Tracks protocol adherence and maintains audit trails for Claude sessions, providing tools for logging steps, activities, and violations, and computing compliance scores.
Enables AI clients to create and configure monitors, subscribe to their evidence streams with durable cursors, and process observations with guaranteed at-least-once delivery.
Provides real-time system metrics and information through a Model Context Protocol interface, enabling access to CPU usage, memory statistics, disk information, network status, and running processes.
Enables AI assistants to see which model IDs each provider's API has started listing since the last check, and to list the models a given provider currently serves with optional substring filtering, all without spending tokens.
An MCP server that records agent execution metrics and exposes a Context Window Explorer to visualize exactly what entered the model's context window across sessions, tokens, and tool calls.
Enables assistants to read-only explore and analyze Grafana dashboards and data sources, execute queries against Prometheus and Azure Log Analytics, and run guided investigation workflows.
Provides comprehensive system diagnostics and hardware analysis through 10 specialized tools for troubleshooting and environment monitoring. Offers targeted information gathering for CPU, memory, network, storage, processes, and security analysis across Windows, macOS, and Linux platforms.
Read-only MCP server that exposes governed operational records with provenance and validity windows so clients can check which evidence is still current before answering. It lets agents reconcile freshness, schedule state, and evidence specificity to determine whether scheduled automations are actually healthy, rather than trusting an expired dashboard summary.
Enables AI agents to access real-time Windows PC context including active window, system performance, screen time, productivity analytics, and historical usage through MCP tools.
Windows tray process that exposes local ambient context (presence, foreground app, battery, etc.) as MCP tools with privacy classification and opt-in controls.
Enables auditing MCP servers' tools by their estimated context token cost, ranking verbose names, descriptions, and schemas before they consume the agent window. Can run as an MCP tool so agents can budget their own context.
Provides a read-only interface to Kubernetes clusters, enabling LLMs to list pods, get pod status and logs, fetch deployment manifests, and perform pod health analysis with resource trend tracking.
Provides unified project context and monitoring capabilities including project health metrics, build diagnostics, Git integration, and infrastructure validation to initialize development sessions with comprehensive project information.
Provides tools to monitor host system health including CPU load, disk usage, and network status while enabling file system management tasks like searching and moving files. It includes built-in safety guards to prevent operations on critical system directories.
Reads local Codex rollout logs to expose context-window usage snapshots, remaining-token estimates, and log-event freshness through a read-only MCP tool.
Enables integration with Datadog APIs to monitor and retrieve information about monitors, metrics, dashboards, logs, events, and incidents through the Model Context Protocol.
Enables natural-language querying and analysis of AWS spending and model-invocation usage, including detailed breakdowns by service, region, account, day, and instance type.
Enables users to query and analyze cloud spending, EC2 cost breakdowns, service-level spend, and model usage logs through natural-language interactions. It also supports cross-account spend retrieval when configured.
Enables users to analyze AWS spend and Bedrock model usage through natural-language queries, including EC2 costs, service spend reports, and breakdowns by region, model, and user.
A Model Context Protocol server that provides LLM Agents with a comprehensive toolset for IP geolocation, network diagnostics, system monitoring, cryptographic operations, and QR code generation.
A robust Model Control Protocol server that enables AI agents to access real-time cyber threat intelligence and detailed information about vulnerabilities, threat actors, malware, and other cyber-security entities.
Native Model Context Protocol (MCP) server turning AI coding agents into .NET runtime diagnosticians, memory forensic analysts, and CIL binary architects.
A Model Context Protocol server that monitors shadow-cljs builds and provides real-time build status updates for ClojureScript projects, allowing LLMs to verify build status after making code changes.