"Using an MCP server with Atlassian JIRA to extract ticket descriptions" matching MCP servers:

Enables running and learning from a production-shaped MCP server that exposes ticket-desk operations, server administration, timers, events, and schema-driven queries across stdio, SSE, and Streamable HTTP with configurable auth, rate limiting, and observability.

Enables AI agents to debug and repair historical Solaris/SPARC systems by providing an out-of-band control plane with guest DTrace, QEMU monitor access, SPARC-aware GDB, host eBPF/perf tracing, and an immutable evidence ledger for cross-layer hypothesis testing.

Enables interaction with Jira Service Management Operations alerts and on-call schedules, including searching alerts, reading notes and activity logs, acknowledging, closing, annotating, adding responders, and querying current and next on-call rotations.

A Model Context Protocol server that provides AI assistants access to AWS CloudWatch Logs, enabling browsing, searching, summarizing, and correlating logs across multiple AWS services.

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

Enables an AI assistant to diagnose a broken application on a local Kubernetes cluster by inspecting pods, events, and logs, with server-side guardrails, least-privilege RBAC, and an audit log. It can also restart deployments in allowed namespaces only after a dry-run preview and host approval.

Enables natural language analysis of network packet captures, including protocol detection, flow analysis, and security threat identification, integrated with AI assistants.

A secure MCP proxy server with JWT authentication, SQL guardrails, rate limiting, and Kafka audit logging, enabling enterprise-grade access control for MCP clients like Cursor and Claude Desktop.

Enables coding agents to read performance findings from sixty, get detailed evidence for each finding, and close or resolve them with descriptions, while also checking service telemetry and providing installation instructions.

Enables data observability operations with the Sifflet platform. Supports exploring assets, monitors, incidents, generating monitor-as-code YAML from descriptions, and performing impact analysis.

An MCP server for querying Observe.com logs using OPAL, with tools for dataset discovery, schema inspection, and log searching.

Enables unified management of maintenance windows and incidents across Atlassian Statuspage and Uptime Kuma. It allows AI assistants to schedule maintenance, update service statuses, and list monitors through a single MCP-compatible interface.

Monitors API endpoint health by checking availability, measuring response times, and providing detailed status reports with error handling and timeout protection for the localhost:8080/api/ticket endpoint.

Enables MCP-compatible agents to perform read-only incident investigations by querying logs, building timelines, searching runbooks, validating evidence, and creating ticket drafts only with explicit approval.

Incident Triage MCP is a Model Context Protocol (MCP) server for incident triage. It provides safe, auditable tools for evidence retrieval, deterministic summaries, ticket workflows, and notifications.

An MCP interface that allows AI assistants to query and analyze Grafana Loki logs using LogQL, with support for authentication and various output formats.

A debugging gateway for distributed systems that provides 17 tools for interacting with AWS CloudWatch, Step Functions, LangSmith, and Jira. It enables efficient log analysis, workflow tracing, and ticket management while significantly reducing token usage through a single-interface discovery pattern.

MCP server that answers service status and uptime questions by reading Atlassian Statuspage feeds from hundreds of vendors, with keyless access and tools for checking status, incidents, and vendor listings.

A verifying MCP gateway that pins approved tool manifests and skill files to content addresses, blocking tampered descriptions before they reach model context.

Enables an LLM assistant to analyze automated test results, search logs, group failures by cause, and draft Jira bugs with steps to reproduce, with dry-run Jira filing by default.