Expose llms-txt to IDEs for development. Enables developers to integrate LLM documentation directly into their IDEs, streamlining the development process. Connects to IDEs like Cursor and Windsurf, and apps like Claude Code/Desktop.
git clone https://github.com/langchain-ai/mcpdoc.gitThe mcpdoc skill is designed to expose llms-txt documentation to integrated development environments (IDEs), facilitating a more efficient coding experience. By integrating llms-txt resources directly into IDEs like Cursor, Windsurf, and Claude Desktop, developers can access vital documentation seamlessly, enhancing their coding assistance and overall productivity. This skill is particularly beneficial for those working in environments where rapid access to accurate documentation is crucial for maintaining workflow efficiency. One of the key benefits of using mcpdoc is the potential for significant time savings during the development process. While specific time savings are currently unknown, the ability to retrieve and audit documentation from multiple llms.txt files means developers can spend less time searching for information and more time focusing on coding. Additionally, setting up a local MCP server allows for testing and validation of llms.txt files before deployment, ensuring that developers can catch issues early in the workflow. This skill is ideal for developers and product managers who are looking to streamline their workflow automation processes. By incorporating mcpdoc into their toolset, teams can ensure that they are working with the most relevant and up-to-date documentation, which is essential for maintaining a competitive edge in AI development. The integration of domain access controls also adds a layer of security, ensuring that documentation is retrieved only from trusted sources, which is critical in sensitive development environments. With an intermediate implementation difficulty and a setup time of approximately 30 minutes, mcpdoc is accessible for teams ready to enhance their AI-first workflows. The skill's integration capabilities allow for a dynamic connection to llms-txt resources, making it a valuable addition to any developer's toolkit. As AI automation continues to evolve, skills like mcpdoc will play a pivotal role in shaping efficient and effective development practices.
Integrate llms-txt documentation into Cursor IDE for enhanced coding assistance.
Retrieve and audit documentation from multiple llms.txt files for better context during development.
Set up a local MCP server to test and validate llms.txt files before deployment.
Connect various IDEs like Windsurf and Claude Desktop to access llms-txt resources dynamically.
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/langchain-ai/mcpdocCopy the install command above and run it in your terminal.
Launch Claude Code, Cursor, or your preferred AI coding agent.
Use the prompt template or examples below to test the skill.
Adapt the skill to your specific use case and workflow.
Create a detailed mcpdoc configuration for a [COMPANY] in the [INDUSTRY] sector. The configuration should include [DATA] processing workflows, error handling, and integration with [IDE] for seamless development. Ensure the documentation is clear and actionable for the engineering team.
# mcpdoc Configuration for GreenTech Solutions ## Overview This configuration is designed for GreenTech Solutions, a company in the renewable energy sector. The setup focuses on processing sensor data from wind turbines and integrating it into the Cursor IDE for real-time monitoring and analysis. ## Workflows - **Data Ingestion**: - Source: Wind turbine sensors - Frequency: Every 5 minutes - Data Format: JSON - **Data Processing**: - Transformation: Convert JSON to CSV - Validation: Check for missing or corrupt data - Storage: Store processed data in a centralized database ## Error Handling - **Data Validation Errors**: - Log errors in a dedicated error log file - Notify the engineering team via email - **System Failures**: - Implement automatic retry mechanism - Escalate to the on-call engineer if the issue persists ## IDE Integration - **Cursor IDE**: - Real-time data visualization - Alerts for abnormal data patterns - Integration with existing development workflows
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