8 boosters for "observability" — AI-graded, open source, ready to install
This MCP server integrates Dynatrace monitoring and observability capabilities into Claude and Copilot, enabling developers to query application performance data, logs, and metrics directly within AI assistants. It benefits DevOps engineers, SREs, and developers who need real-time access to Dynatrace insights during troubleshooting and development workflows.
A production-grade technical writer agent that automates API documentation, user guides, tutorials, and code examples with enterprise-level reliability. Ideal for development teams needing consistent, high-quality technical documentation at scale.
Logfire is a debugging skill that queries Pydantic Logfire logs, traces, and performance data to help developers diagnose errors, identify slow requests, and analyze backend issues. It's essential for any team using Logfire for observability.
Structum is a system prompt for migrating Python framework documentation to enterprise-grade standards using custom grid systems and design tokens. It benefits documentation maintainers and technical writers working with Sphinx-based projects who need structured, standards-compliant documentation processes.
A production-grade system prompt for building a security-hardened RAG (Retrieval-Augmented Generation) document Q&A platform with JWT auth, multi-tenant isolation, and Gemini integration. Ideal for teams building enterprise-ready AI assistants that prioritize security and observability.
Alpic is an MCP server that enables developers to manage hosted MCP projects, debug deployments, and monitor analytics from within Claude and Cursor. It's ideal for teams running multiple MCP servers who need centralized management and observability.
An SRE agent that evaluates system stability, observability, and operational risks by assessing monitoring, logging, and alerting configurations. Essential for teams building reliable production systems who need automated reliability reviews.
The Dynatrace Expert Agent automates observability and security analysis within GitHub workflows, helping development teams investigate incidents, detect performance regressions, and manage vulnerabilities without leaving their repository. It benefits DevOps engineers, security teams, and developers working with Dynatrace-monitored applications.
