"Task" matching MCP servers:

Enables AI assistants to search, analyze, and debug application API traffic captured by Tusk Drift, including HTTP requests, database queries, distributed traces, latency metrics, and error rates.

Connects MCP-capable AI clients to a monitoring API to inspect cron and recurring-task checks, with opt-in mutations for pings, integrations, and check changes.

MCP server for XXL-JOB Admin that enables AI assistants to manage scheduled jobs through natural language, including task lifecycle management, manual triggering, cron preview, and execution log retrieval.

Tracks AI agent token usage and spending in real time, with budget alerts, per-task cost breakdown, and a visual dashboard.

Enables inspection and management of Apache Airflow DAGs, runs, and logs across multiple instances. It provides tools for monitoring workflows and performing gated write operations like triggering DAGs or clearing task instances.

A read-only MCP server for AppDynamics that exposes 12 task-oriented tools to query applications, topology, metrics, snapshots, health rules, anomalies, events, alerting config, analytics events, and dependency maps via OAuth API-client credentials.

Enables real-time tracking of AI task starts and completions with integrated webhook notifications for monitoring activity. It allows users to log progress and send task-related event data to external services via configurable webhook URLs.

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.

MCP server for controlling a Hermes agent deployment via its HTTP API, enabling health checks, log retrieval, task management, and deployment configuration from MCP clients.

Monitor and manage Apache Airflow clusters through natural language queries via MCP tools: DAG inspection, task monitoring, health checks, and cluster analytics without API complexity. * Guide: https://call518.medium.com/mcp-airflow-api-a-model-context-protocol-mcp-server-for-apache-airflow-5dfdfb2

A local, read-only MCP server for RavenDB diagnostics, providing 21 tools to inspect cluster, database, index, task, storage, and performance state, plus logs and data.

An MCP server that enables AI assistants to manage, monitor, and diagnose Windows systems through 42 tools across 8 modules, including services, event viewer, task scheduler, processes, network, diagnostics, observability, and safety features.

A task-scoped MCP stdio proxy that learns candidate least-privilege policies from labeled successful runs, requires human review, enforces exact decisions, detects tool-definition drift, and emits privacy-minimized JSONL events for Wazuh.

A read-only MCP server that turns Codex task logs into privacy-aware event ledgers and interactive timelines, providing tools to list sessions and retrieve trajectory data with summary or full detail levels.

An observable autonomy harness for AI coding agents that enables long task execution without drift or cheating, while recording all actions for cost and quality steering.

Enables AI agents to query Zabbix for current problems, host investigations, alert triage, metrics, and maintenance operations through task-shaped tools with bounded JSON output and human-approval safeguards for any changes.

Password-protected dashboard and Remote MCP observer for Codex work, recording concise task milestones via observe_start, observe_report, and observe_finish tools.

Enables deterministic task routing, contract enforcement, and effect authorization across domain owners while exposing a minimal MCP interface and read-only observability.

MCP server that lets AI coding agents report task progress and milestones, providing a real-time web dashboard and macOS notifications for monitoring multiple AI workbenches.

Provides observability for multi-agent workflows by tracking hierarchical task structure, architectural decisions, reasoning, encountered problems, code modifications with Git diffs, and temporal metrics.