"How to fully utilize Docket application or platform" matching MCP servers:

A read-only MCP server that enables AI agents to act as GCP platform engineers, allowing them to investigate incidents, take inventory, and find cost-optimization opportunities in Google Cloud projects without mutating any infrastructure.

Read-only MCP server that exposes Kubernetes platform state (tenants, pods, SLOs, ArgoCD applications, chaos schedules, and catalog services) to AI agents, enabling natural language queries about cluster health and configuration.

Lets your coding agent talk to the RunWhen platform — workspace chat, issues, SLXs, run sessions, and the Tool Builder — over the Model Context Protocol. Enables workspace chat with AI assistant, task authoring via Tool Builder, and direct data access to workspaces, issues, SLXs, run sessions, and more.

Enables MCP hosts like ChatGPT and Claude to query data platform metadata, explore schemas and tables, inspect SQL explain plans, and monitor DAG and ETL status through standardized tools.

A framework for building MCP servers with built-in enterprise capabilities such as authentication, authorization, telemetry, audit logging, and data classification, allowing domain developers to focus on business logic while maintaining governance and compliance.

An MCP server that enables conversational interaction with Wazuh SIEM, allowing users to investigate alerts, hunt threats, tune false positives, edit rules, and run security actions via natural language.

Enables ingestion and full-text search of Windows application logs via MCP tools. Supports dual-backend indexing with Elasticsearch and SQLite for fast retrieval.

Enables AI assistants to monitor and troubleshoot AWS Application Signals services by tracking service health, analyzing SLO compliance, querying CloudWatch metrics, and investigating issues using distributed tracing with AWS X-Ray.

Enables AI assistants to manage Sciple platform content including environments, services, dashboards, runbooks, projects, and tickets through the Sciple REST API.

Fetches Langfuse observability traces directly into a VS Code coding agent's context, enabling querying and viewing trace data through natural language.

Enables AI clients to connect to live IoT deployments, read devices, provision hardware, deploy automation rules, manage alarms, and send commands to physical equipment.

BeamScope gives AI coding agents access to your running BEAM application through a resilient TCP architecture that survives app restarts.

An MCP server that connects AI agents to Scouter APM, enabling natural-language queries against real-time application performance data.

Provides read-only observability and operational insight into a MuleSoft Anypoint Platform estate, enabling natural-language queries for application logs, archived log search, Runtime Manager inventory, API Manager instances, and Exchange assets using your own credentials.

Enables LLMs to query telemetry data via the Spyglass AI agent, providing intelligent insights about application performance, errors, and bottlenecks.

An MCP server for managing and monitoring Docker, Docker Compose, and Kubernetes environments alongside Azure Application Insights. It enables advanced log filtering, container lifecycle management, and querying of cloud application traces and metrics.

A read-only MCP server that provides diagnostic tools for MySQL performance troubleshooting, including processlist, lock waits, InnoDB status, and replication lag, without exposing generic SQL or application data.

A Claude Code MCP server that connects to Laravel Clockwork to provide insights into application performance, database queries, errors, and request flow.

Connects AI coding assistants to Shipbook for investigating issues using real application logs with error classification, log search, and session tracing.

Enables grading a website's security headers with per-gap fixes and a README badge, and analyzing logs, code, configs or suspicious messages to determine what happened, how severe it is, and what to fix first. Requests are masked for sensitive data and automatically retried across multiple AI providers when one refuses, subject to a defensive-use-only policy.