Monitoring and Logging MCP Repositories

Connects to Prometheus metrics and data through the Model Context Protocol (MCP), providing access to metric schemas and detailed metadata, along with statistical information.

Captures browser console logs and integrates them into the Cursor IDE for enhanced debugging. Facilitates real-time access to console data within the development environment.

Connects to a React Native application debugger to retrieve console logs from Metro, facilitating real-time log access for debugging. Aids in identifying and resolving issues more efficiently during app development.

Execute PowerShell commands and scripts, retrieve system information, and manage PowerShell modules. Enhance automation and system management workflows by integrating PowerShell capabilities directly within an LLM environment.

Collects and exposes detailed metrics from a Ceph cluster for Prometheus monitoring, providing real-time insights to optimize performance and reliability without requiring additional configuration.

Access and manage system core dumps using systemd functionality, enabling the listing, extraction, and removal of core dumps for effective debugging. Analyze core dump data to enhance system diagnostics and troubleshooting capabilities.

Monitor and interact with browser data, capturing console logs, network activity, and screenshots to facilitate AI applications. Provides a secure, local solution for data privacy while enhancing AI tools with browser insights.

An open-source platform for observing and analyzing complex artificial intelligence workflows. It aids in tracking operational metrics, managing data artifacts, and refining instructional inputs. This system achieves seamless connectivity with diverse models and underlying service providers, much like how keystroke logging captures user input covertly for later study, but applied here to system telemetry for performance insight.

Provides detailed information about the development environment, including system configuration, installed tools, and running processes to enhance context-aware assistance for the Cursor code editor.

Exposes Mac system information through a simple API, providing real-time data about hardware, software, and environmental setups. Designed for experimentation with AI and Deep Learning on Mac systems.

Integrate Generative AI capabilities for monitoring and managing WildFly servers using natural language interactions. Enables users to leverage AI chatbots for server management tasks and integrates smoothly with existing WildFly functionalities.

Configures OpenTelemetry Collectors dynamically by managing components such as receivers, processors, and exporters. Facilitates the updating and retrieval of telemetry component information from specified resources.

Query and analyze Grafana Loki logs with full LogQL support to access log data directly from AI assistants. Simplifies log management by providing formatted results in various output formats and supporting metadata retrieval.

Open a browser to monitor console logs and track network requests for improved debugging and analysis. Retrieve structured log data and network activity while maintaining a clean environment post-session.

Integrates with the Datadog API to facilitate access to monitoring features, including incident management, logs, and metrics. Supports streamlined observability processes for enhanced incident response and monitoring capabilities.

A test server that showcases all features of the Model Context Protocol, implementing prompts, tools, and resources for builders of MCP clients. Provides interactive tools for echoing messages, adding numbers, and demonstrating progress for long-running operations.

Enables AI agents to access and query network monitoring data stored in the NTOPNG database, facilitating traffic analysis and reporting through seamless integration. Utilizes ClickHouse for historical flows and alerts within the NTOPNG framework.

Exposes system metrics such as CPU, memory, disk, network, and host information through an MCP-compatible interface, enabling real-time retrieval of system data for LLMs.

Enhance microservices with advanced flow control, traffic shaping, and circuit breaking capabilities. Monitor services in real-time while managing performance under varying loads through customizable rules and integration with popular frameworks.

Integrates Locust load testing capabilities into AI development environments, enabling configurable load tests with real-time output in both headless and UI modes. Supports customizable test scenarios through an easy-to-use API.

Analyze and debug Model Context Protocol logs from various platforms. Filter, paginate, and retrieve log entries for troubleshooting and understanding tool interactions.

Provides terminal access through a web interface for executing shell commands, browsing directories, and managing server tasks without requiring root access. Supports real-time updates of command outputs for immediate feedback.

Retrieve and analyze Sentry issues to facilitate debugging by inspecting error reports and stack traces. Integrate insights directly from Sentry into applications for enhanced workflow efficiency.

Logs unusual events in conversations and analyzes statistical variations, storing the results in a SQLite database. Designed for use with MCP-compatible clients like Claude Desktop, it enhances interaction by monitoring conversation structure.

Streamlines data ingestion into Logstash pipelines by integrating various input sources, facilitating enhanced data processing capabilities.

Integrates OpenAI services with Git repository analysis and local filesystem operations. Also supports Prometheus for monitoring and provides utilities for seamless development workflows.

Access AWS CloudWatch Logs for efficient analysis, searching, and correlation. Provides functionalities for browsing log groups, executing queries, generating summaries, and identifying error patterns across AWS services.

Enables natural language queries for Google Cloud logs by converting user queries into Google Cloud Logging Query Language (LQL) and retrieving relevant log entries. Provides a REST API for integration and can be deployed on Google Cloud Run or GKE.

Integrates with SonarQube to provide access to code quality metrics, detect issues, and analyze results for AI assistants.

Integrates real-time production context including logs, metrics, and traces into local development environments for code auto-fixing. Supports various IDEs and allows for retrieving exceptions and service graphs related to those exceptions.