.
The primary motivation behind the mcp-playbook and its associated tools is to enable Dwarves to systematically extract, share, and build upon the collective knowledge and best practices emerging from our team's programming experiences with LLMs.
We aim to:
- Automate knowledge capture: Reduce the work needed to document insights from AI interactions.
- Foster a living runbook: Create a dynamic, community-driven collection of procedures and technical patterns, powered by AI discoveries.
- Standardize interaction patterns: Provide a consistent way for both humans and AI to interact with our documentation and knowledge systems.
- Preserve valuable assets: Ensure that AI chat logs and effective prompts are treated as valuable, reusable resources.
- Speed up learning and innovation: By making shared knowledge more accessible, we can improve onboarding, help with decisions, and drive innovation across the team.
Ultimately, the mcp-playbook is a step towards creating a more intelligent and responsive knowledge ecosystem that learns and grows with us.
Who should use this?
This documentation and the mcp-playbook server itself are primarily for:
- Dwarves foundation engineers and developers: Anyone using AI-assisted coding tools (like Claude Desktop, Cursor, Zed with LLM integration) who wants to leverage automated documentation, share insights, and contribute to our collective knowledge base.
- Technical leads and architects: Those interested in understanding how we are structuring and managing knowledge in an AI-augmented development environment.
- Anyone involved in setting up or maintaining our development tools: To understand the configuration and operational aspects of the
mcp-playbook. - Future developers of MCP tools: The internal workings can serve as a reference for building similar MCP-compliant servers.
Documentation series
Explore the following documents to learn more:
- MCP playbook setup: A step-by-step tutorial on configuring and running the
mcp-playbookserver, including GitHub token creation and usage withnpx. - AI knowledge automation: Explores the motivations behind the
mcp-playbook, its role in establishing a dynamic runbook, and new patterns for capturing AI-generated knowledge. - MCP playbook data flow: An overview of how data flows through the
mcp-playbookserver, from MCP client requests to tool execution and interactions with the file system and GitHub. - MCP playbook code flow: A detailed look into the
mcp-playbook's internal code flow, data handling for chat logs and prompts, and interactions with GitHub repositories likeprompt-dbandprompt-log.
We encourage you to explore these documents and utilize the mcp-playbook to enhance your workflow and contribute to our shared knowledge.
- Next: MCP playbook setup
